Reviewerly/exHarmony huggingface dataset:
Estimation of Energy Production by Using Probabilistic Programming Methods in Solar Power Plants: The case of Gazi Technopark

In this study, energy production estimation was carried out by using probabilistic programming methods for Gazi Technopark Solar Power Plant (SPP). The measurements obtained from meteorological stations and energy analyzers established within the SPP were created by the real-time recorded data for a year. After analyzing the effect of the measurements on production and selecting the statistically significant ones, energy production was estimated. Firstly, maximum a posteriori (MAP) method was used and the energy production was estimated with 86% successful. Secondly, using a Bayesian approach, which fundamentally enhances subjective belief by the probability of the event, via incorporation of meteorological measurements, was updated the prediction of energy production. In this way, the success of estimation associated with a confidence interval was demonstrated using a probabilistic programming. The probabilistic estimation model, which was specified for the measurements obtained from Gazi Technopark SPP, can be applied for the estimation of energy production in other power plants as well and useful for planning of energy production.
Design of Time Attendance System in Coal Preparation Plant Based on Face Recognition

This paper puts forward the attendance system based on face recognition on the problems existing in the current coal preparation plant personnel attendance. Due to the special working environment of the coal preparation plant, this paper first preprocesses the image, including image filtering and image enhancement, to facilitate post-face recognition.
A Framework For Effective Watermarking Synchronous With Time And Frequency Domain For Resilience To Attacks

Digital watermarking emerges as a very effective tool that provides digital media with the advantages of authentication, integrity verification, ownership protection, and rights management over an unsecured widespread media sharing channel. At present, various watermarking based image security approaches are present. It is found that a lot of research attention is still needed to develop a more robust mechanism in digital watermarking technology to protect digital content from various attacks. The primary purpose of the proposed system is to introduce a cost-effective and robust watermarking scheme that can survive under various image processing attacks and protect the digital content embedded with it. Here, the formulation of the proposed watermarking technique is based on the concept of 2D frictional fryer transformation technique- (FrfT). The idea is to calculate the 2D FrfT of the cover image. In the transform domain, the watermark identifier is used to modify the coefficients in the transform module, taking into account the real and imaginary parts of the cover image. The proposed system also introduces an attack model designed with two commonly known image processing attacks to evaluate the strength of the proposed watermarking scheme. The simulation results demonstrate the effectiveness of the proposed system in terms of PSNR, correlation coefficient, and bit error rate.
A Model-Oriented Requirements Traceability Framework for Small and Medium Software Industries

The Capability Maturity Model Integration (CMMI) has been a proficient approach to judge the maturity level of any software development business. For all software companies, it is very essential to develop standardize software systems for the characteristics essential for that maturity stage. The revolution in the software industry has brought advancement from programming-oriented development to model-oriented approach. Presently Model-Driven Development is turning into a rising method for creating software solutions most effectively. At Managed, which is the second level of CMMI, we need to keep up the bi-directional trace of the meta-models for better administration of user demands. Keeping up a bi-directional trace in such systems is very difficult for engineers, particularly during the assessment and upgrading stages. In this research, we have foreseen a model-based trace model and assessed its presentation dependent on currently used methodologies. This procedure will give a premise to software designers and scientists to propose progressively productive strategies for trace management in model-based systems for organizations.
Development of Mobile Application for Word Reordering Problems: An Attempt Oriented to Learning Analytics

The authors have developed a mobile application for Word Reordering Problems (WRPs) which was designed to collect learners' study logs, such as finger trajectories, to calculate parameter values used in detecting hesitation of learners. Additionally, the authors also have developed a simple Learning Management System (LMS) to store learners' study logs and visualize various parameters or data charts generated by the logs. By comparing the study logs collected on the WRP mobile application with its web version, it is discussed whether or not the parameters provided by the mobile application could also be used to locate the hesitation correctly as previously researched using web application with supervised learning. Another objective of this research is to exploit the practical use of new data types provided by physical sensors embedded in mobile devices. A pilot study showed how the accelerometer values are influenced in the event of accidents such as a sudden stop of a train. By analyzing multiple new data types, we attempt to acquire more information in the process of solving WRPs on mobile devices.
Agent-Based Simulation for Function-oriented Satellite

As an essential part of AI, agent-based modeling depicts the top phenomenon through "bottom-up" dynamic interaction. This paper analyzes the detective function of satellite, explores and gives DSP&SBRIS satellite agents. Then using the system composing by agents simulates detective module of satellite agent. The outcome of which approved that agent based modeling is of great extensibility and agility.
Data Hiding Using Wavelet Transform

This paper proposes the best approach for data hiding algorithm using integer wavelet transform. The proposed method provides large data hiding rate and distortion less recovery of the hidden data in it. Integer wavelet is based on lifting scheme, which provide perfect reconstruction and lossless data compression. A secret key is used for encrypt the data and it is embedded into the middle bit plane of integer wavelet coefficients in high frequency sub bands. This is used for embedding more data and provide imperceptibility requirement and robustness. Modification in the histogram can be done for preventing overflow of the grayscale image used for information hiding. PSNR is used for measuring the quality of the stego images. The obtained result show that the proposed method results in high data embedding, at the same time provide high security issues than other wavelet data hiding. Some experiments are done on various images to corroborate the validity of the proposed algorithm.
Archimedes law simulation for physics instructional media

Abstract Students find physics difficult, because they have to contend with different representations such as experiments, formulas and calculations, graphs, and conceptual explanation at the same time. As far as we know that physics experiments which conducted in well-equipped laboratories, improve student’s science process skills. It is hard to imagine learning to do science, or learning about science, without doing laboratory or fieldwork. But many schools in various part of the world have very poor or no laboratories, so the teachers have to use their own equipment and replace the students experimental work with simple demonstration experiments. The purpose of this study was to create physics learning media for Archimedes law using simulation to cover the lack of the availability of laboratories and physics practicum tools. Rapid prototyping lifecycle model was used as a design of study. CSUQ is used to test usability of the simulation. To test the functional aspect, this study used black box testing the result of questionnaire was 88.4% with very decent criteria. Based on the development steps and test results, it can be concluded that developing a simulation requires full collaboration between IT experts and physics.
Research on Adaptive Fusion Tracking Algorithm for Bearings-Only Measurement

Target tracking is one of the key technologies in the military application of wireless sensor networks. This paper aims at the characteristics of wireless sensor network nodes, which have limited computing capacity but can conduct data fusion through wireless communication, to improve tracking accuracy from two aspects. On the one hand, an adaptive bias- compensated pseudo-measurement Kalman filter based on process noise is proposed, on the other hand, an adaptive weighted fusion algorithm based on innovation is proposed. Through multiple simulations, the proposed algorithms can significantly improve the tracking accuracy and robustness of the system on the premise of low computational complexity.
Body Part Level Attention Model For Skeleton-Based Action Recognition

Skeleton-based action recognition has catched more eyes recently. Many approaches model the human skeleton sequences spatio-temporal representation by Recurrent Neural Networks, while RNNs-based approaches don't explicitly have the attention scheme. In this paper, a body part level attention method is proposed for determining which parts are discriminative. On the basis of human joints natural connection, human body is divided to disjoint parts which then are fed into corresponding modules to generate action scores. A body part level attention module which distributes different importance to different action scores is proposed to adaptively learn informative body parts. The final representative of the action video is produced in a weighted average way and it will be fed into DNNs for classification. Out method outperforms other attention model and we obtain the state-of-the-art performance compared to other methods on two popularly used benchmark NTU dataset and UT Kinect dataset.
Research and Design of Continuing Education Teaching Resource Database Based on IPv6

With the promotion of educational information construction and the rapid development of the Internet, more and more resources are flooding the campus database. The disorder of structure and management difficulties have become a difficult problem for colleges and universities. The teaching resource management platform is to manage the teaching resources of colleges and universities. A good platform, but the existing teaching resources and resource management platforms are mostly in the IPv4 network environment. This paper analyzes the existing teaching resources of the Continuing Education Institute, studies and designs a well-structured teaching resource library, and develops a teaching resource management platform for students. The platform is based on the IPv6 network environment, strictly controls resource quality, improves resource transmission rate, and provides reference for other management platforms based on IPv6 network environment.
Game-based Learning and Augmented Reality Navigation APP for Children's Libraries

Digital technology has been introduced into libraries. Innovative services and quality information are required in libraries to meet the needs of digital natives. The study design of the Augmented Reality navigation APP is to bring children a novel technological experience. Through simulation and games, children's curiosity can be enhanced. They can be attracted to explore the library and motivated to learn more spontaneously. The Augmented Reality navigation APP creates an indoor positioning system by combining iBeacon with the APP system. The design of the visual navigation interface is based on the cognitive abilities of children aged 7-12. The Augmented Reality navigation APP is expected to stimulate children to have more patience and give them the opportunity to explore library entities and virtual resources. It also offers the possibility of letting children study and learn together with their parents. Finally, Augmented Reality should build more development in the field of education in the future.
Knowledge Graph Embedding by Bias Vectors

Knowledge graph completion can predict the possible relation between entities. Previous work such as TransE, TransR, TransPES and GTrans embed knowledge graph into vector space and treat relations between entities as translations. In most cases, the more complex the algorithm is, the better the result will be, but it is difficult to apply to large-scale knowledge graphs. Therefore, we propose TransB, an efficient model, in this paper. We avoid the complex matrix or vector multiplication operation. Meanwhile, we make the representation of entities not too simple, which can satisfy the operation in the case of non-one-to-one relation. We use link prediction to evaluate the performance of our model in the experiment. The experimental results show that our model is valid and has low time complexity.
A Multi-Layer Architecture for Cooperative Multi-Agent Systems

In multi-agent cooperative systems with valued based reinforcement learning, agents learn how to complete the task by an optimal policy learned through value-policy improvement iterations. But how to design a policy that avoids social dilemmas and come to a common consensus between agents is an important issue. A method that increases the success rate of cooperation by assessing the cooperative tendency is proposed in this article. The method learns the rules of cooperation by recording cooperation probabilities for agents in a Layered Cooperation Model (LCM). Probabilities are considered as a base while agents are making a decision beneficial to all through the game theory. The method is tested using two cooperative tasks. The results show that the proposed algorithm, which addresses the instability and ambiguity in the win-or-learning-fast and policy hill-climbing method (WoLF-PHC) and requires significantly less memory space than the Nash Bargaining Solution (NBS), is more stable and more efficient than other methods.
Need for Compliance With Information Security Policy In Universities: a Preliminary survey

Information security has become a significant problem for the use of information technology, especially for universities that have a lot of data. Information security policy is one solution to ensure data security. However, compliance is a classic problem in information security that face by an organization when applied policy. Effect of users who do not comply with information security policies both from internal and external. Research on this subject was surveyed to find out the leadership perspective on compliance with information security policies and to explore the university's need to evaluate user compliance. This research was conducted in a survey using a closed-ended questionnaire given to manager level of the university. The data processed with descriptive statistics. This research founded management realized the importance of compliance with information security policies to reduce information security incidents. Universities need appropriate model to evaluate information security policy compliance. This understanding will help improve predictions of the impact of complying with information security policies.
Construction of Student model based on BP neural network

With the development of personalized learning, the construction of student models is becoming more and more important. At present, there are still problems in the student model that the characteristics are single and the indicators of each dimension are not clear. In this paper, learners will be analyzed from the perspective of student characteristics. And BP (Back Propagation) neural network algorithm will be used to establish a personalized student model. This paper first constructs the feature system of the student model from six dimensions. Secondly, the initial data is obtained through questionnaire survey, and the data is initialized to obtain 30 feature vectors as input to BP neural network. The output of the network is a learner type, which is divided into 36 categories. The construction of the student model will have certain practical significance for realizing the effectiveness of personalized education in distance education.
Face Recognition: PCA vs. l1 Minimization

Automatic Face Recognition techniques face problem when it comes to recognizing face in different expressions, poses, inappropriate lightening, occlusions and disguise. We have used PCA and l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -minimization for the recognition process and casted these problems in the experimental result thus giving a comparison between the two methods. PCA being the first ever method used for the face recognition fails when there is a occlusion or disguise. Thus we have argued that the new method based on sparse representation calculated by l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -minimization solves the problem. We have first used PCA for dimensionality reduction and then used l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -minimization for better correlation and effective result. The postulates of sparse representation predicts how to choose the training images such that the result is efficient, handles errors due to the occlusion and disguise equivalently increasing the robustness of the framework.
Attention Based Encoding and Decoding for Convolutional Neural Networks

Deep learning architectures have been used widely of late for the purpose of image classification tasks. This paper presents an architecture using convolutional neural networks and multi-heads attention. The architecture of our model comprises of two encoders and one decoder. The second encoder and the decoder use multi heads attention mechanism. The proposed model takes individual channels in the image through 3 different encoders and learns from them. The model presented here performs comparatively better even with less number of convolution blocks and its performance significantly improves by adding more layers.
Offline Handwritten Uighur Word Recognition Based on Segmentation-driven and Two-level DTW

Offline handwritten Uighur scripts is cursive and have a large vocabulary, which makes the word recognition more complicated. In this paper, we propose a segmentation-driven recognition algorithm for offline handwritten Uighur word based on grapheme analysis and two-level DTW (dynamic time wrapping). Firstly, a MSAC (main segmentation and additional clustering) algorithm is adopted to over-segment a handwritten Uighur word into two grapheme sequences. After then, a hierarchical hybrid Uighur character classifier is designed to enhance the character recognition accuracy. Finally, a novel maximum likelihood algorithm with two-level DTW is presented to select the best hypothesis of character sequence from grapheme merging and decide the word class. Experiment results show that the proposed algorithm can achieve high character segmentation accuracy and word recognition rate simultaneously.
Method to support auxiliary voice over ongoing MCPTX group call for low priority participants

MCPTX (Mission Critical Push to X) is catering the need of group communication in different kind of emergency situations. Mission Critical (MC) operations in the civil domain comprise of activities handled by police officers, fire fighters, Search & Rescue teams, Medical Emergency support personnel, Civic Protection etc. There are low priority and high priority users. For Low priority user, it is always a problem to transmit the voice on priority under critical situation. In this paper, authors have proposed a method to support auxiliary voice from lower priority participants during MCPTX group call.
Automated System for Cervical Abnormality Detection using Machine Learning

Cervix is a cylinder-shaped neck of tissue that connects uterus and vagina. It is a part of the female reproductive system. Medical diagnosis is a process of detecting a problem with the help of symptoms. In this work, we have developed an automated system for diagnosis of various cervical abnormalities like cervical cancer, ascus, metaplasia, erosion, polyp, inflammation, candidiasis, and nabothian cysts. The system performs diagnosis using images captured from pre and post-VIA (Visual Inspection with acetic acid) and VILI (Visual Inspection with Lugol's Iodine) tests. We apply transfer learning and machine learning algorithms for effective abnormality detection. In this work, we have developed automated diagnostic systems for binary (normal versus abnormal) and multi (3) class classification of abnormalities based on images taken by doctors. We will further evaluate all the approaches and report the accuracy scores for them.
Accurate Object Detection of Electric Power Equipment Based on CapsNet Framework

The automatic monitoring of the electric power equipment through computer vision is the critical part of intelligence of the power industry. While the accurate object detection of electric power equipment is the premise of automatic inspection. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a detection algorithm that solve the multiple object detection of electric power equipment once with higher mean average precision (mAP). Our approach's key insight is: we apply CapsNet framework to classification of the regions proposal in order to localize and segment different objects. Instead of CNN, we use capsules with dynamic routing to classify the features extracted compare to RCNN. We find that this method outperforms the RCNN with samples not large and retain more direction relationships between the different parts of the objects.
Animal Stamp Rally Application using Image Recognition

Tokushima zoo is a municipal zoo housing a lot of animals for educational purpose. Thus, Tokushima city wants visitors to observe animals as many as possible whilst having fun. The animal stamp rally is a game that visitors collect a series of stamps at around animals which visitors have observed. Not to provide real stamps, tables and stamp cards, we develop an animal stamp rally application. Thanks to the Convolutional Neural Network (CNN), the application can recognize stamps from snapshots of real animals instead of real stamps or QR codes. Additionally, to improve animal recognition accuracy, the application uses GPS information. In this paper, we describes a progress of our development of the animal stamp rally application, and estimates its recognition accuracy.
A Case Study of Requirements Ambiguities and Goal-oriented Focused Requirements Specification

This paper conducted a case study in a simulation control software system. In the case study, we analyzed defects that required significant correction effort. The defects were ambiguity defects injected by misunderstandings and inconsistencies among stakeholders during interpreting requirements and specifying design documents. The ambiguities of the specifications are found in the definitions of distance, time (time zone), and calculation accuracy. These cause non-conformance in the implementations and errors in the control simulation execution results. Based on the analysis, as a low-effort defect prevention approach, we propose clearly defining the units to avoid such ambiguities and estimated the expected effort reduction by the definitions in the target control simulation software development.