Reviewerly/exHarmony huggingface dataset:
Getting it Right the Fourth Time: Goal-driven Behavior Using Vector Space Models

This paper presents a method for imbuing agents with high-level domain knowledge via short command phrases given by a human designer. These command phrases, or goals, are provided at design time and describe the behaviors a human deems necessary in order to succeed at the current task. At run time, each goal is converted into a vector representation and is used to guide the agent’s exploration in a text-based environment. At each time step, the agent selects behaviors which it believes are relevant to its current objective, thus reducing the search space and increasing obtained rewards. We show that task-specific goals improve performance in a majority of virtual worlds, and discuss the potential for more general applications.
Mixed Compensation Multidimensional Item Response Theory

Computerized Assisted Testing (CAT) has supported the development of numerous adaptive testing approaches. Such approach as Item Response Theory (IRT) estimates a student’s competency level by modeling a test as a function of the individual’s knowledge ability, and the parameters of the question (i.e. item). Multidimensional Item Response Theory (MIRT) extends IRT so that each item depends on multiple competency areas (i.e., knowledge dimensions). MIRT models consider two opposing types of relationship between knowledge dimensions: compensatory and noncompensatory. In a compensatory model, having a higher competency with one knowledge dimension compensates for having a lower competence in another dimension. Conversely, in a noncompensatory model all the knowledge dimensions are independent and do not compensate for each other. However, using only one type of relationship at a time restricts the use of MIRT in practice. In this work, we generalize MIRT to a mixed-compensation multidimensional item response theory (MCMIRT) model that incorporates both types of relationships. We also relax the MIRT assumption that each item must include every knowledge dimension. Thus, the MCMIRT can better represent real-world curricula. We show that our approach outperforms random item selection with synthetic data.
Video Processing Using Data Mining

Data mining is the extraction of useful information from complex data sets. The data can be complex in terms of its variety, volume and values. Video mining is becoming quite popular nowadays. Video is a combination of several types of data such as audio, video and text data. Mining video requires tremendous efforts. Starting from shooting the video, editing it and posting it in optimized form is itself a big process. And mining data from these videos involves a lot of processing. This paper provides review of various data mining techniques used in the mining of a video. In this analysis, we also describe some methods to improve the performance of video mining by using data mining techniques.
Fridrich Structure Implementation on Gray Scale Images in an Embedded FPGA Platform

These days, a countless number of digital images are made in many fields, such as military, medicine, education, and personal affairs. As digital images usually contain secret or private data, image encryption has grown into a hot issue in theoretical study and in its practical presentation. In this paper an image encryption algorithm based on Logistic map will be used. Contrasting most of the existing image encryption algorithms, the proposed algorithm gives the original image as an integer pixel, and uses Logistic map for confusion and Galois field LFSR for diffusion. The consumption of logistic map and Galois field LFSR makes the proposed algorithm achieves not only a desirable level of security, but also high effectiveness, so the algorithm is suitable for practical application. Apart from this, the algorithm accepts a "confusion-diffusion-confusion" structure to yield good confusion and diffusion things which are the goods of the operation of a secure cipher. Simulation results and security analysis advice that the proposed algorithm has many desirable things of a perfect cryptosystem, thus it is very suitable for practical applications. The security analyses performed are Histogram analysis, Correlation analysis, Entropy analysis, Encryption Quality analysis, Differential analysis, Error metric analysis.
A Method to Generate Soft Reference Data for Topic Identification

Text mining and topic identification models are becoming increasingly relevant to extract value from the huge amount of unstructured textual information that companies obtain from their users and clients nowadays. Soft approaches to these problems are also gaining relevance, as in some contexts it may be unrealistic to assume that any document has to be associated to a single topic without any further consideration of the involved uncertainties. However, there is an almost total lack of reference documents allowing a proper assessment of the performance of soft classifiers in such soft topic identification tasks. To address this lack, in this paper a method is proposed that generates topic identification reference documents with a soft but objective nature, and which proceeds by combining, in random but known proportions, phrases of existing documents dealing with different topics. We also provide a computational study illustrating the application of the proposed method on a well-known benchmark for topic identification, as well as showing the possibility of carrying out an informative evaluation of soft classifiers in the context of soft topic identification.
An Unified Secured Cloud System for the Education Sector of India

Cloud computing is based on years of research on various computing paradigms. It provides elasticity, which is useful in the situations of uneven ICT resources demands. As the world is moving towards digitalization, the education sector is expected to meet the pace. Acquiring and maintaining the ICT resources also necessitates a huge amount of cost. Education sector as a community can use cloud services on various levels. Though the cloud is very successfully running technology, it also shows some flaws in the area of security, privacy and trust. The research demonstrates a model in which major security areas are covered like authorization, authentication, identity management, access control, privacy, data encryption, and network security. The total idea revolves around the community cloud as university at the center and other associated colleges accessing the resources. This study uses OpenStack environment to create a complete cloud environment. The validation of the model is performed using some cases and some tools.
Advanced Training Courses as the Main Method of Forming Professional Competencies of Practicing Social Workers

This article considers one of the key problems of modern education of professional social work, namely the formation of professional competences of social workers through advanced training courses. The organization of advanced training courses should be built on the emphasis of formation of competences. It is necessary to consolidate in the consciousness of the social worker the holistic complex of competences, which he has not in practical activity due to the absence of specialized education, as well as insufficient knowledge received during the period of study in the university. This shortcoming can be made up through advanced training courses. For this purpose, it is important to organize courses in such a way that the necessary competencies become the determining condition for the formation of professionalism of social workers as subjects of practical activities. The formation of professional competences is connected with the mastering, consolidation, application of special knowledge, skills and abilities. The correct organization of advanced training courses stimulates practicing social workers to the active search, conscious overcoming the psychological difficulties that stand in the way of solving the problems of people who find themselves in difficult life situations, and formation of the competences necessary in their professional activities.
Developing a Mobile-Assisted Software Application to Observe University Students’ Vocabulary Growth Through Extensive Reading

Based on a consensus of what makes good training of extensive reading, this paper aims to demonstrate how a mobile-assisted extensive reading (MAER) tool including a recommendation service and an online assessment was designed to support a reading process of the printed articles in a setting where English is learned as a foreign language. It was intended that learners could still use a paper format to read the topics closely matched their interests and vocabulary levels. This paper also shows how this tool was implemented in a class as a case study. 35 English majors studying in a freshmen class were observed in a ten-week period to use the system. Up to 3 hours guided by MAER, they were able to read up to 24 articles on average. Moreover, their vocabulary recognition level was found to have a significant improvement according to a paired-samples t-test. The implications and suggestions for using or developing MAER are then offered in consideration of the findings.
Unconstrained Face Recognition Using Cell Phone Devices: Faces in the Wild

The ever growing field of face recognition is constantly expanding to tackle new and more challenging, problems as the advances in algorithms yield higher accuracy results. The most recent advances have opened up the possibility of conducting high accuracy face recognition on faces from completely uncontrolled sources, such as search engines, social-media, and other online sources. Conducting face recognition in this area is usually deemed as faces-in-the-wild, given the unbounded nature in which faces are collected. While performing face recognition on faces-in-the-wild datasets has many advantages, it can make it difficult to determine the limitations of the face recognition algorithm in terms of the scenarios in which the faces were collected. In this work, we will collect a simulated faces-in-the-wild dataset using four cell phones (common sources for faces-in-the-wild) in varying scenarios (distance, lighting, background, etc.) to fully demonstrate the capability of newly proposed deep learning based methods of face recognition. Furthermore, we will contrast this with previous, standard, methods of face recognition in the same scenarios to see how recent improvements in the filed have opened up new capabilities.
Body weight anthropometry expert system for disease diagnosis

Abstract Human body measurements or anthropometry are carried out, among others, to meet industrial and health needs. Products that will be used by humans need to use anthropometry to suit the people who use them. The use of anthropometry in health, among others, is used as an indicator of health as well as a person nutritional level. The measure that is widely used for the criteria for human health is weight. Based on body weight can be determined the level of health and the possibility of a disease suffered by a person. Knowledge of weight anthropometry is packaged in the form of an expert system so that it can be used for consultation by people who have health problems based on body weight. The methodology used in this design uses Expert System Development Life Cycle, as well as knowledge representation techniques use artificial intelligence production systems. The design results are dynamic software, which is able to overcome diseases based on body weight data. Disease is divided into two categories, namely diseases based on high body weight and low body weight. Expert System has been validated by an expert. The validation results describe the expert system can provide solutions that are in accordance with the knowledge provided. The resulting system can be run by lay people in the training process for 2 hours. The conclusions from this study are that expert systems are able to be applied to assist humans in introducing effective and efficient weight-based anthropometric diseases. The artificial intelligence production system provides a knowledge base, rule base, and control strategies for finding weight anthropometric solutions.
PENGEMBANGAN GAME EDUKASI PAKAIAN TRADISIONAL INDONESIA BERBASIS ANDROID

Culture in Indonesia has a lot of diversity. Indonesia which has many provinces also has many cultures. One of the cultures that exist in Indonesia is raditional clothing. Traditional clothing, a characteristic of adat and culture in each region and also used in each traditional ceremony. Along with the development of the times, the concern for the preservation of national culture, especially traditional clothing, was very minimal. For example, the lack of recognition and socialization of Indonesian culture in the community is one of the important factors why people's interest is lacking in traditonal clothing. This study aims to develop the development of an Android-based educational game on traditional Indonesian clothing. The method used in this study uses Research and Development methods. The results of this study were developed an educational game "Introduction to Traditional Clothing in Indonesia" on the Android platform that provides information to users about any traditional musical instruments in Indonesia. Educational games are expected to help in the introduction of existing cultures and can be used as a way to provide insight to children. From the results of testing carried out with the black box method, it can be concluded that this application can function well. And can provide users of Android smartphone games that are educational.Keywords: Educational Games, Traditional Clothing, Android Smartphones
Comparison of classification model and annotation method for Undiksha’s official documents

Abstract Shakuntala is a system that manages official documents and letters at UniversitasPendidikanGanesha. The system stores various documents in PDF format which are categorized by type of document. But Shakuntala can only receive scanned documents, and document categorization were done manually by the operator. Documents uploaded to Shakuntalaalso generally contain information about people who were manually tagged by the operator. This causes inefficiencies that should be carried out automatically by machine. This study aimed at finding the best classification model for determining document categories. In addition, this research also intent to figure out the best method for tagging the people listed on the document. The results of the study showed that the Decision Tree classification model was the best model with an accuracy of 83.06% compared to KNN and Naive Bayes. As for the annotation of the person’s name, the Levenshtein distance method with a similarity threshold of 95% obtained an accuracy of 68.20%.
Abnormal Wind Power Data Identification Based on the Improved FCM Algorithm and Considering the Influence of Wind Speed

In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the increasing installation capacity worldwide. However, due to factors such as unscheduled shutdown, load shedding and communication noise, a great variety and amount of abnormal data may be present in wind power data, which harms the economic and safe operation of wind turbines. In some cases, abnormal operating status even cannot be detected in time due to the disruption of abnormal data, causing serious accidents. Therefore, it is necessary to identify the abnormal wind power data from massive measurements and ensure the availability of accurate and effective data. In this paper, an abnormal wind power data identification strategy is proposed by the improved fuzzy c-means (FCM) algorithm and considering the influence of wind speed. Specifically, the feasible domain matrix is employed to identify abnormal data while the mean comparison method is utilized for data correction. The feasibility and effectiveness of the proposed abnormal wind power data identification and correction strategies in this study is tested by detailed simulations on the East-China Sea offshore wind power SCADA database.
Scene Grammars, Factor Graphs, and Belief Propagation

We describe a general framework for probabilistic modeling of complex scenes and for inference from ambiguous observations. The approach is motivated by applications in image analysis and is based on the use of priors defined by stochastic grammars. We define a class of grammars that capture relationships between the objects in a scene and provide important contextual cues for statistical inference. The distribution over scenes defined by a probabilistic scene grammar can be represented by a graphical model, and this construction can be used for efficient inference with loopy belief propagation. We show experimental results with two applications. One application involves the reconstruction of binary contour maps. Another application involves detecting and localizing faces in images. In both applications, the same framework leads to robust inference algorithms that can effectively combine local information to reason about a scene.
An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems

Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and exploitation processes of Gray Wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i.e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, 20 mathematic benchmark functions are used to evaluate and compare the proposed method. The simulation results show that the proposed method outperforms the bat algorithm and other metaheuristic algorithms in most implementations and has a high performance.
Identification of earthquake types based on seismogram data

Abstract Indonesia is one country that has many active volcanoes, and it can have a high potential for earthquakes due to volcanic activity. Central Java is an area that has an active volcano, the Mount Slamet. Various kinds of earthquake signals, earthquake strength, and frequency are recorded using a seismogram. The process of recognizing seismic signal patterns using short-wave transformations has a higher chance of success. Several types of earthquakes that have occurred on the Mount Slamet. There are three types of earthquake data tested, Shallow Volcanic Earthquakes (VB), Gust, and Tremors. This study aims to identify the type of earthquake vibration signals recorded on the seismograph. Earthquake image processing system consists of several parts. The image is normalized to get the image in the time domain. Then the image is processed with two processes to determine the characteristics of the earthquake. The Fast Fourier Transform process is used to determine the strength of earthquake signals based on the frequency. The quantization process is used to take samples of each data in the time domain. In this study, the method used for identification is pattern recognition and decision trees. The identification system can recognize signals that are approached using the Root Mean Square, Average Power Spectrum, and statistical features. The results of tests carried out obtain 100% accuracy of each method.
The Important Role of Organizational Justice as Predictor of HR Performance

This study aims to examine the effects: Organizational justice towards organizational commitment, OCB and employee performance.The survey was conducted on three companies with a sample of 90 employees.Data is collected through a questionnaire and then analyzed using Structural Equation Modeling.The results showed that: The influence of organizational justice on organizational commitment, OCB and employee performance is significant.So that it contributes both practical and theoretical to the need for company leaders to prioritize fair treatment in the reward system for employees in the process of improving their performance going forward.
Privacy Enabled Duplication Free Cloud Environment with Protective Crypto Services

The main objective of this cloud server maintenance system is to provide the highest level of protection for remote cloud server users with Authenticated Key Exchange Scheme[1] such as One-Time High Security Passwords using Message-Digest (MD5) protocols as well as the new algorithm Efficient Encryption-System-with-Attribute-Preventing-Norms (ECSAPN) technique. The new methodology demonstrates that the proposed system is efficient in terms of confidentiality, duplication-free maintenance and enabling easy-services for all of its consumers. The replication-free cloud service eliminates duplication of content, which indirectly limits server storage size. The duplication healing process allows data owners to upload the data to the server once, the same user attempts to move the same content again, the duplication identifier does not allow the user to progress further and to alerts the user. Data owner coordinates the server to reduce the size and increase the access speed of the application[1][2].
Upper–Level Task Ontology for Generic Ontology Verbalization

Semantic technologies have become very popular across contemporary computer scientists in resolving issues across a range of domains. When a semantic web-based knowledge model is represented to exemplify knowledge related to a particular domain, that is referred to as an ontology. Researchers have pinpointed, ontologies are domain rich conceptualizations. That`s why there are thousands of ontologies already developed and available in online repositories relevant to multiple domains such as medicine, law, management and etc. Even though, the biggest bottlenecks linked with reusability and knowledge dissemination of ontologies are complexity in understanding it's schema and necessity of writing complex SPARQL or SQWRL queries for knowledge retrieval, which is not feasible for everyone. Therefore, addressing that gap, this research focuses on designing an upper-level task ontology to govern the entire process of generic ontology verbalization in Natural language for both RDF and OWL formats.
Calculating the Similarity of Indonesian sentences using Latent Semantic Indexing based on KBBI

Calculating Semantic Similarity between sentences is a problem that is long discussed in the field of language processing. This paper presents two methodologies with semantic similarities using the traditional TF-IDF algorithm and LSI method of using TF-IDF with the term distribution of definitions. In this research, the data used comes from KBBI, in which each document is converted into tokens and gets definitions according to KBBI, then calculates the similarity of each definition. The accuracy value gained 75.9% for semantic similarity using the traditional TF-IDF and 80% for the LSI and TF-IDF methods using the term distribution weights.