Reviewerly/exHarmony huggingface dataset:
JRC TMA-CC: Slavic Named Entity Recognition and Linking. Participation in the BSNLP-2019 shared task

We report on the participation of the JRC Text Mining and Analysis Competence Centre (TMA-CC) in the BSNLP-2019 Shared Task, which focuses on named-entity recognition, lemmatisation and cross-lingual linking.We propose a hybrid system combining a rule-based approach and light ML techniques.We use multilingual lexical resources such as JRC-NAMES and BABELNET together with a named entity guesser to recognise names.In a second step, we combine known names with wild cards to increase recognition recall by also capturing inflection variants.In a third step, we increase precision by filtering these name candidates with automatically learnt inflection patterns derived from name occurrences in large news article collections.Our major requirement is to achieve high precision.We achieved an average of 65% Fmeasure with 93% precision on the four languages.
Making perfectly controlled arrays of molecules at rest

Molecular collisions are probed with ultracold optical traps
H<sub>∞</sub> state estimation for stochastic recurrent neural networks with randomly occurring nonlinearities and variance constraint

This paper is coped with the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> state estimation problem under error variance constraint for discrete stochastic recurrent neural networks (RNNs) with parameter uncertainty and randomly occurring nonlinearities. The activation functions satisfy the sector-bounded condition, and the phenomena of randomly occurring nonlinearities are described by random variables obeying Bernoulli distribution. The major purpose is on the development of a time-varying H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> state estimation scheme such that some sufficient conditions are derived to ensure both the prescribed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance requirement and estimation error variance constraint. Accordingly, the desired estimation algorithm can be realized based on recursive linear matrix inequality approach. Finally, an example is presented to show the effectiveness of the main constrained estimation results.
FACTORS AFFECTING GRADE 11 STUDENTS’ ACHIEVEMENT IN LEARNING POPULATION EDUCATION IN SCHOOL POPULATION AWARENESS, INDONESIA

Background : School population awareness program (SSK) is a school program of teaching and learning strategies of population education developed at school level with the pilot project in Sukabumi, Indonesia. Prior this study, there was no research had been conducted to see the effectiveness of the program from the perspective of the students, on how the students perceived, their learning achievement, and whether the teaching and learning population under the SSK program contribute as the factor affecting learning achievement, which all of this could draw a conclusion whether the SSK program effective for students. Further, it would also give a valuable input for teacher and school sectors on how to improve the students’ learning achievement. This study has a purpose to investigate the attitude of student toward five aspect of learning situation of population education, which are the subject, method of teaching, activity of teaching, media of instruction, and class environment. Students’ learning achievement was also assessed in this study including the factors that affecting the students’ learning achievement in population education Methods : A descriptive cross-sectional study was applied using a quantitative research method. The sample size was 188 of eleventh grade students studying at three pilot projects of School Population Awareness in Sukabumi, Indonesia. Data were collected through a survey using close-ended questionnaire which distributed purposively. Results : Students’ attitudes were at high level, and their learning achievements were also high. Three predictive factors of learning achievement in population education were found: the attitude towards subject, activity of teaching, and method of teaching. Conclusion : From the result it was reveal that all of the predictive factors found were associated with the teaching and learning strategies of population education under the SSK program which draw a conclusion that the program is effective for the students. For BKKBN as the government institution who developed the SSK program it is recommends to advocate the Ministry of Education and Culture of Indonesia and other senior high schools to implement the SSK program based on the model of SSK implemented in Sukabumi. For the respective teachers and school sectors it is suggested to emphasize the students with the importance of learning population education, adding more real-life activities, and provide more group work in the learning process in order to improve the students’ learning achievement in population education.
Natural Adversarial Sentence Generation with Gradient-based Perturbation

This work proposes a novel algorithm to generate natural language adversarial input for text classification models, in order to investigate the robustness of these models. It involves applying gradient-based perturbation on the sentence embeddings that are used as the features for the classifier, and learning a decoder for generation. We employ this method to a sentiment analysis model and verify its effectiveness in inducing incorrect predictions by the model. We also conduct quantitative and qualitative analysis on these examples and demonstrate that our approach can generate more natural adversaries. In addition, it can be used to successfully perform black-box attacks, which involves attacking other existing models whose parameters are not known. On a public sentiment analysis API, the proposed method introduces a 20% relative decrease in average accuracy and 74% relative increase in absolute error.
Sistem Pakar Diagnosis Hama Dan Penyakit Tanaman Kopi Menggunkan Metode Breadth First Search (Bfs) Berbasis Web

Several factors cause low quality and productivity of coffee in Indonesia.Pests and diseases are considered as two of the main reason for these factors.Most coffee farmers in Indonesia, particularly in Province of Lampung are undereducated farmers.They still use traditional practices to identify pests and diseases.Using an expert system, we hope that farmers can accurately diagnose coffee pests and diseases.The main goal of this research is to develop a web-based expert system to diagnose coffee pests and diseases.To identify the pests and diseases Breadth First Search (BFS) technique was implemented.The data consist of 5 pests, 5 diseases, 28 symptoms of pest, and diseases the coffee plant.Certainty factor is used to weight the symptoms and it was determined by experts and the user.Then the certainty factor is iteratively computed the system to measure the likelihood of the diagnosis.To evaluate this system, 30 users were chosen and grouped into three clusters (the coffee experts, farmers, and agriculture students, computer science students).They were asked to assess this expert system by fulfilling questionnaires.They inferred that this system runs well with percentage respectively 75.56%, 73.78%, and 83.39%.
Using Software Agents to Raise Awareness and Lower Information Overload in a Multi-user Collaborative Environment

Ubiquitous devices provide users with notifications that continue to blur the distinction between work and personal activities and can lead to information overload. This research aims to support users of multiple collaborative and social systems who may experience this, by designing and evaluating a personal software agent to support the user and ameliorate the effects of overload. A technique uniting personas and Empathy Map has been applied to model typical user behaviours, goals and pain points, which will inform the design of a solution to manage interruptions and information overload.
The Unreasonable Effectiveness of Machine Learning in Neuroscience: Understanding High-dimensional Neural Representations with Realistic Synthetic Stimuli

Author(s): Thielk, Marvin | Advisor(s): Gentner, Timothy; Sharpee, Tatyana | Abstract: Parametrizing complex natural stimuli is a difficult and long-standing challenge. We used a generative deep convergent network to represent and parametrize a large corpus of song from European starlings, a songbird species, into a compressed low-dimensional space. We applied psychophysical methods to probe categorical perception of natural starling song syllables, which reveal a shared categorical perceptual space. Some categorical boundaries are sensitive to the category assignment of training syllables, indicating that the consensus is context dependent and that underlying dimensions of the space are not independent. We record simultaneous firing from populations of 10's of neurons in a secondary auditory cortical region of anesthetized starlings. By estimating how fast population level neural representation change with respect to the stimuli, we produce a measure along a path in stimuli space that is shared between birds and descriptive of the psychophysically determined parameters in other birds. Consistent with this, we predict the behavioral psychometric function along one dimension by fitting the behavior for other dimensions to the population level neural activity. Thus, knowing how the animal responds in one sub-region of the parametrized space informs responses in other sub-regions. Our results implicate the importance of experience in shaping shared perceptual boundaries among complex communication signals and suggest the categorical representation of natural signals in secondary sensory cortices is distributed much more densely than predicted by traditional hierarchical object recognition models. This thesis also explores other applications of machine learning to solve neuroscience problems, in particular, the curse of dimensionality and exploring predictive coding and surprise. A model explicitly designed to predict future states allows the compression of high-dimensional time-varying signals into a lower-dimensional representation encoding exclusively predictive and predictable information and has many practical applications.
Classical field theory

Classical Field Theory is a graduate level introduction to modern classical field theory, including electromagnetism and general relativity and describes various classical methods for fields with n...
Smart Stick for Hurdle Detection and Location Tracking for Blinds

Vision is a .beautifulgift to the human being by the god.The proportion of visually impaired and blind..people in..the..world..has..been..increased..very.largely...In this .paper,.weare introducing a.. smart..stick..system for assisting the blind people.The..smart.stick..comes..as..a.solution to enable.. visually..impaired..people..to find difficulties in.. detecting..obstacles and..dangers in front of the blind people..during..walking and..to..identify..the world around...The system consists of various..sensors along..with..the Arduino..Nano..microcontroller..and..the..GPS-GSM.Microcontroller receives the sensor signals.and process
A Ph.D in PHD2 Guiding

This chapter goes through PHD2 Guiding, showing off some of its other useful tools, and helping the user optimize the system. The end of the last chapter was an introduction to PHD2 Guiding. You earned your diploma in guiding if you worked through that. Now it is time for your Ph.D.
Explainable Product Search with a Dynamic Relation Embedding Model

Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. They, however, ignore the problem that there is a gap between how systems and customers perceive the relevance of items. Without explanations, users may not understand why product search engines retrieve certain items for them, which consequentially leads to imperfect user experience and suboptimal system performance in practice. In this work, we tackle this problem by constructing explainable retrieval models for product search. Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session. Ranking is conducted based on the relationship between users and items in the latent space, and explanations are generated with logic inferences and entity soft matching on the knowledge graph. Empirical experiments show that our model, which we refer to as the Dynamic Relation Embedding Model (DREM), significantly outperforms the state-of-the-art baselines and has the ability to produce reasonable explanations for search results.
A Review Of Credit Card Fraud Detection Techniques In Electronic Finance And Banking

The growing innovation in technology has been exploited in the finance/banking sector in the form of electronic commerce (e-commerce). Financial transaction is the backbone of global market. Rather than make transaction conventionally, cashless transaction is being done in recent times using credit cards. The credit card transaction uses electronic technology to make commerce easy, and its use for such purposes has increased. However, credit card being the most common means of payment in the society today, has witnessed increased number of fraud cases. In order to reduce or solve the problems of fraud cases related to credit card transactions, several methods have been proposed and implemented in literature and real-world. This paper discussed some of the recent fraud detection techniques that have been developed to identify and detect fraudulent cases of credit card transactions. Since fraud detection consist of detecting fraud as soon as possible once it is done, the various models developed are built to provide fast response, accuracy, high degree of sensitivity, effectiveness and efficiency. These techniques have their individual benefits and limitations. However, it is recommended that a hybrid model with robust and optimal performance that combines various be used for fraud detection in finance/banking. The paper has also presented observed challenges of credit card techniques.
Presença da disciplina História do Livro nos currículos dos cursos de Biblioteconomia do Brasil nos anos de 1978 e 2018

The curriculum of the courses of Librarianship in Brazil is composed as much by technical disciplines, relative to the professional work of the librarian, as disciplines of humanistic and cultural character. In these is the History of the book (and of the libraries), one of the oldest in the curriculum of the course and of great importance for the formation of the librarian. Based on this assumption, this research carried out a survey of the presence of the discipline in the curriculums of the courses in two moments (1978 and 2018), as well as a review of the literature on the history of the book and the development of the teaching of the discipline in the librarianship curriculums. The data collected showed that in the two periods almost all the courses had the discipline in their curriculums, although with different nomenclatures.
Understanding and Improving Virtual Adversarial Training

In semi-supervised learning, virtual adversarial training (VAT) approach is one of the most attractive method due to its intuitional simplicity and powerful performances. VAT finds a classifier which is robust to data perturbation toward the adversarial direction. In this study, we provide a fundamental explanation why VAT works well in semi-supervised learning case and propose new techniques which are simple but powerful to improve the VAT method. Especially we employ the idea of Bad GAN approach, which utilizes bad samples distributed on complement of the support of the input data, without any additional deep generative architectures. We generate bad samples of high-quality by use of the adversarial training used in VAT and also give theoretical explanations why the adversarial training is good at both generating bad samples. An advantage of our proposed method is to achieve the competitive performances compared with other recent studies with much fewer computations. We demonstrate advantages our method by various experiments with well known benchmark image datasets.
How Do I Set Up My Equipment?

This chapter is a general overview of how to set up and coordinate the equipment and software described in Chapter 2 . In general, the process starts with defining what will be in the system, loading the various pieces of software necessary to run that hardware, physically connecting the various components, bringing them on line, and doing some testing.
Imbalanced Data Classification Using Weighted Voting Ensemble

Imbalanced data classification is still remaining thje important topic and during the past decades, plenty of works are devoted to this field of study. More and more real-life based imbalanced class problems inspired researchers to come up with new solutions with better performance. Various techniques are employed such as data handling approaches, algorithm-level approaches, active learning approaches, and kernel-based methods to enumerate only a few. This work aims at applying a novel dynamic selection methods on imbalanced data classification problems. The experiments carried out on several benchmark datasets confirm its pretty high performance.
Content Based Image Retrieval System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Design Thinking for Gesture-based Human Computer Interactions

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Non-invasive detection of breast cancer using deep learning (Conference Presentation)

Abnormal and excessive cell growth, known as neoplasms, are not always a sign of cancer. These neoplasms may be benign malignant. Only malignant neoplasms are known as cancer. Therefore, determining the nature of a neoplasm is a critical step in the cancer detection process. Commonly used invasive methods for figuring out the type of neoplasm include fine needle aspiration cytology (FNAC) and tru-cut biopsy. Both these processes involve extracting tissue from the affected area via a needle and then observing the cells under a microscope. This observation is known in medical terms as histopathology. Histopathological images provide a direct evidence for the classification of the neoplasm. Histopathological image analysis however requires a skilled pathologist to describe the pathological findings. On the other hand, there are many non-invasive procedures used for cancer detection like elastography and mammography. Elastography maps the elastic properties of soft tissue using ultrasound or magnetic resonance imaging. Cancerous neoplasms will often be stiffer than healthier ones. A mammogram is just an x-ray picture of the breast. These non-invasive approaches unfortunately provide only an indirect evidence for classification making the classification job quite difficult. Creating a correlation between the non-invasive results and the histopathological images using deep learning can make it much easier to diagnose the nature of the tumor using only non-invasive procedures. Furthermore, training a deep network to recognize malignant tumors from histopathological images can make the entire cancer detection process automated. This paper proposes a deep learning based approach to determining the malignancy of a neoplasm using only non-invasive imaging. The results show good prediction accuracies.
An Analysis Study Of Students’ Difficulties On Reading Comprehension At Seventh Grade Of SMP N 1 Baturetno 2018/2019 Academic Year

Bondan Surya Maulana/ A320140249. An Analysis Study of Students’Difficulties on Reading Comprehension at Seventh Grade of SMP N 1 Baturetno2018/2019 Academic Year. Research Paper. School of Teacher Training andEducation. Muhammadiyah University of Surakarta. July, 2019.This research is aimed at investigating the difficulties in learning readingcomprehension and the strategies used by the students to overcome the difficulties inreading comprehension of seventh grade students of SMP Negeri 1 Baturetno. Thetype of this research is descriptive qualitative. The subjects of this research is 6students in two classes of the seventh grade in SMP Negeri 1 Baturetno will bechosen as participants. The methods of collecting data are observation, interview anddocument review. The result of the study shows that 67 % of the students’ faceddifficulties because their unfamiliar words, complex syntax and lack of backgroundknowledge. The strategies used by students to overcome the difficulties of readingcomprehension are making a connection and inferring.
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

Under special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers. To this end, the prototype document-summary pairs can be utilized to generate better summaries. There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries. To tackle these challenges, we design a model named Prototype Editing based Summary Generator (PESG). PESG first learns summary patterns and prototype facts by analyzing the correlation between a prototype document and its summary. Prototype facts are then utilized to help extract facts from the input document. Next, an editing generator generates new summary based on the summary pattern or extracted facts. Finally, to address the second challenge, a fact checker is used to estimate mutual information between the input document and generated summary, providing an additional signal for the generator. Extensive experiments conducted on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.