Reviewerly/exHarmony huggingface dataset:
Sustainable Development Goals: Tracing Social Media’s Tamil Citizen Voices in Indian context

As per Human Development Index of 2019 report, India is in the 129th rank. Nearly 28% of the Indian population lives below the poverty line. As per another report from The Lancet journal on healthcare index, India is in 154th position among the 195 countries. According to India’s Annual Status of Education Report 2017, 14% of children in the age group of 14-18 are not enrolled in any school system. Education, health and living standards are the primary factors to measure the quality of life in any social settings. Giving this grim view of social conditions in India, the role of public institutions as well as individual members’ are crucial in lending adequate support to the society to enhance the standings of living conditions. According to a study published in 2011, only 2% of news space were being given to the issues pertinent to development by the leading Indian national newspapers. Many news stories in these 2%, might have appeared due to 'Coups and earthquakes syndrome’ type of events that contained news values of death, disease and disaster. From the perspective of Sustainable Development Goals, the participatory approach of individuals is more meaningful in order to achieve the stated 17 goals as well as 169 targets. Apart from the government’s initiative for the SDGs and substantial fundings from the donor agencies, awareness about the SDGs is essential and ideal for the bottom-up approach towards achieving the development goals and its targets by 2030. In this respect, with the help of twitteR package of and text mining tools of R programming, citizen’s voice was measured for those keywords and its equivalent hashtags which were appeared in June 2018. English is being the elite language in India, one regional language Tamil would be included to compare the non-English private sphere with regard to SDGs in Indian context.
Benchmarking Learned Indexes

Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art traditional baselines. Using four real-world datasets, we demonstrate that learned index structures can indeed outperform non-learned indexes in read-only in-memory workloads over a dense array. We also investigate the impact of caching, pipelining, dataset size, and key size. We study the performance profile of learned index structures, and build an explanation for why learned models achieve such good performance. Finally, we investigate other important properties of learned index structures, such as their performance in multi-threaded systems and their build times.
IMPROVING STUDENTSâ€™ READING COMPREHENSION ON NARRATIVE TEXT THROUGH MIND MAPPING TECHNIQUE AT SMA SANTO PETRUS MEDAN

This study is about improving studentsâ€™ reading comprehension on narrative text through mind mapping technique at SMA SANTO PETRUS Medan. The theory applying is Buzanâ€™s theory of mind mapping and its method are quantitative and qualitative methods. This reseach is Classroom Action Research to apply the mind mapping technique in teaching reading. The data are collected through observations, interviews, and documentations. The qualitative data consist of the table of studentsâ€™ scores, and the quantitative data consist of diary notes, interview transcripts, documentations, and studentsâ€™ works. This research consisted of two cycles. It was conducted collaboratively with the English teacher. The subjects of this research were 36 students of class X MIA in SMA Santo Petrus Medan.Â The result of this study finds that mind mapping technique can improve the studentsâ€™ reading comprehension. It is proved by the studentsâ€™ ability in reading comprehension and the studentsâ€™ scores after the test were given. In the pre-test, the studentsâ€™mean score is 61,44. Then, in the formative test cycle 1, the studentsâ€™ mean score is 62, and in the post-test cycle 2, the studentsâ€™mean score is 72. It is conclude by the mind mapping can improve studentsâ€™ reading comprehension achievement.
Sickle Cell Disease - A Comprehensive Study and Usage of Technology for Diagnosis

Sickle cell is haematological disorder (haematology is a study of blood in health and diseases) which may lead to an organ damage, heart strokes and serious complications. It may also reduce human life span. Most of the sickle cells are observed in new born babies. At the start of sickle cells in human people though it’s a kind of feature in tribal people but it has spread over the world. Sickle cell Symptoms are observed in human beings as episodes of pains (crisis), Vision problems, swelling of hands and Feet. Sickle Cell Disease (SCD) can harm patient’s spleen (slightly pain at left Ribs). If one organ is affected in human body, then slowly it affects the entire body by spreading into Brain, Lungs, Heart, Liver, Kidneys, Joints, Eyes, Penis, Skin or Bone. This paper is aimed at presenting the complete details of the SCD with its properties, symptoms, signs, treatment for this disease. This is also a comprehensive study and characteristics of this disease with other similar diseases. The technological implications and usage in the field of SCD for better accuracy of identification of the disease is presented.
An Efficient and Wear-Leveling-Aware Frequent-Pattern Mining on Non-Volatile Memory

Abstract Frequent-pattern mining is a common approach to reveal the valuable hidden trends behind data. However, existing frequent-pattern mining algorithms are designed for DRAM, instead of persistent memories (PMs), which can lead to severe performance and energy overhead due to the utterly different characteristics between DRAM and PMs when they are running on PMs. In this paper, we propose an efficient and Wear-leveling-aware Frequent-Pattern Mining scheme, WFPM, to solve this problem. The proposed WFPM is evaluated by a series of experiments based on realistic datasets from diversified application scenarios, where WFPM achieves 32.0% performance improvement and prolongs the NVM lifetime of header table by 7.4× over the EvFP-Tree.
KEEFEKTIFAN MODEL TGT BERBANTU PERMAINAN TEBAK GERAK UNTUK MENINGKATKAN KREATIVITAS PADA PEMBELAJARAN TEMATIK

In thematic learning the learning independence of students is still lacking and has not optimized the ability to think creatively to improve their creativity due to a lack of confidence so that they have not achieved maximum learning outcomes. Efforts to improve using the Team Games Tournament model are aided by guessing games. The method used in this study is quantitative research. This research is a pre-experimental design with one group pretest-posttest design. The research subjects were fifth grade students of Wonosoco Kudus Elementary School with a total of 20 students. The results of the study showed that learning using the Team Games Tournament model helped the Guess the Motion game to improve the creativity of class V children viewed from the pretest average of 60.55 and the posttest average of 83.35. This is evidenced by the t test of tcount (15,630)&gt; t table (1,684) then H0 is rejected and Ha is accepted. So the conclusion is effective using the Team Games Tournament (TGT) model with the help of Guess the Motion game to Improve Children's Creativity in Thematic Learning.
PoseGAN: A Pose-to-Image Translation Framework for Camera Localization

Camera localization is a fundamental requirement in robotics and computer vision. This paper introduces a pose-to-image translation framework to tackle the camera localization problem. We present PoseGANs, a conditional generative adversarial networks (cGANs) based framework for the implementation of pose-to-image translation. PoseGANs feature a number of innovations including a distance metric based conditional discriminator to conduct camera localization and a pose estimation technique for generated camera images as a stronger constraint to improve camera localization performance. Compared with learning-based regression methods such as PoseNet, PoseGANs can achieve better performance with model sizes that are 70% smaller. In addition, PoseGANs introduce the view synthesis technique to establish the correspondence between the 2D images and the scene, \textit{i.e.}, given a pose, PoseGANs are able to synthesize its corresponding camera images. Furthermore, we demonstrate that PoseGANs differ in principle from structure-based localization and learning-based regressions for camera localization, and show that PoseGANs exploit the geometric structures to accomplish the camera localization task, and is therefore more stable than and superior to learning-based regressions which rely on local texture features instead. In addition to camera localization and view synthesis, we also demonstrate that PoseGANs can be successfully used for other interesting applications such as moving object elimination and frame interpolation in video sequences.
A Position Aware Decay Weighted Network for Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment polarity of a text given another text segment or aspect. In ABSA, a text can have multiple sentiments depending upon each aspect. Aspect Term Sentiment Analysis (ATSA) is a subtask of ABSA, in which aspect terms are contained within the given sentence. Most of the existing approaches proposed for ATSA, incorporate aspect information through a different subnetwork thereby overlooking the advantage of aspect terms’ presence within the sentence. In this paper, we propose a model that leverages the positional information of the aspect. The proposed model introduces a decay mechanism based on position. This decay function mandates the contribution of input words for ABSA. The contribution of a word declines as farther it is positioned from the aspect terms in the sentence. The performance is measured on two standard datasets from SemEval 2014 Task 4. In comparison with recent architectures, the effectiveness of the proposed model is demonstrated.
3δ principle and boxplot method are used to estimate the cost interval of tower replacement technology

Abstract The replacement of tower is an important part of the technical reform of power grid lines. To analyze the rationality of the cost of the technical reform of tower, it is necessary to deal with the abnormal engineering data based on the cost data of previous projects. In this paper, the unit cost of tower replacement is sorted out by using 3δ principle and boxplot method, and the reasonable cost range is obtained to provide a basis for project investment.
Analysis of Discriminant Model on Marketing Mix Against the Decision of Buying Traditional Weaving Handicraft Products of Karo District

This research aims to know the function of discrimination and to know the level of precision marketing classification mix against the decision of purchasing products traditional weaving fabric Karo district. The interest rate of local and international buyers is very low due to the expensive price and the effort of obtaining raw materials such as yarn research method used in this study is a descriptive explorative. According to Usman (Usman, 2009) that any research is definitely descriptive (clarifying), then this research includes a descriptive research-exploratory. This research was conducted to test the hypothesis proposed using the research methods that have been designed in accordance with the variables examined in order to obtain accurate results. This research-based discriminant analysis. Result of discriminant function accuracy of marketing mix to purchase decision of traditional weaving fabric of Karo district.
Dynamic proofs of retrievability with low server storage

Proofs of Retrievability (PoRs) are protocols which allow a client to store data remotely and to efficiently ensure, via audits, that the entirety of that data is still intact. A dynamic PoR system also supports efficient retrieval and update of any small portion of the data. We propose new, simple protocols for dynamic PoR that are designed for practical efficiency, trading decreased persistent storage for increased server computation, and show in fact that this tradeoff is inherent via a lower bound proof of time-space for any PoR scheme. Notably, ours is the first dynamic PoR which does not require any special encoding of the data stored on the server, meaning it can be trivially composed with any database service or with existing techniques for encryption or redundancy. Our implementation and deployment on Google Cloud Platform demonstrates our solution is scalable: for example, auditing a 1TB file takes just less than 5 minutes and costs less than $0.08 USD. We also present several further enhancements, reducing the amount of client storage, or the communication bandwidth, or allowing public verifiability, wherein any untrusted third party may conduct an audit.
MixBoost: A Heterogeneous Boosting Machine

Modern gradient boosting software frameworks, such as XGBoost and LightGBM, implement Newton descent in a functional space. At each boosting iteration, their goal is to find the base hypothesis, selected from some base hypothesis class, that is closest to the Newton descent direction in a Euclidean sense. Typically, the base hypothesis class is fixed to be all binary decision trees up to a given depth. In this work, we study a Heterogeneous Newton Boosting Machine (HNBM) in which the base hypothesis class may vary across boosting iterations. Specifically, at each boosting iteration, the base hypothesis class is chosen, from a fixed set of subclasses, by sampling from a probability distribution. We derive a global linear convergence rate for the HNBM under certain assumptions, and show that it agrees with existing rates for Newton's method when the Newton direction can be perfectly fitted by the base hypothesis at each boosting iteration. We then describe a particular realization of a HNBM, MixBoost, that, at each boosting iteration, randomly selects between either a decision tree of variable depth or a linear regressor with random Fourier features. We describe how MixBoost is implemented, with a focus on the training complexity. Finally, we present experimental results, using OpenML and Kaggle datasets, that show that MixBoost is able to achieve better generalization loss than competing boosting frameworks, without taking significantly longer to tune.
An Exploratory Factor Analysis of AI Enabled SE Attacking Risk in Higher Learning Institute

Any kind of business organization or individual organization one word is very common that is information. Depending on the information and its communication medium ensure the effective of business. Whereas it is very much essential to understanding Artificial Intelligence (AI) Enabled Social Engineering (SE) attacks and its security risk management approach. Under such circumstance, information is exchanged from one country to another country for various purposes. In that case, artificial intelligence enabled social engineering attack must be considered in any kind of organization. Any kind of business venture is disturbed by this kind of attacks which will be failure the business operation, whilst enabling them to concentrate on their core business activities. Social engineering is one kind of criminal activities in the information security. It has proven to be very successful way for a criminal to get inside an organization. Once social engineering got password from an employee, then snooping around the sensitive data. However social engineering are the types of attacks inherent risk and any kind of organization should be aware of its potential risks. So, it could be identify and resolved quickly. The objective of this article, therefore, conducting an Exploratory Factor Analysis (EFA) in artificial intelligence enabled social engineering attacks on various organizations. Which consequently provide knowledge of the most relevant information security risk factor. For this article distributed 300 questionnaires in education sector for the study and 110 were returned. That means the response rate is 36%. In that case, the finding of the article shows that threat and vulnerability factors in artificial intelligence enabled social engineering attacks. So, for any organization these two factors are the mostfor information security risk.
Research and Application of Path Planning Algorithm in Complex Terrain

Abstract Aiming at the failure of path planning in complex three-dimensional terrain, a path planning algorithm based on Hopfield neural network is proposed. According to the three-dimensional terrain, the undulating terrain and obstacles are transformed into a calculable regular figure, and the terrain function model is established according to the flow field control equation; the terrain function model is effectively integrated with Hopfield neural network algorithm. The algorithm is applied to complex three-dimensional terrain environment, can avoid undulating terrain and obstacles, and find an optimal path, which lays an important foundation for the navigation vehicle path planning.
Design Patterns in Practice from the Point of View of Developers

Design patterns are a relevant research topic that has been empirically investigated by academia in the last years. However, there is still a gap in the perception of the use of design patterns in practice. In this paper, we aim to bridge this gap by analyzing a real Brazilian scenario. Considering that Belo Horizonte is one of the main Brazilian cities in the context of software development, we decide to carry out a survey with 58 active developers from this city. The results exhibited in this paper bring a real perception of the use of design patterns in a relevant center of software development. In this survey, we have identified that design patterns are not widely disseminated in the local industry since 40% of the participants claimed do not frequently make use of them. We have also found that the lack of use of design patterns is mainly associated with the lack of knowledge of these solutions by the developers besides the absence of incentives from the companies. Moreover, we discussed some benefits pointed out by the participants and listed the design patterns most used and less used by the developers.
Named Entity Extraction with Finite State Transducers.

We describe a named entity tagging system that requires minimal linguistic knowledge and can be applied to more target languages without substantial changes. The system is based on the ideas of the Brill's tagger which makes it really simple. Using supervised machine learning, we construct a series of automatons (or transducers) in order to tag a given text. The final model is composed entirely of automatons and it requires a lineal time for tagging. It was tested with the Spanish data set provided in the CoNLL-$2002$ attaining an overall $F_{\beta = 1}$ measure of $60\%.$ Also, we present an algorithm for the construction of the final transducer used to encode all the learned contextual rules.
An Exploratory Study of Argumentative Writing by Young Students: A Transformer-based Approach

We present a computational exploration of argument critique writing by young students. Middle school students were asked to criticize an argument presented in the prompt, focusing on identifying and explaining the reasoning flaws. This task resembles an established college-level argument critique task. Lexical and discourse features that utilize detailed domain knowledge to identify critiques exist for the college task but do not perform well on the young students data. Instead, transformer-based architecture (e.g., BERT) fine-tuned on a large corpus of critique essays from the college task performs much better (over 20% improvement in F1 score). Analysis of the performance of various configurations of the system suggests that while children's writing does not exhibit the standard discourse structure of an argumentative essay, it does share basic local sequential structures with the more mature writers.
In Darwin’s Garden: An Evolutionary Exploration of Augmented Reality in Practice

This chapter discusses the rapid developments in augmented reality and mixed reality technologies, from a practitioner’s perspective of making the augmented reality sculptural work In Darwin’s Garden. From its conception in 2012, to its exhibition at Carbon Meets Silicon II in 2017, the advances in augmented reality technology led to an interplay between the goal of the creators and the technological realisation of that vision. The art, design and technology involved, generated a reactive process that was mired in external influences as the accessibility to augmented reality became commercially valuable and subsequently restricted. This chapter will be of interest to anyone who wants to understand more about the possibilities, technologies and processes involved in realising mixed reality practice and about the commercial culture that supports it.
Probabilistic Solar Forecasts Evaluation Part 2: Quantile Forecasts

Two types of probabilistic forecasts are used in the field of solar energy forecasting.The first one takes the form of Ensemble forecasts commonly provided by meteorological utilities such as ECMWF.The second one is based on statistical methods that generate quantiles forecasts.The first type of forecasts is treated in the first part of this article.In this second part, we focus on the assessment of the quality of quantile forecasts characterized by two main attributes namely reliability and resolution.More precisely, we propose a verification framework based on diagnostic tools and quantitative scoring rules that are specifically designed for the evaluation of quantile forecasts.The probabilistic models are evaluated on two selected sites that experience very different climatic conditions.It is shown that the decomposition into reliability and resolution of a scoring rule, the Continuous Ranked Probability Score (CRPS), may help to obtain a detailed picture of the performance of the models and consequently may help in selecting the best probabilistic method.
Automatic Query Optimization for Retrieving Traffic Tweets

Twitter, like many social media and data brokering companies, makes their data available through a search API (application programming interface). In addition to filtering results by date and location, researchers can search for tweets with specific content with a boolean text query, using {\it AND}, {\it OR}, and {\it NOT} operators to select the combinations of phrases which must, or must not, appear in matching tweets. This boolean text search system is not at all unique to Twitter and is found in many different contexts, including academic, legal, and medical databases, however it is stretched to its limits in Twitter's use case because of the relative volume and brevity of tweets. In addition, the semi-automated use of such systems was well studied under the topic of Information Retrieval during the 1980s and 1990s, however the study of such systems has greatly declined since that time. As such, we propose updated methods for automatically selecting and refining complex boolean search queries that can isolate relevant results with greater specificity and completeness. Furthermore, we present preliminary results of using an optimized query to collect a sample of traffic-incident-related tweets, along with the results of manually classifying and analyzing them.