Reviewerly/exHarmony huggingface dataset:
NOTE-TAKING AS A TECHNIQUE IN TEACHING READING COMPREHENSION

The aim of this study is to explain the effective way in teaching reading comprehension through Note-Taking technique. Reading is an important skill in learning a language besides listening, speaking, and writing. It is an effort to increase our knowledge and achievement. Everyone should have the ability to read, especially in English because the fact shows that most of the scientific books are written in English. This research applied Classroom Action Research method by using four stages, such as; planning, implementation of action, observation, and reflection. This research conducted in two cycles. They were first and second cycle. Each cycle comprises the series of activities which relates each other. The realization of the second cycle was continued and improved based on the evaluation of the first cycle. The results of this research can be explained that note taking technique increase students’ reading comprehension. In this research, the researcher applied note-taking technique to increase students’ reading comprehension ability because by using note-taking technique the students are easier to make note and easy to answer the questions. They can explain about the keywords or the main ideas of the text, and absolutely they can comprehend the text. The result of the students is increasing by looking their score in cycle 1 and cycle 2. Their mean score in cycle 1 only 51 and cycle 2 the score develops to be 76. It means that the student’s reading comprehension was increased.
Parts of Speech in NovaMorf, A New Morphological Annotation of Czech

Abstract A detailed morphological description of word forms in any language is a necessary condition for a successful automatic processing of linguistic data. The paper focuses on a new description of morphological categories, mainly on the subcategorization of parts of speech in Czech within the NovaMorf project. NovaMorf focuses on the description of morphological properties of Czech word forms in a more compact and consistent way and with a higher explicative power than approaches used so far. It also aims at the unification of diverse approaches to morphological annotation of Czech. NovaMorf approach will be reflected in a new morphological dictionary to be exploited for a new automatic morphological analysis (and disambiguation) of corpora of contemporary Czech.
RETRACTED: Big data Markov chain model based dynamic evolution of regional gap—Take China’s highway resources as an example

Characterized by large scale, variety, fast generation, extremely high value but low density, big data can be used to mine effective information, provide users with auxiliary decision-making and re...
Fully Homomorphic Encryption Scheme Based on Decomposition Ring

In this paper, we propose the decomposition ring homomorphic encryption scheme, that is a homomorphic encryption scheme built on the decomposition ring, which is a subring of cyclotomic ring. By using the decomposition ring the structure of plaintext slot becomes ℤpl, instead of GF(pd) in conventional schemes on the cyclotomic ring. For homomorphic multiplication of integers, one can use the full of ℤpl slots using the proposed scheme, although in conventional schemes one can use only one-dimensional subspace GF(p) in each GF(pd) slot. This allows us to realize fast and compact homomorphic encryption for integer plaintexts. In fact, our benchmark results indicate that our decomposition ring homomorphic encryption schemes are several times faster than HElib for integer plaintexts due to its higher parallel computation.
A Named Entity Recognition Approach for Albanian Using Deep Learning

Named EntityTrandafili, Evis Recognition (NER) is an informationMeçe, Elinda Kajo extraction task that deals with the identification and tagging of generic named entities and/or domain-specific named entities. NER is a crucial task in semantic processingDuka, Enea of text data, making it a key component in different Natural Language Processing applications such as Question Answering, Machine Translation, etc. In this paper we propose an approach for Named Entity Recognition based on Deep Learning models using an Albanian corpus. We focused on the generic named entities such as person’s name, geographical location, name of organization/institution and other categories. Given that there is no publicly available Albanian annotated corpus, we have manually created one. Furthermore, we have built a deep neural network using LSTM cells as the hidden layers and a Conditional Random Field as the output, using both word and character tagging. Taking into consideration the complexity of the Albanian language and the little research done in NLP for Albanian, the results achieved are promising. The results obtained from the experiments demonstrate that the NER performance can be further improved by using a larger annotated corpus to train the model.
BBB: Make Benchmarking Blockchains Configurable and Extensible

Interest in Blockchain technology gradually developed after Bitcoin was introduced in 2008, and has grown exponentially as Blockchain can be applied in many scenarios that require coordination among parties that do not typically trust each other. Due to its popularity and wide applications, enormous number of Blockchain systems have been proposed in the past few years. One way to understand the performance is to build a benchmarking tool to evaluate different systems under different scenarios. A few research teams have build their benchmarking tools, such as BLOCKBENCH and HFBench. Unfortunately, there are several limitations of these tools. In this paper, we enumerate these limitations and challenges. We then present our tool – Boston Blockchain Benchmarking (BBB), which is more configurable and extensible than prior benchmarking tools. Moreover, BBB allows us to evaluate the impact of some common attacks against Blockchains. Finally, we present some preliminary results using BBB.
Pigeon Inspired Optimization of Bayesian Network Structure Learning and a Comparative Evaluation

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Probabilistic dependency relationships among the variables can be represented by Bayesian networks. One strategy of a structure learning Bayesian Networks is the score and search technique. In this paper, we present a new method for structure learning of the Bayesian network which is based on Pigeon Inspired Optimization (PIO) Algorithm. The proposed algorithm is a simple one with fast convergence rate. In nature, the navigational ability of pigeons is unbelievable and highly impressive. In accordance with the PIO search algorithm, a set of directed acyclic graphs is defined. Every graph owns a score which shows its fitness. The algorithm is iterated until it gets the best solution or a satisfactory network structure using map and compass, and landmark operator. In this work, the proposed method compared with Simulated Annealing, Bee optimization and Simulated Annealing as a hybrid algorithm, Bee optimization and Greedy search as a hybrid algorithm, and Greedy Search using BDeu score function. We also investigated the confusion matrix performances of the methods. The paper presents the results of extensive evaluations of these algorithms based on common benchmark data sets. The results indicate that the proposed algorithm has better performance than the other algorithms and produces higher scores and accuracy values.
Research on Indoor and Outdoor Positioning System for Special Population

Abstract Special populations, such as empty-nesters, people with a mental health condition, active children, prison inmates, etc., have a considerable number. The WHO noted that there were 47.5 million people with dementia worldwide in 2016. As the number of people with dementia in low- and middle-income countries continue to increase, it is predicted that the total number of people with dementia will reach 75.6 million in 2030 and reach 135.5 million in 2050. The National Health and Family Planning Commission released the “China Family Development Report (2015)”, which is half of the total number of elderly people. The number of severely mentally disabled patients registered in the country reached 4.297 million, and there were 1, 650 national mental health professional institutions and more than 20, 000 psychiatrists. As of 2012, there were 681 prisons in the country, 300, 000 people in prison, and 1.64 million prisoners. More than 80% of the prisons have established video surveillance systems. The Ministry of Public Security’s “Emergency Release Platform for Children’s Missing Information” is about 100 people abducted every year. The number of missing children who are about one-millionth of a year is not found, and the missing rate is about three-tenths of a million. The real-time positioning of the above-mentioned special population has become the key to solving related problems. Relevant nursing homes, community institutions, mental health institutions, and prisons have become the main demand side.
Electronic Evidence in Criminal Procedure: On the Effects of ICT and the Development towards the Network Society on the Life-cycle of Evidence

This paper provides a summary of the doctoral work of Juhana Riekkinen, awarded Doctor of Laws by the Lapin yliopisto (University of Lapland) on 5 June 2019.&#x0D; Index words: Evidence law, civil procedure, Electronic evidence law and legislation, Electronic discovery, Electronic records, Information Technology, Networks
LCA- Lightweight Cryptographic Algorithm for IoT Constraint Resources

Recently, constraint resources devices applications are growing very fast. Such devices are connected together to serve ‎billions of users. A well-known example of these structures is ‎the Internet of Things (IoT). Saving energy is one of the most challenging aspects in IoT environments. IoT devices operate on unattended mode in hostile environments. Therefore, sensitive data communication should be protected using some sort of cryptography. Due to the limited computing power of wireless devices, it is impossible to apply one of the traditional advanced encryption algorithms for the data encryption. A ‎lightweight cryptographic algorithm is one of the most ‎suitable solutions to save information in such environments. Therefore, a novel light weight cryptography algorithm is proposed in this paper, called LCA.  LCA fits IoT ‎systems due to its low energy consumption, simple hardware requirements, and its level of security. A new strong substitution box is suggested considering   the algorithm immunity against the various types of ‎attacks such as linear, differential, and side channel cryptanalysis. LCA has a simple bit slice implementation and it relies its design on a new approach of Feistel structure.
Dialogue Generation Using Self-Attention Generative Adversarial Network

Developing an intelligent conversation system is one of the longest-running goals in Artificial Intelligence research. For a successful conversation system, appropriate dialogue generation is one of the main components. This article describes a method to generate dialogue using Self-Attention Generative Adversarial Network (SAGAN). The general objective of this method is to produce dialogue which is very similar to human-generated dialogue both structurally and contextually. Generative Adversarial Network (GAN) is mainly used to generate dialogue which is structurally very similar to human-generated dialogue. On the other hand, the self-attention network helps to maintain the context of the conversation in extreme detail for both single track and multi-track dialogue generation. SAGAN enhances the accuracy for both single track and multi-track dialogue generation in a significant way.
Image Analysis in Cytopathology

Digital image analysis is applicable to clinical and non-clinical use cases in cytopathology. Various cytology samples, including smears, liquid-based preparations, and cell blocks, are suitable for image analysis. Cytopathology is one of the original fields in medicine where computer-assisted tools were successfully adopted to automate Pap test screening. Since then, there have been numerous publications applying digital image analysis to fine-needle aspiration and exfoliative specimens. Image algorithms have evolved from offering simple quantitative image analysis of biomarkers to sophisticated artificial intelligence tools due to improvements in computers and deep-learning techniques. This chapter reviews the literature and commercial marketplace of digital image analysis applied to cytopathology.
An Adaptive Approach to Controlling Parameters and Population Size of Evolutionary Algorithm

Abstract Evolutionary algorithm (EA) is a part of evolutionary computing inspired by the theory of biological evolution. Evolutionary algorithms have three parameters that must be defined, namely population size, probability of crossing over, and probability of mutation. The absence of standard rules in setting the value of these parameters becomes a difficulty in utilizing evolutionary algorithms to solve optimization problems so that it can cause early convergence in obtaining local optimum values. This research was conducted to find a way to adjust the value of these parameters by using fuzzy logic to determine the probability of crossing and mutation probability and the method of determining population size based on the best fitness value. This method is called Population Resizing on Fitness Improvement Fuzzy Evolutionary Algorithm (PRoFIFEA). The testing of this method uses the problem of Traveling Salesman Problem (TSP) compared to the standard evolutionary algorithm method which states that the PRoFIFEA method is able to find a more optimal solution.
E-Government Integration of Sidoarjo Regency using Service Oriented Architecture (SOA)

the slow process of government services is a major problem of current government service systems (e-government). This is due to the lack of integration between e-government systems with regional governments. The application of data integration can accelerate the process of interaction and communication between agencies in the regional government. This study aims to develop an integrated e-government from various services that exist in government services in Sidoarjo Regency. The architecture used in developing this integration is using the Service Oriented Architecture (SOA). In the e-government development process has 3 main stages, namely: database creation, service creation, and service implementation on the frontend. The results of this study are in the form of conclusions on the architectural capabilities used in e-government integration accompanied by the speed of services produced.
Reconfigurable Snapshot HDR Imaging Using Coded Masks and Inception Network

This work was supported by King Abdullah University of Science and Technology as part of VCC center baseline funding. Masheal Alghamdi is supported by King Abdulaziz City for Science and Technologyscholarship
MOAFS: A Massive Online Analysis library for feature selection in data streams

Each feature selection algorithm performs efficiently depending on different circumstances, such as data dimensionality (low, medium, high or ultra), speed rate, attribute type (nominal or numerical), number of classes, among others.Therefore, to perform different experiments on some of the most relevant feature selection algorithms proposed for data streams classification problems, the Massive Online Analysis Feature Selection (MOAFS) was created.MOAFS is a library for the Massive Online Analysis (MOA) (Bifet, Holmes, Kirkby, & Pfahringer, 2010) framework, and it is based on the MOAReduction (Ramírez-Gallego, Krawczyk, García, Woźniak, & Herrera, 2017) extension.It contains seven feature selection algorithms to be used as dimensionality reduction techniques in data streams classification problems, especially in the text-domain field, since they are not directly available on MOA.MOAFS includes incremental versions of information-based filter algorithms such as Information Gain and Gain Ratio (Quinlan, 1986), Symmetrical Uncertainty used by the Fast Correlation-Based Filter (Yu & Liu, 2003), Chi-squared (Pearson, 1992) and Cramers V-Test (Cramer, 1946), as well as wrapper algorithms such as Online Feature Selection (Wang, Zhao, Hoi, & Jin, 2014) and Extremal Feature Selection (Carvalho & Cohen, 2006).Therefore, MOAFS is a package for MOA to perform feature selection in data streams classification problems.
The 2^k Neighborhoods for Grid Path Planning

Grid path planning is an important problem in AI. Its understanding has been key for the development of autonomous navigation systems. An interesting and rather surprising fact about the vast literature on this problem is that only a few neighborhoods have been used when evaluating these algorithms. Indeed, only the 4- and 8-neighborhoods are usually considered, and rarely the 16-neighborhood. This paper describes three contributions that enable the construction of effective grid path planners for extended 2k-neighborhoods; that is, neighborhoods that admit 2k neighbors per state, where k is a parameter. First, we provide a simple recursive definition of the 2k-neighborhood in terms of the 2k-1-neighborhood. Second, we derive distance functions, for any k ≥ 2, which allow us to propose admissible heuristics that are perfect for obstacle-free grids, which generalize the well-known Manhattan and Octile distances. Third, we define the notion of canonical path for the 2k-neighborhood; this allows us to incorporate our neighborhoods into two versions of A*, namely Canonical A* and Jump Point Search (JPS), whose performance, we show, scales well when increasing k. Our empirical evaluation shows that, when increasing k, the cost of the solution found improves substantially. Used with the 2k-neighborhood, Canonical A* and JPS, in many configurations, are also superior to the any-angle path planner Theta* both in terms of solution quality and runtime. Our planner is competitive with one implementation of the any-angle path planner, ANYA in some configurations. Our main practical conclusion is that standard, well-understood grid path planning technology may provide an effective approach to any-angle grid path planning.
Following Surgical Trajectories with Concentric Tube Robots via Nearest-Neighbor Graphs

Concentric tube robots, or CTRs, are tentacle-like robots composed of precurved telescoping tubes (Fig. 1a) and are controlled by rotating and translating each individual tube [6]. Their dexterity and small diameter enable minimally-invasive surgery in constrained areas, such as accessing the pituitary gland via the sinuses. Unfortunately, their unintuitive kinematics make manually guiding the tip while also avoiding obstacles with the entire tentacle-like shape extremely difficult [19]. This motivates a need for new user interfaces and planning algorithms.
Width-Based Lookaheads Augmented with Base Policies for Stochastic Shortest Paths

Sequential decision problems for real-world applications often need to be solved in real-time, requiring algorithms to perform well with a restricted computational budget. Widthbased lookaheads have shown state-of-the-art performance in classical planning problems as well as over the Atari games with tight budgets. In this work we investigate width-based lookaheads over Stochastic Shortest paths (SSP). We analyse why width-based algorithms perform poorly over SSP problems, and overcome these pitfalls proposing a method to estimate costs-to-go. We formalize width-based lookaheads as an instance of the rollout algorithm, give a definition of width for SSP problems and explain its sample complexity. Our experimental results over a variety of SSP benchmarks show the algorithm to outperform other state-of-the-art rollout algorithms such as UCT and RTDP
Automatic Slide Generation for Scientific Papers

We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the current sentence to rank the sentences. Once, the sentences are ranked, salient sentences are selected using Integer Linear Programming (ILP). Our results show the efficacy of our model for summarization and the slide generation task.
Emerging Data Security Solutions in HADOOP based Systems: Vulnerabilities and Their Countermeasures

Hadoop is a framework which manages and stores structured or unstructured big data on clusters in a known environment. But the data gets vulnerable due to the lack of default potent security tools in Hadoop. Many security issues which were not given preference earlier emerge due to its flexibility. These issues are dangerous for our data and could lead to an attack. The users are forced to install various external security tools to protect their data clusters. The security of Hadoop framework is getting better in the versions but it has a very little effect. The goals of this paper are to provide an in-depth survey of Hadoop framework, to provide some information about Hadoop security threats, to outline their challenges and resolve the issues. Further, we acknowledge the challenges associated based on security services of Hadoop to spur the further analysis.
An End-to-End Trainable System for Offline Handwritten Chemical Formulae Recognition

In this paper, we propose an end-to-end trainable system for recognizing handwritten chemical formulae. This system recognize once a time a chemical formula, instead of one chemical symbol or a whole chemical equation, which is in line with people's writing habits, at the same time could help to develop methods for the complicated chemical equations recognition. The proposed system adopts the CNN+RNN+CTC framework, which is one of state of the art methods in imagebased sequence labelling tasks. We extend the capability of the CNN+RNN+CTC framework to interpret 2D spatial relationships (such as 'subscript' existing in chemical formula) by introducing additional labels to represent them. The system evaluated on a self-collected data set of 12,224 samples, achieves the recognition rate of 94.98% at the chemical formula level.
Fast projective image rectification for planar objects with Manhattan structure

This paper presents a method for metric rectification of planar objects that preserves angles and length ratios. An inner structure of an object is assumed to follow the laws of Manhattan World i.e. the majority of line segments are aligned with two orthogonal directions of the object. For that purpose we introduce the method that estimates the position of two vanishing points corresponding to the main object directions. It is based on an original optimization function of segments that estimates a vanishing point position. For calculation of the rectification homography with two vanishing points we propose a new method based on estimation of the camera rotation so that the camera axis is perpendicular to the object plane. The proposed method can be applied for rectification of various objects such as documents or building facades. Also since the camera rotation is estimated the method can be employed for estimation of object orientation (for example, during a surgery with radiograph of osteosynthesis implants). The method was evaluated on the MIDV-500 dataset containing projectively distorted images of documents with complex background. According to the experimental results an accuracy of the proposed method is better or equal to the-state-of-the-art if the background occupies no more than half of the image. Runtime of the method is around 3ms on core i7 3610qm CPU.