Reviewerly/exHarmony huggingface dataset:
Hyper-Meta Reinforcement Learning with Sparse Reward

Despite their success, existing meta reinforcement learning methods still have difficulty in learning a meta policy effectively for RL problems with sparse reward. To this end, we develop a novel meta reinforcement learning framework, Hyper-Meta RL (HMRL), for sparse reward RL problems. It consists of meta state embedding, meta reward shaping and meta policy learning modules: The cross-environment meta state embedding module constructs a common meta state space to adapt to different environments; The meta state based environment-specific meta reward shaping effectively extends the original sparse reward trajectory by cross-environmental knowledge complementarity; As a consequence, the meta policy then achieves better generalization and efficiency with the shaped meta reward. Experiments with sparse reward show the superiority of HMRL on both transferability and policy learning efficiency.
Multiple Seed Node Based Content Transmission in WSN

Sensor nodes distribute data in their network region. This study proposes if there are cluster peripheral nodes and they act as a multimedia byte stream supplier then they can act as a router and can route stream from one cluster to another cluster. These nodes can serve as an inter-cluster routing node. Peripheral-node can circulate messages to another cluster node as well as their nearby nodes. They can communicate with respective cluster heads. The control messages are called as beacons. These beacons contain the peripheral area and hierarchy structure information tm the area scope exists. Thus, the region can be well defined for each cluster. The cluster has cluster region and cluster members are taken care by cluster peripheral nodes to communicate with rest of the nodes inside and outside the cluster as well as within the cluster. The nodes responsible for processing is called a cluster processor or processor’s head. These processor heads can communicate and supply payload to other cluster processor head using cluster peripheral level routing node without interrupting internal cluster nodes. This study proposes the payload handling in term of array or stream of the compressed image. These image data are circulated from a set of the processing node to another set of the processing node. Processing can be accomplished in parallel processing fashion.
Employing differential privacy for big data security

Big data frequently contains huge amounts of personal identifiable information and therefore the protection of user’s privacy becomes a challenge. Lots of researches had been carried out on securing big data, but still limited in efficient privacy management and data sensitivity. This paper designs a mechanism that employs Differential Privacy for protection of personal data, which enforces privacy and access restriction level. The Differential Privacy technique acts on the request by introducing a minimum distortion to the information provided by the database system. The mechanism, DP-Data was implemented with Python scripting and Java Programming languages, Mysql and VmWare on Apache Hadoop platform. To test the effectiveness of DP-Data, a medical dataset with 1,048,576 instances and 12 attributes was employed. It was evaluated based on its utility, scalability, accuracy, sensitivity, specificity and processing time. The results indicated accuracy of 95.80 %, sensitivity of 93.60 %, specificity of 98.00 % and 0.40 ms processing time with high utility and good scalability which shows that the time it takes to preserve a data of 5000 tuples or less are almost similar. From these results, the application of differential privacy in solving privacy issue proved a high level of efficiency. Hence, the deployment of a secure big data framework that is based on access restriction and preserved level of privacy poses a higher level of protection of user’s privacy in comparison with other techniques.Keywords: Big Data, Privacy Preservation, Differential Privacy
English Teaching using Mobile Serious Games with Geo-Location Features: A Systematic Literature Review

Serious games have been widely studied from the educational perspective due to their proven potential to increase the learners' motivation and engagement in different academic activities, including language acquisition. At the same time, the fast growth of mobile devices access and Internet connectivity have motivated the creation of serious games for mobile environments that encourage the learner to access the educational content without time or location restrictions. In this sense, mobile serious games that lead the learners to move to different locations to achieve the game objectives, are known as location-based serious games. On the other hand, Latin American governments have started to propose and implement new policies to overcome the problem of English acquisition in their teenagers and then expand their professional opportunities by improving their English skills. Considering these phenomena, a systematic literature review was conducted to find the gaming elements that help to create successful serious games with geo-location features for English teaching, Additionally, this review inquires about the different validation methods and the results obtained in several researches. The answers to the research questions show that some of those elements, particularly self-improvement and competition, with a proper combination with educational aspects, are presented in most of the games analyzed and may indeed increase the learner commitment.
Software architecture for enhancing interagency collaboration on intelligence and records management

At a time Nigeria is facing internal security challenges such as Boko Haram insurgency, kidnapping, armed banditry, ethno-religious crisis, etc, it became imperative for authorities to look for ways of strengthening the effectiveness of security agencies. Information Technology (IT) has the potential of improving service delivery as demonstrated in many sectors of the economies of both developed and developing countries. Critical to the utilization of hardware in organizations is well-engineered software built based on good architecture. This paper proposes software architecture or model for building software application that provides enabling environment for security agencies to work in synergy in the management of domestic intelligence information and records. The proposed architecture was implemented and the two-step authentication technique employed was effective in making the software secured and robust.Keywords: Software architecture, Inter-agency collaboration, Records management
Penerapan Augmented Reality Pada Kegiatan Laboratorium Berbasis Android

The laboratory has an important role in the world of education which is supported by special equipment to meet certain standards so that the laboratory is suitable for use by students who will do the practicum. To be able to reduce error rates and attract student interest in conducting activities in laboratories, a more interesting and interactive system is needed. With the presence of augmented reality technology then a laboratory activities guide application based on android can be applied according to standard operating procedure of preparation before entering the laboratory and basic network subject which is make Local Area Network or LAN cables look more interactive and interesting by using three-dimensional animation. The method used is the object recognition method where three-dimensional objects are used as marker. This study aims to determine the design of markers using the object recognition method and get the maximum and minimum position of the distance and angle of the camera to the marker and the best position for the smartphone camera can detect the marker in the application of augmented reality in laboratory activities in the form of 3D animation standard operating procedures entering laboratory rooms and basic network subject. It can be concluded in this study that the design of markers uses the Vuforia Object Scanner application to scan LAN tester objects which are then uploaded to the Vuforia website to become markers and the results obtained are the best test distance for the camera detecting the marker, 20 cm to 50 cm with the best angle of 0° to 30°.
Teacher’s Strategies in Teaching English Vocabulary forEleventh Grade Students

This research describes about the teacher’s strategies in teaching Englishvocabulary for eleventh grade at MAN 5 Barito Kuala. There were three problemsin this research: (1) What are the teacher’s strategies in teaching vocabulary at theeleventh grade students of MAN 5 Barito Kuala?, (2) Why does the teacher applythe strategy in teaching English vocabulary vocabulary at the eleventh gradestudents of MAN 5 Barito Kuala?, and (3) What are the teacher’s problems inteaching English vocabulary at the eleventh grade students of MAN 5 BaritoKuala?.The purposes of this research: (1) To describe the teacher’s strategies inteaching english vocabulary at the eleventh grade students of MAN 5 BaritoKuala, (2) To find the teacher’s reason in applying the strategy in teachingEnglish vocabulary at the eleventh grade students of MAN 5 Barito Kuala, and (3)To describe the teacher’s problems in teaching English vocabulary at the eleventhgrade students of MAN 5 Barito Kuala.The researcher used descriptive qualitative method. In this case, descriptivequalitative method was used to describe English teacher’s strategies in teachingEnglish vocabulary for eleventh grade at MAN 5 Barito Kuala. The subject of thisresearch was the teacher’s of English. The researcher used observation,documentation, and Interview for technique of collecting data. The researchfindings were presented and discussed to answer of the research problem.The researcher found the strategies used by the teacher were multiplemeaning, game and memorization. It was found that the students had a goodresponses to the teacher strategies in teaching vocabularies. They could enjoy theprocess in teaching and learning process. By using multiple meaning, game andmemorization strategies the students could remember the new words easier.
Blockchain Application Framework for Priority Metric Based Academic Record Repository

Blockchain as the name suggests can be virtually visualized as an endless chain of blocks. The complex algorithms and computational power required to process the transactions for inserting blocks into the chain laid to the basic structure. Blockchain functionally work on consent of all, removing the requirement of middle man authority. This paper proposed a Blockchain fabric where an academic track record can be managed for every Indian Citizen after getting verified through Aadhaar Number. The resultant information generated by the proposed fabric would be immutable, decentralized and on distributed ledger. This is beneficial for all stakeholders like, government recruitment portal, institutes, organizations, etc. All electronic academic records, summary, vitals, transcripts, certifications etc. are mapped (Aadhaar UID) and are kept on Blockchain fabric, which can be accessed anytime with no loss of data (fully secured). In the proposed framework, all Stakeholders can filter the data as per skill set in demand. Job information and opportunities can reach to beneficiary directly with minimum efforts.
Procesador dedicado para el tratamiento de datos en formato de punto flotante

The development of Hardware in the commercial sector is considerably low with respect to the development of Software worldwide, it is worth mentioning that Mexico does not have the appropriate impulse to implement new architectures dedicated to the resolution of specific problems in Hardware for the improvement of processes or analysis of data in an optimal way.Consequently, and in order to improve statistics, this article shows the development and implementation of a dedicated processor based on the RISC architecture that allows data processing in floating point format consuming a single clock cycle per instruction, since it counts with a dedicated Floating Point Unit (UPF) that allows the execution of operations between whole numbers and decimals following the IEEE-754 standard for processing.
Towards detecting credit card frauds using Hidden Markov Model

E-commerce systems have become increasingly popular due to the widespread of internet shopping and banking. Credit card is one of the mostly used forms of payment on e-commerce platforms. However, there has been a tremendous rise in fraudulent credit card transactions, resulting to huge financial losses. In this work, a Hidden Markov Model (HMM) is proposed to design a credit card fraud detection system. Each HMM specifies the likelihood of a transaction given its sequence of previous transactions. This model is driven by a combination of K-Means and Baum Welch algorithms. A clustering process, obtained by the K-Means algorithm groups each transaction based on users’ spending profiles, where each cluster is used for different hidden states of the model. Subsequently, the Baum Welch algorithm generates a trained set of observations and calculates the probability of acceptance, which is used to detect if a current transaction is fraudulent or legitimate. This approach was implemented using PHP and was tested with a simulated dataset. Four performance metrics were used on the model which includes a Fraud Detection Rate (FDR), False Alarm Rate (FAR), Accuracy (A) and Sensitivity (S). The experimental results gave a high level of FDR and a low level of FAR, indicating that the proposed Hidden Markov Model is an effective approach for detecting credit card frauds.Keywords: Credit card, Fraud, E-commerce, Hidden Markov Model, K-Means algorithm, Baum Welch algorithm
Ultrasonic section locating method based on Hough transform

Reconstructed 3D ultrasound imaging based on low- dimensional array transducer can not only visually display the measured object, but also accurately measure the size of the object. Furthermore, the cost is lower than other 3D ultrasound imaging methods, and the experience the measured object is also improved. Due to the uncertainty of the measured object, it is hard to position the 2D ultrasonic section and reconstruction the 3D image. This process has high requirement for the probe user, and increase the complexity of post-processing algorithm. In order to solve this problem, this paper proposes an ultrasonic section locating method based on Hough transform. By specifying and measuring a number of detection points inside the targeting object, the Hough transform is used to calculate the spherical coordinate points in the known radius. The probe position is obtained by an external sensor. By comparing the probe position with the coordinate points obtained by the Hough transform, the 2D slice can be quickly positioned and the 3D image reconstructed. This method can effectively overcome the effect of changes in the surface of the object.
Identification of Number of Shop Visitors Using Faster Regions Convolutional Neural Networks

The number of store visitors is essential data for the evaluation of a store; data collection become complicated if there are a large number of visitors. Object detection is one part of computer vision and digital image processing, which has the purpose of detecting a semantic object from a particular class. One approach is the Faster Region Convolutional Neural Networks (Faster R-CNN) method, which can be used to identify the number of shop visitors. This study used Faster R-CNN to calculate the number of store visitors. The process starts by retraining the Faster-RCNN model that has been trained before using the COCO dataset. Then it used the INRIA Person Dataset that has been labeled manually using Labelimg. After training and configuration modifications, the accuracy reaches 69% with real-time accuracy 63%.
Secret Key Encryption based Steganography on Audio Signal by using 3 Dimensional Graphs

In the present literary work of extensive mediums for transmission technologies, it has always been a difficult task to ensure the secrecy of the sensitive information that is transmitted over a secured transmission channel. Amongst various suitable and efficient techniques to secretly exchange information, Audio Steganography is considered very supporting by giving encouragement. LSB technique for Audio Steganography is very efficient that encapsulate the knowledge that is hidden information with an audio wave file by restoring LSBs of that audio wave. In this paper Signal to Noise Ratio and Spectrogram analyses method is proposed.
An Image Texture based approach in understanding and classifying Baby Sign Language

A sign language is a mode of communication in which the intent of the message or the message itself is conveyed through body postures or the movement of the parts of the body like head, eyebrows and cheeks etc. Every expression is distinct and has distinguishable parts of language, its grammatical content fully displayed through gestures. There are more than three hundred sign languages used in the world. The baby sign language is a method of communication between the mothers and their toddlers by means of gestures, clearly expressing their emotions and desires. In the present research work study of the existing literature has been carried out and then prepared a data set of still jpeg images for 60 odd baby signs, performed GLCM(Gray Level Co-Occurrence Matrix) based feature extraction, then performed classification of gestures using KNN and Random Forest based machine learning algorithms. A classification accuracy of 73% has been achieved on the dataset prepared.
Image encryption algorithm for torsional components of generator based on compound chaotic model

In order to solve the problem that the image encryption algorithm cannot eliminate the strong correlation between adjacent pixels in the image, with poor ability to resist attack and low efficiency, an Image encryption algorithm for torsional components of generators based on complex chaotic model is proposed. Extracting the RGB torsional vibration component of the image for discrete cosine transform transformation, it is then rotated and fused to complete the initial encryption of the image information. In order to further enhance the security of image information, 2-D discrete cosine transform and 2-D compressed sensing measurement are applied to the initial encrypted image information. The real number matrix of measurement is merged into the complex value matrix, and the adaptive random phase coding is applied. The image information complex chaotic encryption model is constructed according to the coding result, so as to eliminate the strong correlation between the adjacent pixels. Particle swarm optimization is used to co-ordinate and optimize the parameters of the compound chaotic encryption model to improve the encryption performance of the model. Experimental results show that the pixel has low correlation, high security and strong ability to resist attacks after encrypting with this algorithm.
POSSIBILITIES OF DIELECTRIC LOGGING APPLICABLE TO THE STUDY OF THE BAZHENOV FORMATION

The work is devoted to substantiating the lithological interpretation of dielectric logging data in the Bazhenov Formation intervals, based on the frequency dispersion effect of bazhenites. Mathematical modeling shows the high sensitivity of the relative amplitude-phase characteristics to the frequency-dependent electrophysical characteristics of carbonate-siliceous-clay deposits in a high-frequency range. The results of the study indicate the fundamental possibility of dielectric logging data numerical inversion to highlight the main lithological types of the Bazhenov Formation rocks.
RETRACTED ARTICLE: A Robust Face Emotion Recognition Approach through Optimized SIFT Features and Adaptive Deep Belief Neural Network

We, the Editor and Publisher of Journal of Applied Security Research, have retracted the following article:Yenumaladoddi Jayasimha & R. Venkata Siva Reddy, A Robust Face Emotion Recognition Approach through Optimized SIFT Features and Adaptive Deep Belief Neural Network, Journal of Applied Security Research, 2019, DOI: 10.1080/19361610.2019.1656471.This article contains extensive and continuous content similarity across all sections with the previously published article: Yenumaladoddi Jayasimha & R. Venkata Siva Reddy (2019) A robust face emotion recognition approach through optimized SIFT features and adaptive deep belief neural network, Intelligent Design Technologies, 13.3, 379–390. DOI: 10.3233/IDT-190022.The substantial similarity between the articles is not acknowledged.Following a request by the corresponding author to withdraw this article from Journal of Applied Security research, we discovered the previously published article. We contacted the corresponding author multiple times but have received no response. Therefore, we have determined that retracting this article is the best course of action.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted.”
Hubungan Antara Gaya Belajar dan Prestasi Belajar Siswa Kelas XI pada Mata Pelajaran PAI di SMAN 9 Manado

This study aims to determine the Relationship between Student Learning Stlye and Student Achievement  in Class XI in subject of Islamic Education at SMAN 9 Manado.In collecting data, the author uses a quantitative approach with 2 variables, variable X (Learning Stlye) and variable Y (Learning Achievement). This research uses nonparametric statistical data. Non-parametric methods are assessed based on rank, level, or ordinal preparation of the data obtained. The populastion in this study were students of class XI who were Muslim whose sample number was 60 people, 30 had been tested and 30 others were research samples. After testing the normality of the data using the chi square test. It was found that the variables X X2count = 346,41 and variable Y X2count= 123,8. Whereas for value X 2tables with dk=k-1= 7-1=6 obtained X 2tables 12,592 and dk= k-1= 6-1=5  obtained X 2tables 11.070 with error level  5% turns out  X2 count ? X2 tables. So it can be conculed that the variable X data is not normally distributer and the Y variable is not normally sidtributed. After the prerequisite test was found, the data was not normally distributed. So the researchers used the correlation test rho. Based on the result of the manual rho correlation test, the calculated value rscount= 0.991. Where the result of the calculated value is greater than the rtables atau 0,991 ? 0.364 then Ha is  accepted so that there is a significant relationship between learning stlye and learnong achievement.Based on the result of theabove research, the researchers suggest the foloowing: in this study, student learning achievement is only vewed from the factors of student learning stlye without reviewing from other factors. Therefore, for other researchers who want to do research on learning achievement can tracce from other factors
Integrating Antonyms in Fuzzy Inferential Systems via Anti-membership.

Numerous authors have proposed extending fuzzy inferential systems to include the antonyms in fuzzy rules. To date, however, those efforts require significant changes to the nature of a linguistic variable, directly implying substantial additional computation. We propose a new mechanism for incorporating antonyms into fuzzy rules, based on allowing negative-valued memberships along with two new union and intersection operations developed by Dick et al. We prove that these operations form a total ordering over [−1,1], and then show how they integrate antonyms into fuzzy rules seamlessly and require little additional computation.