Reviewerly/exHarmony huggingface dataset:
ANALISA METODE CLASSIFICATION-DECISSION TREE DAN ALGORITMA C.45 UNTUK MEMPREDIKSI PENYAKIT DIABETES DENGAN MENGGUNAKAN APLIKASI RAPID MINER

Diabetes disease is a degenerative disease that each year the presentation of its victims are always increasing. Ignorance of lay people to predict the likelihood of the disease either from a derivative or derivatives is still not a bit. These things affect the level of vigilance sufferers against things that can trigger diabetes getting worse. Classification of research aims to form model decision tree in order for handling derivative-based diabetes disease are increasingly easy to do. To generate new information then used calculation algorithm c. 45 and testing algorithms that use application rapid miner would further reinforce the decision. The research on testing using multiple attribute classification i.e. the attribute weight, gender, blood pressure, blood sugar levels, and a history of diabetes. All of these attributes will be used as reference in search results so that sufferers can predict whether diabetes is the diabetes disease suffered a derivative or derivatives not
Free Electron Qubits

We show that a single relativistic free electron can carry one or multiple qubits within its wave-function in Floquet-dressed basis and implement 1-qubit gates based on the electron interaction with designed optical pulses. © 2019 The Author(s)
Garbage Carrier Roboboat Based On Image Processing

This research explain about garbage carrier roboboat. the purpose of making this robot is to reduce the trash found on river surfaces. Indonesia is an archipelagic country, this country dominated by ocean as rives estuary, however many of the resources produced from the sea have been polluted due to dirty and full of garbage from the river. So with the creation of this robot, it is expected to minimize the pollution. The shape of this robot is a boat that can operate in rivers and waters that still allows this robot to operate. This robot uses a brushless motor to move on the surface of the water and electronic speed control to control the brusless motor speed with the PWM setting method, for the navigation system the HC5409 ultrasonic sensor is used to detect any obstacles that avoid collisions, then image processing techniques are applied to detect garbage on the surface of the water, The Matlab R2012a program is an application used to process this image processing technique. In sum, the working process of this boat robot is, first, the navigating robot looks for garbage objects by detecting it using image processing. If garbage is detected, the robot will maneuver and activate brushless motors with garbage manuever algorithms, instructions sent from the microcontroller to Arduino Mega 2560, the robot approaches garbage object and crawl it. There are two garbage objects determined, namely styrefoam and a blue bottle, this boat can execute advanced, right and left maneuvers with the percentage of success reaching 80-90%. The advantage of this boat is that garbage objects can be detected from various directions and operate semi-automatically .&#x0D; Keywords :MATLAB, Image processing ,Motor brushless, Arduino Mega 2560 , manuever .
High-resolution Silicon Microring based Architecture for Optical Matrix Multiplication

We characterize silicon photonic microring resonators specifically designed with low Quality Factors (Q) to operate with high linearity measured over a range of 1.44 volts suitable for microring-based high resolution optical matrix multiplier architectures. © 2019 The Author(s)
Learning to Communicate Efficiently with Group Division in Decentralized Multi-agent Cooperation

Recent advances in multi-agent reinforcement learning show that agents can spontaneously learn when and what to communicate with each other to support effective cooperation. However, the existing approaches assume a fully-connected network with unlimited bandwidth, which is impractical in many real-world scenarios. For instance, in many multi-robot applications, robots are connected only through an unstable wireless network with limited bandwidth. Therefore, we must enable the agents to learn communication strategy that takes the consumption of network resources into account. This paper proposes a group division-based attentional communication model (GDAC), which can divide agents into groups according to their attention in the learned communication strategy. According to the novel attention mechanism, agents can be dynamically grouped according to their task relevance, and the communication only takes places inside the same group. As a result, it avoids a fully-connected communication architecture and can significantly reduce the bandwidth consumption at runtime. This model has been successfully applied to the environmental exploration task with a group of agents. The results show that GDAC could effectively reduce the total amount of communication message and yield improved performance over the existing fully-connected communication architecture.
Information Use and Mobile Devices: A Study Amongst Library Users of Selected Medical Faculties in Sri Lanka

A preliminary study was carried out as an initiative to probe into the possibilities of incorporating mobile apps and social networks in medical library services. Major objective was to find out the trend and preference of medical faculty students in using mobile devices for accessing information. The cross-sectional survey design was used with mixed method. Self-administered questionnaire was distributed among students of eight medical faculties in Sri Lanka and 120 filled questionnaires were received from six medical faculties. Data were analyzed using descriptive statistics and factor analysis. The use of a mobile device was analyzed through factor analysis. Cronbach’s alpha of the particular statements is 0.864 which indicate the adequate internal consistency of the statements and Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett test value is 0.788 which indicate that the data is suitable for factor analysis. The factor analysis resulted in four-factor model, which explain 66.43% of variance in the data. The four factors are ‘educational use’, ‘entertainment use’, ‘basic communicational use’ and ‘free call use’. Since the factor - educational use explain highest percentage (39%) of variance in the data, it indicates that there is a potential to use mobile device for educational purposes. The results also have revealed that majority of the students (85.2%) retrieved medical information through web resources using mobile devices. They also collect information using personal communication especially from friends and lecturers (41.5%). Among the information sources which they use through the mobile devices, Google is at the first place (82.3%) and Wikipedia (80%) is in second place. Only 67.2% of them use the mobile device to read e-books. In the purpose of using mobile devices for educational purpose, highest usage was observed for retrieval of images (81.8%), information on drugs (76.6%) and clinical guidelines (72.3%). Slow Internet connectivity (77.9%), poor network or signal coverage (76.6%), and lack of Wi-Fi facilities in faculty settings (55.9%) are the major barriers to using of mobile devices. Despite the students are positive about mobile devices, they consider more about the authenticity of online information and lack of knowledge and awareness on access to medical information. Moreover, they were mostly inclined to Google and Wikipedia but were unaware of other online resources to access medical information. Therefore, adopting mobile devices in health/medical information literacy programmes and curricula are recommended with further research.
A Survey of Clustering Algorithms in Association Rules Mining

The main goal of cluster analysis is to classify elements into groupsbased on their similarity.Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition.In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.
On the development of cat swarm metaheuristic using distributed learning strategies and the applications

Purpose The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems. The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations. In addition, the models are tested for nonlinear systems with different noise conditions. In a nutshell, the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies. Design/methodology/approach Population-based evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) are implemented in a distributed form to address the system identification problem having distributed input data. Out of different distributed approaches mentioned in the literature, the study has considered incremental and diffusion strategies. Findings Performances of the proposed distributed learning-based algorithms are compared for different noise conditions. The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate, but incremental CSO is slightly superior to diffusion CSO. Originality/value This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems. Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.
Coordinated Multiagent Reinforcement Learning for Teams of Mobile Sensing Robots

A mobile sensing robot team (MSRT) is a typical application of multi-agent systems. This paper investigates multiagent reinforcement learning in the MSRT problem. A naive coordinated learning approach is first proposed that uses a coordination graph to model interaction relationships among robots. To further reduce the computation complexity in the context of continuously changing topology caused by robots' movement, we then propose an on-line transfer learning method that is capable of transferring the past interaction experience and learned knowledge to a new context in a dynamic environment. Simulations verify that the method can achieve reasonable team performance by properly balancing robots' local selfish interests and global team performance.
ANALISIS PENINGKATAN DAYA MINAT DAN MOTIVASI BELAJAR SISWA MENGGUNAKAN DELTA MULTIMEDIA EDUTAINMENT SEBAGAI MEDIA PENUNJANG PEMBELAJARAN

Professor Toshiko Kinosita stated that Indonesia's human resources are still very weak to support industrial and economic development. The reason is because the government has never put education as the most important priority. Education is not placed as the most important priority because the people of Indonesia, ranging from the layman to politicians and government officials, are only pursuing money pursuits to enrich themselves and never think long. The opinion of the Japanese Waseda University Professor is very interesting to study considering that the Indonesian government is now starting to look at education as a long-term investment, after all this time education has been neglected. One indicator is that the MPR has agreed to prioritize the education budget of at least 20% of the APBN or APBD. This step is an initial awareness of the importance of education as a long-term investment. This study measures the extent to which the correlation and impact of the use of Information Technology in the teaching and learning process on student achievement in SMA Negeri 8 Tangerang. This research also modeled a learning media with Multimedia method which we named Delta Multimedia Edutainment (DME) which is a local web-based learning media school. The target to be achieved is an increase in interest and student achievement in SMA Negeri 8 Tangerang. The results of this study are useful for schools in improving school performance and maintaining the quality of graduates.
The Impact of Instructional Behavior of Sport for All Instructors on Sport Participants’ Achievement Goal Orientation and Exercise Commitment

Purpose: The aim of the study is to investigate the constructional relationship among teaching behavior types of sport for all instructors, achievement goal orientation and exercise commitment of sport participants.BR Method: Three hundred sport for all athletes who took badminton lessons from sport for all instructors are selected as participants of the study. They were conducted a survey related to instructional behavior, achievement goal orientation, and exercise commitment. The collected survey data were analyzed by factor analysis and correlation analysis focusing on interrelation and cause-effect relation of variables’ sub-category factors.BR Results: According to the results, training and coaching behavior and authoritarian behavior of the instructional behavior type of sport for all instructors recognized by participants had a significant effect on the achievement goal orientation and exercise commitment of sport participants. Also, the achievement goal orientation of sport participants had a significant influence on their exercise commitment. However sport for all instructor’s instructional behaviors partially influenced on sub-categories of both achievement goal orientation and exercise commitment of participants based on statistical outcomes.BR Conclusion: Instructional behavior type of sport for all badminton instructors partially influences on the achievement goal orientation and exercise commitment of participants. Achievement goal orientation of participants also has a meaningful effectiveness on a part of exercise commitment.
Detection and Tracking of Vehicles Based on Colour Probability Density

Vehicle monitoring is a very important part in the intelligent transportation systems towards real-time monitoring of intersection traffic condition, the dynamic traffic incident detection and traffic parameter extraction. This paper proposes a vehicle tracking method based on mean shift. During the detection period, tracking objects of vehicles are constructed. The current vehicle position is predicted from the target area of former frame. In the candidate area of the target image, foreground area mask is adopted as a condition whether a pixel is selected; this makes the colour probability density to more accurately reflect the characteristics of the vehicle, and avoids the background region's influence on the mean shift iterations. Experiments show that this method can effectively detect the position of the vehicle, and provides an effective vehicle tracking method in the intelligent transportation system.
Unified dynamic texture segmentation system based on local and global spatiotemporal techniques

Dynamic texture (DT) is temporal extension of static texture. There are two broad types of dynamic textures: natural and manmade. The examples of dynamic textures are waving tree, sea water, fountain, traffic, moving crowd, etc. Dynamic texture segmentation is a technique used to separate the moving objects from stationary content in the video. Majority of the techniques give good results either for natural or for manmade dynamic textures. The proposed approach for DT Segmentation is combination of local spatiotemporal technique, i.e., local binary pattern-Weber local descriptor and global spatiotemporal technique, i.e., contourlet transform. The local spatiotemporal technique considers the appearance and motion of the object for segmentation. The technique is computationally less complex than optical flow and gives good results for manmade dynamic textures. The global spatiotemporal technique is based on Laplacian pyramid and directional filters. It gives good results for natural dynamic textures. The proposed technique discussed in this paper is unified approach for any type of dynamic texture. The regions of images commonly segmented by both the techniques are considered as the final segmented output. The proposed system works equally well on any kind of dynamic texture.
Learning from Others’ Mistakes: An Analysis of Cyber-security Incidents

Cyber security incidents can have dramatic economic, social and institutional impact.The task of providing an adequate cyber-security posture to companies and organisations is far far from trivial and need the collection of information about threats from a wide range of sources.One such a source is history in the form of datasets containing information about past cyber-security incidents including date, size, type of attacks, and industry sector.Unfortunately, there are few publicly available datasets of this kind that are of good quality.The paper reports our initial efforts in building a large datasets of cyber-security incidents that contains around 14,000 entries by merging a collection of four publicly available datasets of different size and provenance.We also perform an analysis of the combined dataset, discuss our findings, and discuss the limitations of the proposed approach.
Emerging, crystalizing, and changing psychological contracts over time: introducing the iPC-network model

This chapter introduces the individual psychological contract network (iPC-network) model as an alternative approach for studying psychological contracts. This model departs from the idea that a psychological contract forms a mental schema containing obligated inducements and contributions, which are exchanged for each other. This mental schema is captured by a dynamic network in which the nodes represent the inducements and contributions and the ties represent the exchanges. Building on dynamic systems theory, the author proposes that these networks evolve over time towards attractor states, at the level both of the network structure and of the nodes. He highlights how the iPC-network model integrates recent theoretical developments in the psychological contract literature and explains how it might advance scholars’ understanding of exchange relationships. This would allow for more precise predictions of psychological contract breach and fulfilment consequences and would explain how the content and process of the psychological contract continuously influence each other.
O5. Classification of Schizophrenia Using Machine Learning With Multimodal Markers

Schizophrenia risk is associated with genetic variation and with early life environmental factors. Moreover, cognitive abnormalities are key features of this disorder. Our aim was to use multimodal machine learning to assess the performance of an ensemble of genetic, environmental and cognitive variables on schizophrenia classification.
Selective Dynamic Compression

Dynamic compression methods continuously update the model of the underlying text file to be compressed according to the already processed part of the file, assuming that such a model accurately predicts the distribution in the remaining part. Since this premise is not necessarily true, we suggest to update the model only selectively. We give empirical evidence that this hardly affects the compression efficiency, while it obviously may save processing time and allow the use of the compression scheme for cryptographic applications.
Relation Discovery with Out-of-Relation Knowledge Base as Supervision

Yan Liang, Xin Liu, Jianwen Zhang, Yangqiu Song. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Towards Reducing Reversible Circuit Synthesis Time

Synthesizing reversible circuits is still an important issue in the area of low power consumption circuit design. Several algorithms have been proposed in the field in order to synthesize reversible circuits with minimum cost in terms of line number and quantum cost with sacrificing the objective of reducing the synthesis time. However, for large scale circuits to be synthesized, long synthesis time is required since some algorithms perform iterative synthesis during the optimization process. Binary Decision Diagram (BDD) is considered a step forward in this field due to the minimization of cost achieved. In this work, we are moving from the BDD node-based to subtree-based mapping in the process of reversible circuit synthesis. We propose considering a complete BDD subtree to be converted in one step into a cascade of reversible gates instead of single node mapping in order to minimize the synthesis time. All possible subtrees are saved in a two column lookup table of subtrees and their corresponding reversible gates. Theoretically, our proposed algorithm can be more than 2X faster the transitional single node-to-gate synthesis.
Framework to Integrate Post-quantum Key Exchanges into Internet Key Exchange Protocol Version 2 (IKEv2)

This document describes how to extend Internet Key Exchange ProtocolVersion 2 (IKEv2) so that the shared secret exchanged between peershas resistance against quantum computer attacks. The basic idea is toexchange one or more post-quantum key exchange payloads in conjunctionwith the existing (Elliptic Curve) Diffie-Hellman payload.
A reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.
Language Model Score Regularization for Speech Recognition

Inspired by the fact that back-off and interpolated smoothing algorithms have significant effect on statistical language modeling, this paper proposes a sentence-level Language model (LM) score regularization algorithm to improve the fault-tolerance of LMs for recognition errors. The proposed algorithm is applicable to both count-based LMs and neural network LMs. Instead of predicting the occurrence of a sequence of words under a fixed order Markov assumption, we use a composite model consisting of different order models with either n-gram or skip-gram features to estimate the probability of the sequence of words. In order to simplify implementations, we derive a connection between bidirectional neural networks and the proposed algorithm. Experiments were carried out on the Switchboard corpus. Results on N-best lists re-scoring show that the proposed algorithm achieves consistent word error rate reduction when it is applied to count-based LMs, Feedforward neural network (FNN) LMs, and Recurrent neural network (RNN) LMs.