Reviewerly/exHarmony huggingface dataset:
A Novel Approach in Automated Bengali Text Summarizing by Statistical and Sentence Similarity Method

World is now moving in faster speed with the blessings of technology. Information is vastly stored in the cloud instead of hard copy documents or compact disk. Hence, to keep information in short and concise way in the cloud, summarization of information could be a greater choice. Doing manual summarization is obviously tedious task and hence data scientists are thinking of an automated process that provides human quality summary. In this paper, we work with two algorithms, namely, statistical and sentence similarity approach. The first approach returns the summary based on frequency of word appearances processing the probability theory while the second figures out the similarity of sentences based on python NLTK corpora and WordNet modules. While testing with several inputs, we observe that the sentence similarity approach gives much better result than statistical approach although it needs a slightly much time. Therefore, sentence similarity could be considered as the best approach of automatic text summarization than statistical approach. Besides, in our paper, we choose python as a programming language considering its various advantages like having open source NLTK library, Brown Corpus and WordNet database, integration properties etc.
Activity Detection from Encrypted Remote Desktop Protocol Traffic

An increasing amount of Internet traffic has its content encrypted. We address the question of whether it is possible to predict the activities taking place over an encrypted channel, in particular Microsoft's Remote Desktop Protocol. We show that the presence of five typical activities can be detected with precision greater than 97\% and recall greater than 94\% in 30-second traces. We also show that the design of the protocol exposes fine-grained actions such as keystrokes and mouse movements which may be leveraged to reveal properties such as lengths of passwords.
Study of Introductory Education Materials for Power Engineering at National College of Technology

Elctrical students at National College of Technology need to acquire the subject of Power engineering for the MCC. For the experiencing like an experiment, we developed an introductory education material based on a gaming simulation. The learning objectives of this material are to understand the 3E (Economic, Environmental, and Stable Energy Supply) and the characteristics of power generation systems that differ in each country. In this paper, the practiced result in the fifth grade lecture was shown. Also, the possibility of learning about global issues related to environmental and energy was shown.
Real-time and Autonomous Detection of Helipad for Landing Quad-Rotors by Visual Servoing

In this paper, we first present a method to autonomously detect helipads in real time. Our method does not rely on any machine-learning methods and as such is applicable in real-time on the computational capabilities of an average quad-rotor. After initial detection, we use image tracking methods to reduce the computational resource requirement further. Once the tracking starts our modified IBVS(Image-Based Visual Servoing) method starts publishing velocity to guide the quad-rotor onto the helipad. The modified IBVS scheme is designed for the four degrees-of-freedom of a quad-rotor and can land the quad-rotor in a specific orientation.
Chapter 5. Development of a Context-based Learning Model Where Teachers Link Regional Companies and Science Classes Utilizing Relevance to Students

The purpose of this chapter is to develop a context-based learning model in which teachers develop teaching materials involving regional companies. As an example, the development of ‘making a kidney using a hollow fiber-type dialyzer and its functions (Cooperative Enterprise: Nikkiso)’ was evaluated. The author developed teaching materials for lower secondary school students, raising their awareness of the usefulness and value of learning science, enhancing their interest and promoting the advancement of science. The results indicate that in this class model teachers can connect regional companies and science lessons, transforming students’ attitudes to science learning.
METHODOLOGY: A WAY TO DIFFERENTIATE SIGNIFICANT FROM INTUITIVE RESPONSES

1 Aveiro Institute of Accounting and Administration, University of Aveiro (ISCA-UA) and GOVCOPP-UA (PORTUGAL)2 Institute of Higher Studies of Fafe (PORTUGAL)3 Polytechnic Institute of Tomar (PORTUGAL)4 Polytechnic Institute of Tomar & CI2-IPT (PORTUGAL)
A Study on the Form and Usages of Participles in Educational Meanings

The purpose of this paper was to examine how students understand and use the participles. They should be able to understand and use participles and participial adjectives, not just to understand the participle construction. It is an effective way to approach and teach in order to understand and utilize the participle. Students have the best understanding of the participial adjectives because they have a clear form, and the present and past participles also show a high degree of understanding in terms of understanding and using verbs. In addition, they were able to understand because they studied the concept of a participle construction while learning an adverb in middle and high school. However, in terms of usage, it is too complicated and has an ambiguous expressions, making it difficult to select a conjunction. Lastly, they haven t heard the term itself for the clause, and it also appears to take some time to understand. From grammarization to degrammarization, it is time to deal with English grammar for living communication. To this end, a more systematic study of all grammatical terms or usages will be required, and the study on this will be postponed to the next opportunity.
Caesar Cipher with Goldbach Code Compression for Efficient Cryptography

This paper proposes an enhancement on the traditional Caesar cipher by introducing the concept of compression using the Goldbach code algorithm to conceal the ciphertext generated by the Caesar cipher for added security.The Caesar cipher is one of the early cryptographic algorithms that is widely used for information hiding and data security.However, the traditional Caesar cipher is vulnerable to attacks and is extremely easy to crack.For the Caesar cipher, each character of a message is always replaced by the same fixed character that has been predetermined.To improve the cipher's security feature, the result of the Caesar cipher is encrypted and is compressed using the Goldbach code algorithm.Simulation results revealed that the proposed method produces a less predictable and shortened ciphertext length than the existing process, ensuring a secure and cost-effective encryption process.
A Self-Assessing Compilation Based Search Approach for Analytical Research and Data Retrieval

While meta-analytic research is performed, it becomes time-consuming to filter through the sheer amount of sources made available by individual databases and search engines and therefore degrades the specificity of source analysis. This study sought to predict the feasibility of a research-oriented searching algorithm across all topics and a search technique to combat flaws in dealing with large datasets by automating three key components of meta-analysis: a query-based search associated with the intended research topic, selecting given sources and determining their relevance to the original query, and extracting applicable information including excerpts and citations. The algorithm was evaluated using 5 key historical topics, and results were broken down into 4 categories: the total number of relevant sources retrieved, the efficiency given a particular search, the total time it takes to finish a complete cycle, and the quality of the extracted sources when compared to results from current searching methods. Although results differed through several searches, on average, the program collected a total of 126 sources per search with an average efficiency of 19.55 sources per second which, when compared and qualitatively evaluated for definitive results, indicates that an algorithm developed across all subject areas will make progress in future research methods.
A Review of English Novel Studies in Korea

This study reviews English novel studies in Korea for the past 60 years, to examine the ways in which English novel studies in Korea have developed and further contribute to the future of English scholarship in Korea. Since reflecting on all the English novel studies in Korea is immense, this study focuses on articles published in Studies in British and American Language and Literature from 1960 to 2019. Organizing the articles by decades, this review attempts to indicate the notable features of each decade and shows who was researched most actively. While Henry James was researched the most by far, William Faulkner, Joseph Conrad, and Virginia Woolf were the most favorite novelists throughout the last 6 decades. Female novelists, such as Jane Austen, Charlotte Bront&#235;, and Virginia Woolf, were first introduced in the 1970s, which shed some light on the sharp increase in the study of female novelists for the coming years: Jane Austen topped the list in the 2000s and Toni Morrison in the 2010s. 2000s saw the most remarkable expansion in research: the number of the articles doubled and novelists from countries other than the British and American were first introduced. Korean scholarship on English novel studies has expanded not just in its number of articles, but also in its diversity.
Artificial Intelligence & Damages: Assessing Liability and Calculating the Damages

Establishing liability for damages caused by AI used to be rather straightforward when only one or few stakeholders are involved, or when the AI could only take a limited range of pre-defined decisions in accordance with specific parameters defined by a human programmer. However, AI usually involves several stakeholders and components (e.g. sensors and hardware, softwares and applications, data itself and data services, connectivity features) and recent forms of AI are increasingly able to learn without human supervision which makes it difficult to allocate liability between all stakeholders. This contributions maps various possibilities, identify their challenges and explore lines of thought to develop new solutions or close the gaps, the whole from a global perspective. Existing liability regimes already offer basic protection of victims, to the extent that specific characteristics of emerging technologies are taken into account. Consequently, instead of considering new liability principles (solutions that require certain amendments of the current liability regimes), one should consider simply adapting current fault-based liability regimes with enhanced duties of care and precisions regarding shared liability and solidarity between tortfeasors, which could potentially be done through case-law in most jurisdictions. When it comes to the calculation of damages, given the difficulties in calculating the damage and to take into account the specificities of IPR or privacy rights, economic methods may be considered to calculate the damages in general, such as the Discounted Cash Flow Method (DCF) and the Financial Indicative Running Royalty Model (FIRRM), as well as the Royalty Rate Method and case-law about Fair, Reasonable and Non-Discriminatory license terms (FRAND). This path will lead to a certain “flat-rating“damages (“baremisation“ or “forfaitisation“), at least when IPR and personal data are illegally used by AI-tools and mostly not visible, hence barely quantifiable in terms of damages.
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models

Image captioning models have been able to generate grammatically correct and human understandable sentences. However most of the captions convey limited information as the model used is trained on datasets that do not caption all possible objects existing in everyday life. Due to this lack of prior information most of the captions are biased to only a few objects present in the scene, hence limiting their usage in daily life. In this paper, we attempt to show the biased nature of the currently existing image captioning models and present a new image captioning dataset, Egoshots, consisting of 978 real life images with no captions. We further exploit the state of the art pre-trained image captioning and object recognition networks to annotate our images and show the limitations of existing works. Furthermore, in order to evaluate the quality of the generated captions, we propose a new image captioning metric, object based Semantic Fidelity (SF). Existing image captioning metrics can evaluate a caption only in the presence of their corresponding annotations; however, SF allows evaluating captions generated for images without annotations, making it highly useful for real life generated captions.
Analysis on the Acoustics of an Auditorium

This paper examines the characteristics of a harmonic plane wave in 1D and applies it to a model of an auditorium in the shape of a quarter of an ellipsoid.In the application, this paper will mainly look at transmission loss, reverberation, disruption of the performers, and differences between different frequencies.The differences between different frequencies will be analyzed on both a macroscopic auditorium level as well as on a microscopic level of a single point in the audience.
First-Order Logic for Flow-Limited Authorization

We present the Flow-Limited Authorization First-Order Logic (FLAFOL), a logic for reasoning about authorization decisions in the presence of information-flow policies. We formalize the FLAFOL proof system, characterize its proof-theoretic properties, and develop its security guarantees. In particular, FLAFOL is the first logic to provide a non-interference guarantee while supporting all connectives of first-order logic. Furthermore, this guarantee is the first to combine the notions of non-interference from both authorization logic and information-flow systems. All theorems in this paper are proven in Coq.
Efficient Private Algorithms for Learning Large-Margin Halfspaces

We present new differentially private algorithms for learning a large-margin halfspace. In contrast to previous algorithms, which are based on either differentially private simulations of the statistical query model or on private convex optimization, the sample complexity of our algorithms depends only on the margin of the data, and not on the dimension. We complement our results with a lower bound, showing that the dependence of our upper bounds on the margin is optimal.
ASYMMETRIC EXCHANGE RATE EXPOSURE OF FINANCIAL PERFORMANCE: EMPIRICAL EVIDENCE THROUGH ARDL AND NON-LINEAR ARDL

The importance of banking sector in the foreign exchange market cannot be overemphasized because of their central role in financial intermediation. However, exchange rate changes have greatly affected Nigerian banks competitive position with other international banks. Hence, the need to examine the asymmetric exchange rate exposure of deposit money banks in Nigeria. Quarterly data spanning 2002-2018 were extracted and Autoregressive Distributed Lag (ARDL) and non-linear Autoregressive Distributed Lag are employed to check the symmetrical and asymmetrical effect. The empirical results indicate that deposit money banks are asymmetrical exposed to exchange rate changes. Furthermore, inflation hinders bank performance both at the short run and long run. It was recommended that CBN need to consistently be aware of the depreciation and appreciation of currency for each trading partner.
O cuidado de enfermagem de reabilitação à luz do princípio da esperança: aplicando conhecimentos da neuromarketing

Introduction: Rehabilitation nursing care still appears very incipient in the field of national scientific literature, needing to be more outlined to understand the role and strategies of care that enable the promotion of hope in the family nucleus involved.Thus, it is believed that neuromarketing techniques associated with philosophical reflection can add roots in the nursing care of this specialty.Objective: To reflect on rehabilitation nursing care in the light of the Principle of Hope, applying knowledge of neuromarketing.Method: Reflection study based on Ernst Bloch's Principle of Hope.Results: The study was organized in four parts, addressing the process of hoping in the motivational, communicative, goal-building, emotional and perceptual-sensory dimensions.Discussion: Hope is impregnated in the process of living of the person in rehabilitation, and understanding this inherent bond is to understand rehabilitation nursing care as a promoter of self-confidence, respect, dignity, social esteem and autonomous participation.Conclusion: Rehabilitation nursing care is understood as a springboard of hope for the involved family nucleus and must be carried out in a systematic way, including knowledge of neuromarketing to build a safer and optimistic trajectory.
Attainability of the Holevo-Cramér-Rao bound for two-qubit 3D magnetometry

We study quantum-limited 3D magnetometry using two qubits. Two qubits form the smallest multi-qubit system for 3D magnetometry, the simultaneous estimation of three phases, as it is impossible with a single qubit. We provide an analytical expression for the Holevo-Cramér-Rao bound (HCRB),the fundamental attainable quantum bound of multiparameter estimation, for 3D magnetometry using two-qubit pure states and show its attainability by rank-1 projective measurements. We also examine the attainability of the HCRB in the presence of dephasing noise using numerical methods. While attaining the HCRB may require collective measurements over infinitely many copies, we find that for high noise the HCRB is practically saturated by two copies only. In the low noise regime, up to three copies are unable to attain the HCRB. More generally, we introduce new multiparameter channel bounds to compare quantum-classical and classical-quantum strategies where multiple independent copies of the state are entangled before or after recording the parameters respectively. We find that their relative performance depends on the noise strength, with theclassical-quantum strategy performing better for high noise. We end with shallow quantum circuits that approach the fundamental quantum limit set by the HCRB for two-qubit 3D magnetometry using up to three copies.
A comparative study on evolutionary algorithms for the agent routing problem in multi-point dynamic task

The agent routing problem in multi-point dynamic task (ARP-MPDT) proposed recently is a novel permutation optimisation problem. In ARP-MPDT, a number of task points are located at different places and their states change over time. The agent must go to the task points in turn to execute the tasks, and the execution time of each task depends on the task state. The optimisation objective is to minimise the time for the agent to complete all the tasks. In this paper, five evolutionary algorithms are redesigned and tried to solve this problem, including a permutation genetic algorithm (GA), a variant of the particle swarm optimisation (PSO) and three variants of the estimation of distribution algorithm (EDA). In particular, a dual-model EDA (DM-EDA) employing two probability models was proposed. Finally, comparative tests confirm that the DM-EDA has a stronger adaptability than the other algorithms though GA performs better for the large-scale instances.
Sistem Pakar Diagnosis Penyakit Gastritis Menggunakan Metode Certainty Factor Berbasis Android

&lt;p&gt;With the increasingly of the modern era, more and more diseases that arise due to human lifestyle and bacterial transmission. One of them is gastritis which occurs because of inflammation that occurs in the lining of the stomach which makes frequent pain in the stomach. From these problems, it is necessary to make an expert system application of gastritis disease which aims to provide information to the public to know the early symptoms of gastritis from an early age. The process of diagnose using the certainty factor method The methods in this study are data retrieval, system design, system making and system testing.From this research, it produces an expert system application that can diagnose the type of disease with the first test case 98.848%, the second test case 99.8464% and the third test case with the results of 99.99115264% correct and in accordance with expert knowledge. System testing is done by using black-box testing that shows the function of each unit of application 100% successful.&lt;/p&gt;
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance

Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues. We propose a self-supervised approach to incorporate semantic guidance into a GAN based stain normalization framework and preserve detailed structural information. Our method does not require manual segmentation maps which is a significant advantage over existing methods. We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization network. The proposed scheme outperforms other color normalization methods leading to better classification and segmentation performance.
Un approccio ecologico-culturale per definire il Mobile Learning

Nowadays, Mobile Learning is often consolidated as a possible way to create virtual learning environmentsas well it offers many opportunities to produce and share multimedia contents and promote distance education. Moreover, training projects and initiatives about  mobile technologies are increasing all over the World, thus, this give an evidencethat there is a spread interest in thisareaof study that goes beyond teaching technologies field of educational research. However, mobile learning is still a relatively new phenomenon, and difficult to decipher , due to “daily” updating of digital andtelecommunication technologies that produce new characteristics and advantages for each user. This work will try to provide a clear definition of Mobile Learning and a reasoned guidelineto define it through a cultural-ecological approach to highlight its benefits and limits in everydayeducation both formal and informal.
LibGuides: Research Data Management (RDM): Data Management Plans

Lists resources to help researchers manage their research data, with an emphasis on Canadian tools.
MIND MAPPING: A WAY TO HONE EFL NINTH GRADERS’ WRITING SKILL AT AN ISLAMIC BOARDING SCHOOL

The purposes of this research were to reveal: (a) the teaching process by using mind-mapping technique, (b) the students’ writing ability. This was classroom action research which took place at Islamic Boarding School Al-Munawwaroh Bangko. The participants were ninth grade students that consisted of 23 female students in the class. This research was conducted in three cycles, and each cycle consisted of three meetings. In collecting the data, the researcher employed observation, field note, and tests. For the test, the researcher utilized writing test in the third meeting of every cycle; students were asked to write or compose the paragraph based on the given topic related to their mind map. The result showed that the teaching and learning process by using mind mapping was more interesting, and students were participated more active than before. They are enthusiastic in recalling and brainstorming the words related to the topic given. The data also indicated that there are enhancements of students’ writing after they were taught using mind-mapping technique which could be seen from the improvements of students’ score from the preliminary data to the final data. Consequently, mind mapping technique is highly recommended in teaching and in improving students’ writing.