Reviewerly/exHarmony huggingface dataset:
FANSUR SEBAGAI KOTA TUA ISLAM

Fansur is an ancient Islamic city in Nusantara (the Indonesian archipelago) which was very popular in the past. However, the specific location of the city is still being debated today. Generally, contemporary writers state that Fansur is located in Barus, one of the areas in Central Tapanuli, North Sumatra. This opinion has been challenged from several authoritative sources. To solve the above problems, this research was conducted through an archaeological approach and literature study. Based on archaeological evidence and written sources, Fansur City is located in Lhok Pancu or Lhok Lambaroneujid, one of the coastal areas to the west of the city of Banda Aceh, Aceh. This location fits perfectly with the character of the Fansur location mentioned in ancient records from the Middle East, China and Europe. Due to geological changes that have occurred frequently in the past in the Aceh region, most of the area has been lost and cannot be seen again. The remains of ancient ceramics, tombstones and ancient tomb complex (diway) are archaeological evidence to strengthen the argument that Fansur is an ancient Islamic port city located in the Aceh Besar region, not in Barus, Central Tapanuli.
English-Vietnamese machine translation model based on sequence to sequence algorithm

Machine translation is a task in natural language processing that uses computers to convert between different languages. This article introduces an original seq2seq model experiment on the English-Vietnamese data set. By adding the attention mechanism and comparing the results of the model, we find that the attention mechanism can greatly promote machine translation. Using seq2seq and attention mechanism models to achieve the basic functions of the machine model, and has outstanding performance in the experimental results. Using multi-bleu-perl to analyze, the results show that the attention mechanism shows good performance on Vietnamese machine translation tasks.
DEMOCRATIC VALUES AND DIGITAL SKILLS OF STUDENTS IN HIGHER EDUCATIONAL ESTABLISHMENTS OF POLAND AND UKRAINE

The subject of the paper is evaluating and comparing the activity of using digital technologies for the realisation of democratic values by students of Polish and Ukrainian higher educational establishments: rights to freedom of access to information; tolerant communication with other people; awareness of the values of solidarity, equality.The study was carried out at Maria Curie-Sklodowska University in Lublin (Poland), The Jan Kochanowski University in Kielce (Poland), Radom Academy of Economics (Poland), Taras Shevchenko National University of Kyiv (Ukraine), Poltava V.G. Korolenko National Pedagogical University (Ukraine). Based on the survey results, the quantitative and qualitative analysis of digital technologies used in the development of democratic ideals and social activity of youth are carried.The results obtained are typologically common for students of both Polish and Ukrainian higher educational establishments.The paper provides recommendations for teachers of higher educational establishments on the use of digital technologies to support the development of students’ democratic values in formal and non-formal learning and draws attention to the problem of forming digital critical competence of students.
Defeasible RDFS via rational closure

In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a notable approach. In recent years, RC has gained popularity in the context of Description Logics (DLs), the logic underpinning the standard semantic Web Ontology Language OWL 2, whose main ingredients are classes, the relationship among classes and roles, which are used to describe the properties of classes. In this work, we show instead how to integrate RC within the triple language RDFS (Resource Description Framework Schema), which together with OWL 2 is a major standard semantic web ontology language. To do so, we start from ρdf, a minimal, but significant RDFS fragment that covers the essential features of RDFS, and then extend it to ρdf⊥, allowing to state that two entities are incompatible/disjoint with each other. Eventually, we propose defeasible ρdf⊥ via a typical RC construction allowing to state default class/property inclusions. Furthermore, to overcome the main weaknesses of RC in our context, i.e., the “drowning problem” (viz. the “inheritance blocking problem”), we further extend our construction by leveraging Defeasible Inheritance Networks (DIN) defining a new non-monotonic inference relation that combines the advantages of both (RC and DIN). To the best of our knowledge this is the first time of such an attempt. In summary, the main features of our approach are: (i) the defeasible ρdf⊥ we propose here remains syntactically a triple language by extending it with new predicate symbols with specific semantics; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra non-monotonic capabilities; (iii) the defeasible entailment decision procedure is built on top of the ρdf⊥ entailment decision procedure, which in turn is an extension of the one for ρdf via some additional inference rules favouring a potential implementation; (iv) the computational complexity of deciding entailment in ρdf and ρdf⊥ are the same; and (v) defeasible entailment can be decided via a polynomial number of calls to an oracle deciding ground triple entailment in ρdf⊥ and, in particular, deciding defeasible entailment can be done in polynomial time.
Unordered Multisecret Sharing Based on Generalized Chinese Remainder Theorem

Multisecret sharing schemes have been widely used in the area of information security, such as cloud storage, group authentication, and secure parallel communications. One of the issues for these schemes is to share and recover multisecret from their shareholders. However, the existing works consider the recovery of multisecret only when the correspondences between the secrets and their shares are definite. In this paper, we propose a multisecret sharing scheme to share and recover two secrets among the participants based on the generalized Chinese Remainder Theorem (GCRT), where the multisecret and their shares are unordered. To overcome the leakage of information, we propose an improved scheme including the improved sharing phase and the recovery phase. The improved scheme has not only a more secure performance but also a lower computation complexity. The conditions for recovery failure and success are also explored.
Mining High-Utility Itemsets of Generalized Quantity with Pattern-Growth Structures

High-utility mining is an important issue in pattern discovery. Quantitative high-utility mining further discovers quantitative itemsets that contribute high utility to the database. In this paper, we address the issue of generalized quantity in mining quantitative high-utility itemsets. The relaxation on quantity counting enables novel and generalized findings on quantity combinations of itemsets. We propose an algorithm called QHIM to find out all of the high-utility itemsets of generalized quantity. QHIM maintains a pattern-growth-based structure to store generalized quantity information for the final discovery. The experimental results show that QHIM may efficiently discover these high-utility itemsets. A level-wise method is compared in the experiments. In average, QHIM outperforms the compared algorithm by four times faster for small data and is two orders of magnitude faster than the compared algorithm when the minimum threshold is very small.
Effect of Telenursing on the Self-eﬃcacy of Patients with Myocardial Infarction: A Quasi-experimental Study

Background and Objective: Despite medical advances, patients with Myocardial Infarction do not fully recover and require rehabilitation and other treatment measures as well. One way to empower these patients is to promote self-efficacy. Therefore, the present study aimed to determine the effect of Tele-nursing on the self-efficacy of patients with myocardial infarction. Methods: This quasi-experimental study was conducted on 40 patients with Myocardial Infarction. The participants selected using the simple random sampling method. The patients were divided into two intervention and control groups of 20 participants. Routine training was given to both groups before discharge. The demographic data questionnaire and Sullivan’s cardiac self-efficacy questionnaire were then provided to each patient. In the intervention group, in addition to routine training, the telephone follow-up intervention was performed by the researcher within one month (once a week with an average call duration of 10 minutes). After one month, the self-efficacy questionnaire was provided to each of the study units again. Finally, after determining the normal distribution, the data were analyzed by chi-square, independent t-test, and ANCOVA tests, using SPSS v.25 software. Results: There was no statistically significant difference between the two groups in terms of demographic variables and disease characteristics. The mean score of self-efficacy before and after the intervention in the control group was 22.90 ± 3.93 and 33.35 ± 8.36, respectively, and in the intervention group, was 25.60 ± 6.90 and 47.45 ± 5.60, respectively. There was a statistically significant difference between the two groups after the intervention (p &lt; 0.001). Conclusion: Telenursing can improve adherence to the treatment program and promote patients’ self-efficacy. Therefore, due to its reliability, availability, and low cost, this method can be used in patient care and follow-up.
Improving English Speaking Ability through Vlogging: an Innovative Breakthrough for Suburban Beginner Learners in English Community Muara Bungo

Speaking skill has a very important role in this era to help students to communicate and to show their understanding in English. This speaking ability can help students to achieve higher academic goals or improve their soft skills to face the challenges in globalization. English Language Study Program of STKIP Muhammadiyah Muara Bungo tries to help the students to solve their Speaking problem by establishing a community which called the English Community. This study aimed to improve students’ speaking abilityin Muara Bungo through Vlogging. This model was chosen because they liked to play gadgets and were more interested in doing many activities with their gadgets. This research was classroom action research. The population in this study were all students who joined as members of the English community. The sample in this study was taken by using total sampling. This research was conducted in three cycles. The results of the first cycle shown that students started speaking by learning ways and techniques of using Vlogging. In the second cycle, students began to be interested in using Vlogging to learn speaking and began practicing speaking through Vlogging. In the third cycle, students were able to practice speaking in a fun way through Vlogging. Based onthe results of interviews with students, they stated that speaking through vlogging was fun, they could become millennial humans that could speak English. By learning to use Vlogging they get two things, namely the ability to speak English and the ability to use Vlogging. In conclusion, Vlogging could improve students' ability in Speaking.
Identifying Reward Functions using Anchor Actions.

We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function. PQR does not assume that the reward solely depends on the state, instead it allows for a dependency on the choice of action. Moreover, PQR allows for stochastic state transitions. To accomplish this, we assume the existence of one anchor action whose reward is known, typically the action of doing nothing, yielding no reward. We present both estimators and algorithms for the PQR method. When the environment transition is known, we prove that the PQR reward estimator uniquely recovers the true reward. With unknown transitions, we bound the estimation error of PQR. Finally, the performance of PQR is demonstrated by synthetic and real-world datasets.
Randomized View Reconciliation in Permissionless Distributed Systems

In a sybil attack , an adversary creates many fake identities/nodes and have them join the system. Computational puzzles have long been investigated as a possible sybil defense: nodes that fail to solve the puzzle in time will no longer be accepted by other nodes. However, a malicious node can behave in such a way that it is accepted by some honest nodes but not other honest nodes. This results in different honest nodes having different views on which set of nodes constitute the system. Such view divergence , unfortunately, breaks the overarching assumption required by many existing security protocols. Partly spurred by the growing popularity of Bitcoin, researchers have recently formalized the above view divergence problem and proposed interesting solutions (which we call view reconciliation protocols ). All existing view reconciliation protocols so far have a similar $\Theta (N)$ time complexity, with $N$ being the number of honest nodes in the system. As this paper’s main contribution, we propose a novel view reconciliation protocol whose time complexity is only $\Theta ({\ln N}/{\ln \ln N})$ . To achieve such an exponential improvement, we aggressively exploit randomization. The hidden constant factor in the asymptotic complexity of our protocol, however, is considerably larger than in previous protocols. Concrete numerical comparisons show that our protocol is more suitable for large-scale systems, while existing protocols are better for smaller-scale systems.
Segmentation of White Blood Cell Areas from Colour Degraded Microscope Slide Images

Abstract Advances in computer technology have increased in most aspects of medical analysis, such as for diagnosing diseases, treating drugs, analysing organs, and others. Computers are often used as a tool for analysis of medical images. This paper describes an automatic segmentation process of white blood cell areas on given microscopic images. The input of the system is microscope slide images with various staining conditions. Some images contain colour degradations due to imperfect staining process, while some images are in excellent staining condition. The Hough Transform method is applied for the segmentation process of the white blood cells detection. The system classifies each detected white blood cell area in one of the three defined categories (Small, Medium, and Large). The segmentation accuracy of the proposed system was 40.58% for images with colour degradation, while 100% segmentation accuracy was achieved by the system for images with excellent staining condition.
Avoiding the most common vulnerability-management pitfalls

Vulnerability management (VM) can seem unmanageable at times. But the key to successful VM is working smarter rather than harder. If you approach VM intelligently and prioritise appropriately, you can keep the number of resulting tasks from spiralling out of control. Vulnerability management (VM) can seem unmanageable at times. But the key to successful VM is working smarter rather than harder. Often the devil is in the details as well as the larger processes. That's why it's good practice to step back and evaluate your vulnerability-management programme from end to end. If you approach VM intelligently and prioritise appropriately, you can keep the number of resulting tasks from spiralling out of control, argues Mieng Lim of Digital Defense.
Combining Influence Maps and Potential Fields for AI Pathfinding

This thesis explores the combination of influence maps and potential fields in two novel pathfinding algorithms, IM+PF and IM/PF, that allows AI agents to intelligently navigate an environment. The ...
Android Architecture Comparison: MVP vs. VIPER

Android application development has been of interest since the first Android smartphone was released. Applications are constantly getting more complex as well as smartphone hardware is getting bett ...
Block Model of Lithosphere Dynamics: New Calibration Method and Numerical Experiments

The article is devoted to the last modification of the spherical model describing lithosphere dynamics and seismicity; the emphasis is on different ways of taking into account random factors that influence the behaviour of block structure elements. The main numerical procedures are effectively parallelized. This provides the possibility to use real data (for example, earthquake catalogues) in the simulation process. The main objective of the presented research is to develop a feasible procedure for automatic tuning of model parameters to obtain the best approximation results concerning the global seismicity. A special quality criterion is introduced based on minimization of the weighted sum of discrepancies between model characteristics and real patterns such as the spatial distribution of epicentres of strongest events and the Gutenberg–Richter law on frequency-magnitude relation. To optimize the time-consuming process of the reduced exhaustive search by specified parameters, the additional parallelization of calculations is applied. The results of some series of numerical experiments are discussed.
Performance Evaluation of Mini Single Board Computer in Hadoop Big Data Cluster

Abstract Hadoop is a Java-based and open source software framework that serves to process big size data in a distributed manner. Hadoop uses a framework for applications and programming called MapReduce. Along with the development of Hadoop, big data research using Hadoop is running using a single board computer (SBC). SBC is a mini computer which has a specification similar to a conventional computer but has lower specifications and power usage. However, the use of storage used on a single board computer has constraints, namely low bandwidth and limited local storage, therefore NAS is used because it has large storage and bandwidth speeds is upto1 Gbps. It uses as storage that is carefully used on Hadoop. Based on testing by running Hadoop in a single node and multi nodes using Micro SD storage and NAS does have a significant difference. When running Hadoop on a single node the difference in data access speed generated by Micro SD and NAS has a difference of 2 seconds. On multi nodes (clusters) the ratio is 4 seconds. The difference in NAS bandwidth that is 2 times faster than Micro SD show a significant difference if it is run on Hadoop.
Evaluation of “Serverless” Application Programming Model : How and when to start Serverles

Serverless is a fascinating trend in modern software development which consists of pay-as-you-go, autoscaling services. Promised reduction in operational and development costs attracts not only sta ...
Evaluation Strategy of Teacher Professional Development in Applied Universities Based on BP Neural Network

The progress of education cannot be separated from the development of teachers, and high-quality education cannot be separated from high-quality teachers. To guide teachers to take the road of professional development is the trend and the inevitable direction of the reform and development of application-oriented universities in China. The performance appraisal system of university teachers is an important part of the professional development of higher education teachers and the main means of evaluating teachers’ teaching and scientific research achievements. The establishment of the performance appraisal system is conducive to the development of teachers’ specialization and the improvement of teaching and scientific research level BP neural network is used to study the performance assessment of teachers in application-oriented undergraduate universities in this paper, change the evaluation methods, and guide teachers to take the road of professional development.
Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers

The number of elderly people suffering from dementia, a senile disease, is increasing day by day due to the rapid aging of the population. As a result, social and economic costs are also gradually increasing. To prevent such monetary losses, a system that can operate at a low cost is needed to care for dementia patients. Therefore, this research proposes a sensor-based deviant behavior detection system that allows caregivers to easily manage dementia patients even if they are not in the same location as their dementia patients at a low cost. In this research, the autoencoder and the LSTM models were used together, because deviance behavior is difficult to obtain labeled data. The autoencoder model is a representative unsupervised learning model, which can be used to extract characteristics of data, and was used to learn characteristics of normal behavioral data. The LSTM model is used to determine the deviant behavior from output outlier data that exceeds the threshold in the autoencoder. As a result of the experiment, each model achieved more than 96% and more than 99% accuracy. This research is expected to help caregivers of dementia patients manage the elderly with dementia more inexpensively and efficiently.
The Advantages of Using Highly Stable Reference Oscillators in the Receiving Equipment of GNSS Signals

The advantages of navigation solution based on the signals from three GNSS satellites and using the frequency standard in receiving equipment are discussed. The possibility of obtaining navigation solution when using the extended Kalman filter algorithm without predicting the receiver clock and receiving only three GNSS signals for several tens of minutes is shown as possible only for a highly stable reference oscillator.
Team Emergent State Development, an Enigma No More: Investigating Psychological Safety Emergence

Psychological safety is an important team construct, popular among both scholars and practitioners due to its notable strengths in improving team dynamics and performance. While previous research has provided rich data highlighting the value of team psychological safety, a common call among team scholars is the need for longitudinal examinations of this team construct. By analyzing team psychological safety over time, we respond to this need and open up the black box regarding team emergent state development. In doing so, we broaden our insight regarding team dynamics and leverage state-of-the-art research methods to test our proposed model. Results suggest that team psychological safety climate develops over time through consensus emergence and that this process is triggered by early information sharing. We also find that gender diverse teams are more likely to engage in early information sharing, thus heightening psychological safety climate emergence. Implications for science and practice are discussed.
Performance analysis of a fuzzy disparity selector for stereo matching of image segments under radiometric variations

Stereo matching algorithms generate disparity maps, which contain the depth information of the environment, from two or more images of a scene taken from different viewpoints. The process of obtaining dense disparity maps is a problem which is still being actively researched. The presence of radiometric differences in the images only further complicates the stereo matching problem. In the present research work, the images are initially split into small patches of pixels, such that pixels in each patch have similar intensities. The authors attempt to study the effect of the parameters, namely, tuning parameter `?? and the number of segments, while the images are subjected to variations in exposure and illumination. The value `?? performs the function of a weight signifying the contribution of each data cost, when the two data costs are combined in a linear fashion. Lastly, the results of this methodology are compared with other methods that try to tackle the problem of stereo matching under radiometric variations.