Reviewerly/exHarmony huggingface dataset:
Anonymity in social networks: The case of anonymous social media

The anonymity provided by the so-called anonymous social networks is studied in this paper. More precisely, emphasis is given on investigating whether the underlying personal data processing in such networks may suffice to result in tracking or identification of the users. To this end, five popular anonymous smart applications are being analysed through monitoring the outgoing traffic of Android devices in real-time when using these applications, to examine which personal data - including device data - are being processed by either the anonymous networks or third parties such as library providers. The corresponding privacy policies of these applications are also examined, towards evaluating whether the information provided to the users is sufficient. Our analysis concludes that there is personal data processing in place even in such (so-called anonymous) applications which in turn implies that users anonymity cannot ensured, whilst the corresponding privacy policies may leave room for further improvement.
Cultural Context in English Student Textbook Talk Activefor the Tenth Grade Students (A Content Analysis)

Abstract English as a Foreign Language (EFL) has been introduced inseparably from its culture, either source culture, target culture, or international target culture. The integration of cultural contents in ELT (English Language Teaching) materials is strongly related to the current status of English. Being a global and common language, many recent theories suggest that the cultural contents of English materials should not be limited to native English speaking countries and should include local cultural contents. To this end, this research is intended at investigating the existence of cultural contents in an English textbook used by Senior High School students grade X in MAN 1 (Model) Lubuklinggau, entitled Talk Active. To this purpose, the media of cultural presentation, aspects of culture, categories of culture, and implications for language teaching were investigated. The method used in this study was content analysis. Some underlying models proposed by Cortazzi and Jin (1999), Moran (2001), Yuen (2011), and Xiao (2011) were used as the main framework for analyzing the textbook. The investigation revealed that: (1) cultural contents were mostly presented through reading texts and pictures (56.5%) and (88.2%), (2) the dominant aspect of culture was products (33%), (3) the presented culture were mostly about source culture (30%), and (4) there were some strategies that can be applied that related to implications for language teaching. In summary, the exposures of visible cultural aspects of Indonesian culture which were presented through reading texts and pictures dominated the exposures of culture in Talk Active. Based on the findings, it is a need for textbook developers and teachers to provide a more balanced proportion of cultural contents in terms of the dimensions (products, persons, practices, perspectives) and categories of culture (source, target, and international target cultures) to help learners develop their intercultural understanding. Keywords: Language and Culture, Cultural Contents, English Textbook.
Robust Stereo Matching Based on Cost Volume Fusion for Optimal Disparity Estimation

In a stereo-based disparity estimation algorithm, matching cost evaluation is the key process to construct an initial cost volume. In practice, however, various challenges including occlusion region, reflection and radiometric changes result in inaccurately evaluated costs. This paper presents a local stereo matching method based on cost volume fusion. The proposed method consists of four steps: i) image transformation based on log-chromaticity, ii) cost volume generation, iii) cost volume reconstruction based on fusion, and iv) cost aggregation and optimization for optimal disparity estimation. The proposed method simply selects an optimal disparity map by combining various cost volumes with different properties. Therefore, the proposed method can be applied to various three-dimensional imaging applications, such as robot vision and autonomous vehicular systems.
Data Merging and Data Aggregation Using PySparkSQL

Data merging and data aggregation are an essential part of the day-to-day activities of PySparkSQL users. This chapter will discuss and describe the following recipes.
Real-time online action detection and segmentation using improved efficient linear search

More and more attention has been paid to linear-time online action detection and video segmentation, due to wide application in the fields of human-computer interaction, games and surveillance. In this paper we propose a new descriptor which can be adopted for action recognition, online action detection and segmentation. In addition, we propose the improved efficient linear search (improved ELS) whose scheme is modified to solve the problem of the existence of many action classes' maximum subarray sums exceeding their thresholds. Then we evaluated our approach on MSRC-12 and MSR-Action3D datasets. The results show that our descriptor achieves the state-of-the-art results on action recognition and the performance of the improved ELS is much higher than that of the ELS.
NNGDPC: a kNNG-based density peaks clustering

Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, the geometry of the distribution of the data has not been taken into account in the original algorithm. DPC does not perform well when clusters have different densities. In order to overcome this problem, we propose a novel density peaks clustering based on k-nearest neighbour graph called NNGDPC (kNNG-based density peaks clustering) which introduces the idea of the nearest neighbours (k-NNG) into DPC. By experiments on synthetic datasets, we show the power of the proposed algorithm. Experimental results show that our algorithm is feasible and effective.
Probing the menace of brain drain in Pakistan: does higher education matter

This paper investigates the determinants of brain drain in Pakistan. Brain drain is a burning issue of today in Pakistan, as it augments the major depletion of human capital stock in the country. To explore the determinants of brain drain, we use three types of brains drain: highly qualified migrants, highly skilled migrants and skilled migrants. The empirical results exhibit that GDP per employed labour is reducing the all types of brain drain in the long run, whereas higher education, globalisation, GDP per capita volatility and inflation volatility are increasing brain drain. Further, political rights are increasing the high skilled migrants only. This study suggests the policy makers in the government of the country to take concrete steps like the absorption of skilled and qualified labour at appropriate places with competitive wages and political rights should be ensured to stop the brain drain from Pakistan.
Application and comparison of the cokriging and the fractal model for identifying geochemical anomalies in Janja area, SE Iran

In this study, the cokriging model was used for determining the geochemical concentrations in Janja, SE Iran. Cokriging is a multivariate prediction method in spatial statistics which considers the spatial dependence as well as predicting the dependence between variables. In addition, fractal concentration-area (C-A) model was used for determining the geochemical anomalies in Janja region. Eight elements (Au, Cu, Mn, Zn, Fe, As, Mo and Pb) from 300 stream sediment samples were used to identify geochemical anomalies. The geochemical maps obtained from cokriging and fractal modelling showed that geochemical anomalies for copper and gold are located in the north, NW, and centre of the study area. Comparing the anomalies maps of the elements showed a correlation between elements of gold, copper, molybdenum, and iron in the region. Also, a negative correlation was determined between arsenic with gold and copper elements and there was not any correlation between the gold and copper elements of the region with manganese, lead, and zinc. For examining the results of the studies more closely, some of the data were analysed and evaluated using concentration-area (C-A) fractal model.
Design of a Rapid Development Model for Mobile Internet

A rapid development model for mobile internet was presented and a rapid development software system was built according to analysis of procedure and features of mobile internet development. There is no need writing a single line of code to rapidly building various management system which greatly shorten development procedure. The applications developed with the suggested model and software are dynamic and adaptive which can consist of multiple subsystems or modules. Moreover, developers can add or modify any business logic. The rapid development model and software can build and type of mobile internet application.
Atom-to-Photon State Mapping by Quantum Teleportation

Using a high-brightness narrowband source of 40Ca+-ion-resonant entangled photon pairs at 854 nm, we teleport a qubit from the D5/2 Zeeman sub-levels of the ion onto the polarization qubit of a single photon by heralded absorption.
Requirement analysis to design the knowledge-based system for long jump coaching

This paper presents the requirement for coaching the long jump of sports schools in Thailand. Moreover, a knowledge-based system (KBS) was designed for improving the long jump of athletes. Telephone and face-to-face interview approaches were used for collecting data from sports schools. There were 11 interview questions to investigate the requirement. MAXQDA 12 was employed to generate code and analyse data from interviewees. The results demonstrated that one of the most significant problems of coaching was the lacking of the system for analysis and suggestion. The framework, furthermore, was designed by experts using the knowledge engineering technique to create IF-THEN rules and an algorithm of all phases of the long jump biomechanics. In conclusion, there were many issues of requirements, for instance, all of the coaches required some systems for analysis and suggestion to improve the long jump skills. For the future works, the decision making algorithms of the system should be developed by using computer vision techniques.
Impact of organisational culture and people factors on knowledge management process: case study in an IT service company

Competition from corporate, peers as well as global, small and medium enterprise (SMEs) are major challenges to retain skilled manpower and customer base in information technology (IT) based service industry. Employee attrition has reduced operational efficiency and customer dissatisfaction in SMEs. Although, SMEs adopt different process to address these issues, they found knowledge management (KM) as the best option. The implementation of KM lies within organisation culture and people factors. The study aimed to examine the relationship of organisational culture and people factors with KM process. It was conducted in an IT service provider company that has implemented KM since five years. Descriptive analysis, correlational analysis and structural equation modelling-using SmartPLS were used to analyse data. The results emphasised that organisations' have to give importance to build culture fostering shared vision and collaboration. Moreover trust is needed among employees is needed to promote the activities of KM process.
Adaptive discrete cat swarm optimisation algorithm for the flexible job shop problem

Flexible job shop scheduling problem (FJSP) is a discrete combinatorial optimisation problem. By considering its characteristics, a kind of adaptive discrete cat swarm optimisation algorithm (ADCSO) is proposed with three objectives to be optimised simultaneously, i.e., makespan, maximal machine workload and total workload. Firstly, a two-phase encoding mechanism is proposed to represent each individual, and a heuristic-based strategy is employed to generate the initial population. Secondly, the discrete seeking mode and tracing mode are developed to make the algorithm directly work in a discrete domain. Thirdly, four adaptive adjustment approaches with different decline curves are used to balance the abilities of global search and local search. Furthermore, a local search strategy is embedded into the algorithm to improve the searching capacity. Finally, extensive experimental data are obtained based on the benchmark instances. The comparison results show that ADCSO is effective for the problem under study.
Dealing with the problem of collection and analysis of electronic evidence

Today, the internet is being used in everybody's daily life thanks to rapid development of science and technology including information and communications technology. The cyberspace has also been used as a den for cybercriminals, whose recent number remarkably increases getting things worse and worse. Cybercrime is technically distinguished from traditional one, which makes it very hard to investigate cybercrime. In particular, a number of technical and legal issues arising in the collection and analysis of digital evidence have great influence in cybercrime investigation. The paper explores general concept of electronic evidence, basic requirements for collecting and analysing digital evidence and general procedures and methodologies thereof.
Comparison on Energy Expenditure and Mean Heart Rate according to Exercise Performance Methods during Aerobic Exercise

The purpose of this study was to compare on energy expenditure and mean heart rate according to the exercise ways of self paced exercise, self perceived THR exercise and personal trainer helping THR exercise during bicycle aerobic exercise for 30 minutes. 10 male college students(23±1.0 year. 174±1.4 cm, 70±5.2 kg, 17.7±3.36 %BF) participated voluntarily on this experiment. They were to engage in a 30-min bout of bicycle aerobic exercise(5 min: warm-up, 20 min: main exercise, 5 min: cool down). All participants measured their heart rate reserved(HRR). And then figured target heart rate(THR) with their respective % 70 HRmax respectively. They performed with 3 exercise ways. There were the self paced exercise(SPE), the self perceived THR exercise(SPTE), and the personal trainer helping THR exercise(PTTE) methods in the same amount of time, repeatedly. Subjects were measured energy expenditure(EE) and mean heart rate(mHR) at the point of warm-up, main exercise, and cool down. We analyzed it using oneway repeated measures ANOVA with SPSS/PC ver. 24. Statistical significance was defined as p.05. In the main exercise section, EE and mHR in PTTE method was markedly higher than SPE and SPTE. It showed significant difference(p.001). Meanwhile, in the whole 30-min exercise, mHR in PTTE way was the highest. It demonstrated a statistically noticeable difference(p.001). On the basis of these results, we have to be assisted with personal trainer, when aerobic exercising. These findings indicate that the exercise using the optimal THR under the personal trainer is expected to be more efficient than SPEM and SPTE.
The Effects of Participation in a Core Exercise Program on Body Composition and Health-Related Physical Fitness in Female College Students

The purpose of this study was to examine the effects of a core exercise program for 12 weeks on body composition and health-related physical fitness in female collage students. The subject were 20 female college students without diseases or history of disease attending C University located in Gwangju Metropolitan City. They were divided into the exercise group(n=10) and the control group(n=10). Body composition and health-related physical fitness factors were examined before and after exercise. The following conclusions were drawn. For body composition, body-free mass and weight showed a significant difference at the level of p.001 and abdominal fat and BMI showed a significant difference at the level of p.05. For health-related physical factors, strength and flexibility showed a significant difference at the level of p.05 and endurance and cardiovascular endurance showed a significant difference at the level of p.001. In conclusion, a core exercise program had a positive effect on body composition and health-related physical fitness in female college students. Therefore, an easy and accessible program which is maximized in real life will be helpful to change quality of life and enjoy confident and healthy life in female college students.
Analisis Faktor-Faktor yang Mempengaruhi Prestasi Mahasiswa Departemen Statistika IPB menggunakan Metode SEM-PLS

Education is improving human power. Education is divided into three level namely primary, secondary and high level education. High level education can be obtained from University. University as one of high level education, is a formal education place for all studying and teaching activities, research, community service and develop scientific student to become qualified workforce. Student achievement in universities is influenced by various factors that cannot be measured both direct and indirect. The method that is used to determine those factors is structural equation modeling (PPS) with partial least square (PLS) method. PPS with PLS method used when there are some assumptions on diverse PPS which is not fullfiled, like binormal distribution and the big ammount of examples. The results showed that the six exogenous latent variables (family background, motivation, environment, visual learning style, auditory learning style, and kinesthetic learning style) did not have a significant influence on endogenous variables (student achievement). The model used in this study has a R2 value 14.1%. This values ​​indicates that the model built is still weak in explaining the diversity of student achievement.
Medical device surveillance with electronic health records

Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real world evidence to assess device safety and track device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.7- 53.0% over rule-based methods, and detected over 6 times as many complication events compared to using structured data alone. Using these events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance.
Smart MM

Rapid advancements have been made in the field of artificial intelligence in recent years. This has resulted in its adoption in various technologies from medicine to search engines. Existing media management systems have however not yet fully leveraged the power of artificial intelligence (AI) to give users enhanced information apart from basic media metadata. This chapter proposes a smart movie management system which works majorly offline and uses AI to deliver optimum information to the users on four vital tasks. These tasks are multilevel phrase level review polarity, plot and review keywords, a content-based recommendation system, and an emotion recognition system. The complete system works in near-real time with a user-friendly presentation to maximize a user's information gain.
Stochastic gradient-based particle filtering method for ARX models with nonlinear communication output submodel

This paper develops a stochastic gradient-based modified particle filter algorithm for an auto regressivee xogenous (ARX) model with nonlinear communication output submodel. The outputs of the ARX model are transmitted over a nonlinear communication network, while the outputs of the communication network are available. Based on the modified particle filter and the available outputs, the outputs of the ARX model can be computed, and then the unknown parameters can be estimated by the stochastic gradient algorithm. The simulation results demonstrate that the stochastic gradient-based particle filter algorithm is effective.
The Use of Two Stay Two Stray Strategy to Improve Students Writing Ability in Report Text

Two stay two stray strategy is a fun learning activity because the strategy makes students motivated in learning to write report text. This research uses classroom action research to solve some problems at class IX SMP Al-muhajirin somengko in learning to write English. The problems are less interested in writing English, lack of ideas, grammar, and lack vocabulary. Based on the problems the writer tried to conduct classroom action research by applying two stay two stray strategy. The purpose of this research are: (1) To describe the implementation of two stay two stray strategy in teaching writing for nine grade students of SMP Al-Muhajirin Sumengko. (2) To describe the improvement of writing ability of nine grade students of SMP Al-Muhajirin Sumengko after implementing two stay two stray strategy. (3) To describe the student's response to the implementation of two stay two stray strategy. This study was conducted in 1 cycle of two meetings through four stages: planning, action, observation, and reflection. Data collected through observation checklist, field notes, and questionnaires. The result from data analysis found that students' ability improved after post-activity assignment. The evident is from the results of tests showing the pre-activity assignment data is 13.6 with the category of students adequate to fair, after post activity assignment is 15.7 with category student ability good to adequate. It can be concluded that two stay two stray strategy is successfully implemented to improve the ability to write students.
Establishment of a Zone for Training Top Innovative Talents

The model of undergraduate education is changing from a “teaching paradigm” to a “learning paradigm”. The establishment of special zones for training innovative talents has become an important measure to cope with educational reform. The training of innovative talents by break through from professional education to students-centered education model.