Reviewerly/exHarmony huggingface dataset:
ANALISIS PENGGUNAAN BAHASA PROKEM MELALUI MEDIA SOSIAL WHATS APPLICATION (WA) PADA SISWA SMA

Prokem language is a language that is often used by adolescents, especially among high school students, which in its use often occurs in language symptoms in phonological, morphological, and semantic aspects. The purpose of this study are: 1) to describe the language of the program used by high school students, and 2) to describe the results of the analysis of the language of the aspects of language (phonology, morphology and semantics). Qualitative descriptive method is a method used by researchers in processing data. Data is collected using the observation method. All high school students were used by researchers as a population and 25 students as samples. The results showed: 1) high school students still use professional language when communicating with adults through WhatsApp social media, 2) based on the results of analysis from phonological aspects, students often change certain fonts, such as phonemes / a / phonemes / e /, phoneme additions , as well as the use of regional languages, 3) based on morphological aspects, there are uses of affixes derived from Sundanese, and 4) based on semantic aspects, students often use acronyms derived from English as well as prokem from Javanese and Arabic.
A new method of imputation for the missing value in the IN/OUT procedure of the Random Forest (RF)

Objective:The main objective of study is to propose a new method of imputation for missing data.The study discuss misclassification rate, out of bag error for simulated and real data.Method: In this article, a new imputation method has been proposed for IN/OUT procedure of Random Forest (RF).The proposed method does not depend on the missing data mechanisms which are the principal advantages of this method.The method was evaluated and compared with non-missing data sets.Findings and Conclusion: It is concluded that the proposed method reduced the Out-of-Bag error and also the misclassification error rates in case of missing values using IN/OUT Procedure of RF and Conventional RF procedure at the different level of missing percentages.The proposed method gives interesting results in case of (5-15)% missing data and after that, the rest of the results are same therefore no need to compute the results for this percentage % of missing values.The most important is that this method was first time developed in the IN/OUT procedure of RF and conventional RF.Novelty/Motivation: Missing values a serious problem for all statistical problems.RF and IN/ OUT RF are not exception.Therefore a bootstrap based method to impute missing value in the IN/OUT RF was developed.
INCIDENCE OF BULLYING AND ACADEMIC PERFORMANCE OF GRADE 7 LEARNERS

The study determined the incidence of bullying and academic performance of learners in public secondary schools in Dulag, Leyte during the School Year 2017-2018. The measured variables included the learners’ profile (age, sex, custodian, types of family, types of personality), bullying incidence (physical, verbal, psychological and cyberbullying) and academic performance (Filipino, English, Mathematics, Sciences, Social Studies [Araling Panlipunan], Technology and Livelihood Education [TLE], Music, Arts, Physical Education and Health [MAPEH], and Values Education [Edukasyon sa Pagpapakatao – EsP]). The study likewise examined the association between the selected profile indicators, bullying incidence and academic performance of learners. Quota sampling was employed to randomly chosen one thousand Grade 7 respondents for the study. Descriptive and correlational research designs were employed where data generation was carried out utilising a research questionnaire. The Pearson r was used to test the association among the study variables. Findings revealed that there were more female respondents than their male counterpart. Their combined mean age was 12.78 years with parents as their custodian. The large majority belonged to nuclear type of family. They exhibited moderate level type of personality and experienced various forms of bullying: physical, verbal, psychological, and cyber bullying. Sending harmful text messages on Facebook and other social media platform emerged as most bullying acts experienced by Grade 7 learners. The learners manifested very satisfactory academic performance on courses (or subjects) not requiring highly technical and critical thinking (MAPEH, TLE and EsP) while only satisfactory performance in Mathematics and Science courses which required analytical skills. In conclusion, age, type of family and type of personality are statistical associated with the incidence of bullying such that bullying has negative bearing on academic performance. Younger learners are more likely to experience physical and cyber bullying than their older counterpart. The incidence of bullying has opposite bearing on academic performance, the higher the incidence of bullying among learners, the lower their academic performance. This study confirms the arguments put forward in the Social Dominance Theory (Sidanius and Pratto, 1999), the Choice Theory (Glasser, 1998) and the Social Ecological Theory (Bronfenbrenner, 1999). Article visualizations:
Alignment of Sequences Allowing for Non-overlapping Unbalanced Translocations of Adjacent Factors

Unbalanced translocations take place when two unequal chromosome sub-sequences swap, resulting in an altered genetic sequence. Such large-scale gene modification are among the most frequent chromosomal alterations, accounted for 30% of all losses of heterozygosity. However, despite of their central role in genomic sequence analysis, little attention has been devoted to the problem of aligning sequences allowing for this kind of modification. In this paper we investigate the sequence alignment problem when the edit operations are non-overlapping unbalanced translocations of adjacent factors. Specifically, we present an alignment algorithm for the problem working in $$\mathcal {O}(m^3)$$ -time and $$\mathcal {O}(m^3)$$ -space, where m is the length of the involved sequences. To the best of our knowledge this is the first solution in literature for the alignment problem allowing for unbalanced translocations of factors.
Face Memorization System Using the Mathematical AIM Model for Mobile Robot

In this paper, we present a bio-inspired method to help make efficient utilization of computing resource and battery of a robot system, and at the same time, help a robot to draw people's attention and leave a friendly impression. To achieve the goals described above, firstly, the mathematical Activation-Input-Modulation (AIM) model is applied to the robot, which can emulate the human states of consciousness (e.g., waking, sleeping). By using it, the robot will be able to switch state according to the external environment. When there is nobody around the robot, it turns the state from waking to sleeping, cutting down a part of information processing to economize the utilization of computing resource and battery. Secondly, we propose to develop a face memorization system which enables the robot to recognize the human face by using deep/machine learning methods. When the robot meets a person whose face was memorized, it greets that person by calling his/her name to draw people's attention and leave a friendly impression on them. Besides, in order to make effective use of the sleeping time, when the robot is in the sleeping state, we propose to assign works to the robot that are difficult or unnecessary to be processed when in the waking state, such as image pre-processing and classifier training. These works are also necessary to implement a face recognition function that can achieve high accuracy. Finally, experiments using the mathematical AIM model and the face memorization system together were conducted to demonstrate the effectiveness of the proposed method on the utilization of computing resource and battery.
EyeR

Lack of adequate support in navigation and object detection can limit independence of visually impaired (VI) people in their daily routines. Common solutions include white canes and guide dogs. White canes are useful in object detection, but require physically touching objects with the cane, which may be undesired. Guide dogs allow navigation without touching objects in the vicinity, but cannot help in object detection. By addressing this gap, employing a user-centric research approach, we aim to find a solution to improve the independence of VI people. Here, we began by initially gathering requirements through online questionnaires. Working from this, we build a prototype of a glove that alerts its users when an obstacle is detected at the pointed position; we call this EyeR. Lastly, we evaluated EyeR with VI users and found out that in use our prototype provides real time feedback and is helpful in navigation. We also provide future recommendations for VI prototypes from our participants, who would additionally like the device to recognise objects.
Fuzzy-rule based approach for feature selection in text classification

A well representing feature set that has enough differentiating power plays an important role in classification. The existing techniques for feature set selection are mostly statistical. They only make decision between a feature inclusion or exclusion without taking into account the importance of human thinking and reasoning that surely effect the results of classification and prediction. In this paper, I propose a new method of feature selection and employed it via Fuzzy Set Theory (FST). The effectiveness of proposed method is evaluated in terms of improving the classifier's performance as compared to state of the art methods such as Gini Index, Information Gain, and Document Frequency. A case study is performed with widely known classifiers Naive Bayes, Support Vector Machine and Random Forest in the context of classification of software design patterns. The promising results indicate the applicability of proposed method in context of automated systems of certain domains.
Towards an Automatic Detection of Sensitive Information in Mongo Database

Before talking about the security tools we must first think about what we should protect and how we should distinguish information that seems to be sensitive and identifiable among heterogeneous data that spread over several sources like Facebook, twitter and several other suppliers of big data. Thus, in this paper we proved a method of identifying sensitive information in mongo data store. In this paper we propose an innovative approach and its implementation as an expert system to achieve the automatic detection of the candidate attributes for fragmentation. Our approach is mainly based on semantic rules that determine which concepts have to be fragmented, and on a linguistic component that retrieves the attributes that semantically correspond to these concepts. Since attributes cannot be considered independently from each other we also address the challenging problem of the propagation of it among the whole mongo database. An important contribution of our approach is to provide a semantic modeling of sensitive data.
Bridging the Gap Between Business, Design and Product Metrics

The integration of User-Centered Design with Agile practices studies the interactions between designers and developers and the alignment of the design and development processes. However, beyond the interactions with the development team, designers are often required to operate within a wider business context, driven by goals set on high-level metrics, like Monthly Active Users, and to show how design-led initiatives and improvements address those metrics. In this paper we generalize learnings from prior work on applying usability improvements to Jira, a project tracking software tool created by Atlassian, and we describe a structured approach to bridging the gap between feature work and business metrics.
Moving Towards a Journal-centric Publication Model for CHI

As a scholarly field, the ACM SIGCHI community maintains a strong focus on conferences as its main outlet for scholarly publication. Historically, this originates in how the field of computer science adopted a conference-centric publication model as well as in the organizational focus of ACM. Lately, this model has become increasingly challenged for a number of reasons, and multiple alternatives are emerging within the SIGCHI community as well as in adjacent communities. Through revisiting examples from other conferences and neighboring communities, this panel explores alternative publication paths and their opportunities and risks.
Development of learning media in circular motion for Senior High School using ICT based on contextual learning

A preliminary study was conducted to find topics of senior high school physics subject whose learning has not been supported by the media and appropriate teaching materials. The media and teaching materials are those that facilitate students to foster attitudes, build knowledge, and train sciences process skills through physics learning. The media and teaching materials should use contextual. For certain topics Information Communication and technology (ICT) assistance is needed, because there needs to be engineering on the duration of time or the size of the object under study. Obtained information that In high school X grade in senior high school, only a quarter of the tasks that apply the contextual approach and implementation of the tasks and responsibilities of the topic X grade Senior High School (SMA) that utilizes ICT appropriately. Based on these results, it is necessary to do research media learning based on contextual learning to use ICT in the manufacture and presentation. In the current study the topic is circular motion.
A search-based software engineering for defect prediction in ubuntu ecosystem

A software defect may cost a lot for certain critical deployment such as communication and inter-process management that affects the CPU processes. A lot of time and efforts can be saved if we can predict the potential defect. In this study, we propose an approach to identify and predict the software defects that make use of Search-Based Software Engineering (SBSE) via a network model that collects its dataset from Ubuntu package repositories. We create a network model of package dependency that generated from the repository via a graph utility, namely debtree. In order to explicate the conflict between packages, we analyze the package inter-dependencies as related graph clusters. Since the massive number of dependencies, we use several Machine Learning methods to analyze and predict the potential software defect. Our investigation result shows that the SBSE technique has efficiently improved the defect identification process.
Kicked rotor quantum chaos (KRQC) for radar noise waveform design

Noise radars have many advantages over conventional radars and receive great attentions recently. The waveforms and their characteristics of a noise radar are the crucial factors that impact the performance of this radar. Noise waveforms are usually generated by random number generators or logistic mapping. In this paper, we propose the kicked rotor quantum chaos perturbation (KRQCP) approach. First, we investigate the characteristics of noise waveforms generated by kicked rotor quantum chaos and demonstrate that like random number generation approach and logistic mapping approach, noise waveforms generated by kicked rotor quantum chaos (KRQC) also show the fractal relation between sidelobe values and code lengths. Then, we propose using KRQC noises to perturb quadratic phases and develop the KRQCP noise waveform generation method. We demonstrate that the performance for the noise waveforms generated by the new method is considerably higher than traditional random number generator and logistic mapping approaches.
Window Size Estimation for Nearest Neighbour Compliant Quantum Circuit Mapping

In general, quantum circuits permit any two logical qubits to be combined via a quantum gate. When these are to be deployed on locally-connected grid-shaped quantum processors, swap operations are required to make adjacent interacting qubits. Swap insertion algorithms are complex and time-consuming. Windowing limits this complexity by considering only a subset of the circuit's gates when inserting swaps, but has not been applied to the latest generation of swap insertion algorithms, nor has any systematic method been proposed for determining the appropriate window length. This paper shows how off-line analysis of swap density across the circuit identifies thresholds for window length which limit increases in the number of swap gates. When adopted, speed-ups in the swap insertion process asymptotically approach 100% with the length of the circuit, whilst maintaining swap costs at comparable levels to non-windowed algorithms.
Towards a Graph of (American) Tech Companies

A number of large technology companies, or so-called "tech giants", such as Alphabet/Google, Amazon, Apple, Facebook, and Microsoft, are increasingly dominant in people's daily lives, and critically studied in fields such as Science, Technology and Society (STS) studies, with an emphasis on technology, data, and privacy. This project aims to contribute to research at the intersection of technology and society with a prototype visualization tool that shows the vast spread and scope of these large technology companies. In this paper, a prototype graph visualization of notable American technology companies, their acquisitions, and services is presented. The potential applications and limitations of the visualization tool for research are explored. This is followed by a discussion of applying the visualization tool to research on personal data and privacy concerns and possible extensions. In particular, difficulties of data collection and representation are emphasized.
Mortality prediction using medical notes

Mortality prediction is a critical task for assessing patients' conditions in Intensive Care Units (ICU) of hospitals to improve decision-making and quality of care. Measurements taken and recorded at different time points are the main source of information to be used for tasks related to healthcare. However, the notes written by medical service providers during patients' stay in hospital as a rich source of detailed information is not sufficiently exploited. In this work, we propose a Convolutional Neural Network (CNN) architecture to utilize the unstructured texts to predict the pre-discharge and post-discharge mortality of ICU patients. Evaluations show high performance of the proposed method in terms of precision and recall. Moreover, our method outperforms the state of the art method by achieving a higher AUC.
Elektrik Malzemeleri Sektöründe R ve Shiny ile Pazar Sepet Analizine Yönelik Dinamik Bir Uygulama

In this study, it is aimed to determine the products that customers prefer to buy together by using algorithm of association rules Apriori, and to implement an application related to customer relationship management. The data set used in this study was obtained from a company operating in the electricity sector. CRoss-Industry Standard Process for Data Mining (CRISP-DM) model was used during data analysis. Association rules technique Apriori applied to data that contains the two year. Data analysis is performed with R language. RStudio was used as a development tool for R codes. The model performed with Apriori was transferred to web environment via Shiny (shinyapps.io). The cross sales realized between HESNYA-03 and HESNYA-02 products of this brand and the sales of different colors together bring the product into the forefront. From this result, it is thought that by creating various color packages related to the product, customers can increase purchases of less preferred products by being offered new products besides these and similar packages. The user is given the opportunity to query the analyzed data set and make basic arrangements related to the algorithm. This allows the application to be dynamic, independent of time and space.
Living with decade-old vulnerabilities in datacentre software

The underlying assumption that system software is secure by design has proven wrong many times over the past few years. A modern approach to network design requires a new basis – ‘assume vulnerable’. As all defence is inherently a risk management process this assumption requires different methods of quantifying network security, performing changes and monitoring. The assumption that system software is secure by design has been proven wrong many times and a better approach to network design is – ‘assume vulnerable’. As all defence is inherently a risk management process, this assumption requires different methods of quantifying network security, performing changes and monitoring. It is reasonable to assume that there are numerous vulnerabilities across different core system programs and libraries, explains Daniel Goldberg of Guardicore.
An Empirical Study of the Feasibility of Automated Essay Scoring in Teaching EFL Writing in China

The Automated Essay Scoring (AES) is an information system that makes use of corpus and cloud computing technology to evaluate and score compositions intelligently. This English composition evaluation system provides students with independent writing platform and real-time score feedback that encourages them to modify the composition repeatedly. These procedures have relieved the pressure on teachers to correct students' compositions to a certain extent, but at the same time there are some problems that should not be ignored. This paper mainly studies the beneficial aspects and existing problems of the automated evaluation systems and examines whether there will be a positive development trend in the future. Based on the feedback from the teachers and students using the online writing system, this paper makes an analysis of the existing problems, so that the online writing system can be used more reasonably in the teaching of EFL writing.
Civil Liability for Cyberbullying

Cyberbullying has become a notorious epidemic, culminating in widely publicized suicides. Whether a new and distinct problem or an old one in a new guise, the technological setting has undoubtedly generated new challenges and, at the same time, new opportunities for legal response. Regrettably, while delegation of power to educational institutions and criminalization of cyber-misconduct are relatively common, at least in public discourse, the potential impact of civil liability has been downplayed. This Article puts the underexplored regulatory tool under the spotlight. It provides systematic legal and economic analyses of civil liability for cyberbullying, based on a trichotomy of potential defendants—primary wrongdoers, real-life supervisors (parents, schools), and virtual supervisors (mostly online platform operators).Ultimately, the Article lays the foundations for an efficiency-oriented model which integrates technological features to reduce supervisors’ information costs. In order to incentivize parents to reasonably use advanced surveillance applications, the proposed model imposes liability when failure to employ such tools results in juvenile cyber-wrongdoing, in addition to standard liability for not taking reasonable precautions upon learning about the risk. The model also imposes liability on schools for cyberbullying through school devices if they failed to: (1) enforce reliable identification of users, (2) employ advanced surveillance tools, or (3) take reasonable measures to prevent harm upon notification of possible misconduct. Finally, the model holds a virtual supervisor liable if the victim has insufficient information to identify the wrongdoer, the victim gave notice of the complaint, and the virtual supervisor did not properly respond.