Reviewerly/exHarmony huggingface dataset:
Photovoltaic Power Generation Prediction Using Data Clustering and Parameter Optimization

With the rapid development of the photovoltaic industry, photovoltaic power forecasting has become an urgent problem to be solved. In this paper, a method for predicting photovoltaic power based on data clustering and parameter optimization is proposed. The proposed method can be implemented as follows: firstly, the meteorological feature to be collected is determined by analyzing the physical model of the photovoltaic cell and the collected numerical weather information is divided into a set of categories by K-means. Then, the BP neural network is adopted and trained for individual categories, and an adaptive parameter optimization method is proposed to prevent model from local optimum. In the end, the proposed method is compared with other models to verify its effectiveness.
Machine Learning Techniques for Building and Evaluation of Routability-driven Macro Placement

Routability evaluation in macro placement is a very important issue in the physical synthesis of VLSI design. In this paper, we propose to use two different machine learning methods simultaneously in the evaluation process, with the goal of exploring potential connections to improve the prediction accuracy of machine learning models. The method is integrated into the physical design flow and predict routability effectively during the placement stage to avoid infeasible design in the detailed routing stage. Experiment results show that our evaluation is accurate and effective.
Broad Learning for Robotic Material Recognition

Broad learning was proposed as a flat network based on a random vector functional link neural network. The horizontal expansion of the network is realized by the increment of the feature nodes and enhancement nodes, which shows a good learning ability. In addition, fast incremental learning algorithms are implemented in horizontal expansion without the complete retraining of the entire network when structure is not enough to model the system. However, due to the design of its single layer neural network and the linear mapping to form feature nodes, it is difficult to extract high-level abstract features if inputs are more complicated. To break through this limitation, two algorithms C-CFBRL and C-LCFBRL are proposed based on CFBRL and LCFBRL, combining with convolution and pooling to automatically extract the key features of the image. The related experiments are conducted on the datasets which include RGB images, Depth images, and contact microphone signals. It is proved that the proposed BRL models and algorithms are very effective in classification. It is also shown that broad learning can be easily extended to other networks to solve related problems in robotics.
An Improved Mask R-CNN with Global Context Modeling for Instance Segmentation

Mask R-CNN is a recently proposed state-of-the-art algorithm for object instance segmentation. However, it is still a challenge to improve the accuracy of Mask R-CNN. In this paper, a novel Mask R-CNN approach is proposed. Firstly, we combine global context modeling with Mask R-CNN by capturing long-range dependencies. Secondly, we extend the Mask R-CNN approach by fusing a new simplified non-local neural network and Squeeze-and-Excitation block, which helps to achieve long-range dependencies and adaptively recalibrate the channel feature response by modeling the interdependence between channels. Numerical comparisons between our improved method and original Mask R-CNN solvers, on COCO 2017 dataset, are presented to demonstrate that our algorithm improves bounding box AP by 1.3% and mask AP by 1.6%
Assessing the Effectiveness of Topic Modeling Algorithms in Discovering Generic Label with Description

Analyzing short text or documents using topic modeling becomes a popular solutions for the increasing number of documents produced in everyday life. For handling the large amount of documents, many topic modeling algorithms are used e.g. LDA, LSI, pLSI, NMF. In this study, we have used LDA, LSI, NMF and also lexical database wordNet synset for candidate labels in our topics labeling. And finally compare the effectiveness of topic modeling algorithms for short documents. Among those LDA gives the better result in terms of WUP similarity. This study will help to select the proper algorithm for labeling topics and can easily identify the meaning of topics.
Using a Multi-Level and Multi-Resolution Visual Analytics Software to Understand the Aftermath of a Catastrophe

We participated in the 2019 IEEE VAST Challenge MC3 with the help of Papyrus, a Visual Analytics software for the exploration of text collections. Papyrus extracts terms associations with a bi-clustering approach and organizes them in a term hierarchy. Its visualization helps analyst explore social media messages posted by citizen to anticipate emergency response after a disaster event.
Problem Event Extraction to Develop Causal Loop Representation from Texts

This research aims to extract consequent problem events as a cause-effect concept pair series, from teen-drug addiction web-boards. The extracted consequent problem events benefit for a problem analysis in a solving system through a Causal Loop representation. The research has three problems; how to determine a causative/effect event concept based on a verb phrase expression with an overlap problem between causative-verb concept set and effect-verb concept set, how to determine cause-effect concept pair series from several verb phrases, and how to develop a Causal Loop representation from the extracted cause-effect concept pair series. Therefore, we apply an event rate to solve the overlap problem. We then propose using N-WordCo to determine the cause-effect concept pair series and also use a similarity score to develop the Causal Loop representation. The research results provide a high precision of the problem event extraction from the documents.
Sentiment Analysis in the Mobile Application Review Document Using the Improved K-Nearest Neighbor Method

The large number of smartphone users makes Indonesia a very potential market for business, especially mobile application developers. Each application store allows application users to provide a review of the applications used. From the existing application store, none has the sentiment analysis feature to filter or categorize positive reviews and negative reviews. Sentiment analysis is computational research of opinions, sentiments, and emotions that are expressed textually. The review document will go through several stages starting from preprocessing, calculation of term weighting to the calculation of cosine similarity (degree of similarity) to the training data used. The next process is sorting the level of similarity, determining the new k-values to produce a category for the document. The testing process of Sentiment Analysis in the Mobile Application Review Document Using the Improved K-Nearest Neighbor Method resulted in the best average accuracy of 88,76%. The best accuracy from all scenario test is 90,67 % and optimal k value is 15. Where the number of documents, comparison or balance of the proportion of training data and the determination of the value of k-values used influence the good or not classification process of the document in the form of an application review.
AECC: An Enhanced Public Key Cryptosystem for User Defined Messages

Due to enormous growth of devices and its interconnected communication, there are lot of possibilities for attacks and vulnerabilities in the network. From recent studies, we come to know about various security related techniques and their usage. A lot of cryptographic algorithms are used efficiently and effectively in plenty of applications. Thus it is notable to classify the attacks as two types Active and Passive Attack. Unauthorized access to the information implies passive attack. For example, in the communication channel doing operations like intercepting and eavesdropping are considered as passive attack. If the attacker tries to access the information and modify the information in an unauthorized manner, then it is considered as active attack. Man in the Middle attack is the best example of active attack. Here the attacker sends its public key as the hosts public and tries to hack the message. To overcome the issues in tradional authentication model, we proposed an Advanced Elliptical Curve Cryptographic (AECC) Algorithm which uses a code bit with a random function in both the sender and receiver side for encoding and decoding functions. This code bit is used to identify whether the public key belongs to attacker or the sender. By identifying the sender, the receiver can communicate with it without any restrictions. The results analysis section shows that our proposed AECC algorithm enhances security for user defined input messages like text, image, video and also for binary data.
Experience Sharing Between Cooperative Reinforcement Learning Agents

The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.
Mobile robot path planning in complex environment

Aiming at the problem of task path planning for mobile robot in complex multi-obstacle environment, this paper presents a path planning method that can solve time constraints. Considering the kinematics constraints of the robot in the case of constant speed movement, multiple route points are selected in the complex obstacle environment. By setting the vector direction of the robot at the route points, the smooth transition between different sections can be achieved, and the speed and kinematics constraints of the robot can be guaranteed at the same time. Based on the bi-arc, the path planning scheme of the robot is given, and the analytical expressions of path parameters and the range of path length under different conditions are derived. The numerical simulation results show the effectiveness and practicability of the path planning scheme in complex environment.
A Reading Assistant System for Blind People Based on Hand Gesture Recognition

A reading assistant system for blind people based on hand gesture recognition is proposed in this paper. This system consists of seven modules: camera input module, page adjustment module, page information retrieval module, hand pose estimation module, hand gesture recognition module, media controller and audio output device. In the page adjustment module, Hough line detection and local OCR (Optical Character Recognition) are used to rectify text orientation. In the hand gesture recognition module, we propose three practical methods: geometry model, heatmap model and keypoint model. Geometry model recognizes different gestures by geometrical characteristics of hand. Heatmap model which is based on image classification algorithm uses CNN (Convolutional Neural Network) to classify various hand gestures. To simplify the networks in heatmap model, we extract 21 keypoints from a hand heatmap and make them a dataset of points coordinates for training classifier. These three methods can get good results of gesture recognition. By recognizing gestures, our designed system can realize perfect reading assistant function.
Promoting Common Understanding on Research Data Management using Rubric

Research data management (RDM) is the activity to describe and practice what kind of data is to be used/obtained/generated, and how that data is analyzed/saved/shared or published from the plan to completion of the research project. The RDM consists ambiguous concept so that the understanding of RDM is diverse according to each researcher. In order to foster a common recognition of RDM in a Japanese university, a workshop that researchers utilize a rubric to evaluate and review their attitudes to RDM was held. In this paper, we report the process of the development of RDM rubric and the workshop.
Hand Talk System for Deaf and Dumb Person

This project is a prototype of data glove which has ability to convert the movement of figure into visual display and audible sound of predefined language. I have seen many people who find it difficult to communicate with other due to language problem or due to problem in vocal chord. In a diverse country like INDIA where various types of mother tongues is spoken. People form one region of country when travelled to other region find it difficult to convey their message, which make it tedious to survive them in other region. So this data glove can convert the speaker language into the listener language which make it possible to easy communication between different language speaker. There is approximately 70 million people in world who are deaf and dumb. These people use sign language to communicate with each other. But the person with no disability in vocal chord does not learn sign language, this means that not everyone can understand sign language. So it will be a tedious task for deaf or dumb person to communicate with other person having no knowledge of sign language. This data glove can be used to replace code language which is used during war. A particular command can be converted into a gesture and the commanding officer have to make particular gesture using his hand and the command is displayed on the command receiving personnel. Data glove consists of flex sensor. In this prototype I have used 3 flex sensor which produce 8 output result. You can increase no of flex sensor up to 5 on one glove which can produce 32 output. The output produce by combination of flex sensor on data glove is in analog form which is feeded to ADC channel of Arduino microcontroller. Analog to digital converter of microcontroller convert the analog input into digital form. This digital pattern is compared with stored data and then according to comparing result a particular sentence or word is displayed on a 16 * 2 LCD and same word or sentence is played on an 8 O speaker. Speaker and LCD output is for dumb people communication while only LCD is only sufficient for deaf person. This model consists of SD card which make it possible for storing as much audio as required means there will be no limitation of memory.
Analysis of rainfall depth based on climatology conditions using artificial neural networks

Abstract The quantity of rain that falls on the earth cannot be known with certainty. Floods and droughts due to a small quantity of rainfall are frequent events in some areas of Indonesia. The depth of rainfall at a certain time can be anticipated with accurate information. Along with rapid advances in technology, the forecasting of patterns of rainfall can be performed by artificial intelligence models, using historical data for the climatological parameters. The aim of this study is to predict rainfall depth based on climatology data. There are three categories of data that were obtained using NeuroSolutions for Excel: monthly, daily and hourly data. The input data are temperature, pressure, duration of sunshine, and humidity. The output data is rainfall depth. Based on the results of running calculations on monthly, daily, and hourly data, it was indicated that monthly, daily, and hourly data have relative errors of 11.49%, 8.49%, and 19.32% respectively.
Hammerstein Spline Adaptive Filtering based on Normalised Least Mean Square Algorithm

This paper proposes a normalised least mean square algorithm based on Hammerstein spline adaptive filtering. A nonlinear Hammerstein adaptive filters consists of memory-less function modified during learning and the spline control point is automatically controlled by gradient-based method. Simulation results demonstrate that the proposed algorithm exhibits more robust performance compared with the conventional spline adaptive filtering algorithms.
Real-Time Video Adaptation in Virtualised 5G Networks

Video applications are expected to increasingly dominate the traffic of mobile networks in the 5G era, and thus a real-time adaptation of these high resource demanding network applications is crucial in optimising the overall 5G networks. In this manuscript, we leverage Virtual Network Function (VNF) techniques to implement a video adaptation service that automatically adapts the quality of video transmissions depending on the status of the network. Furthermore, Network Function Virtualisation (NFV) techniques are employed here to simplify, optimise and speed up the deployment process of the aforementioned video adapter service (vAdapter), and therefore, allowing its on-demand deployment in a flexible way. We design, implement and test the scheme in a realistic virtualised 5G testbed. Empirical results focus on the scalability evaluation and performance as well as demonstrates a significant bandwidth reduction without compromising the final user's video quality expectations.
A graph transformation based approach for multi-agent systems reorganization

During the execution of a multi-agent system, a variety of events originating from the external environment and/or the internal elements of that system can lead to a state of failure. To overcome this kind of undesirable situations, the multi-agent system has to reorganize its behavior and structur e in order to continue fulfilling its overall objectives. Several approaches are proposed in the literature that investigate the problem of reorganization at the design time. Unfortunately, we cannot detect all the events at design time, because several among them occur at run-time. In this paper, first, we propose an approach for reorganizing the multi-agent systems at run-time. Second, we propose to formulate the reorganization using graph transformation. In particular, we define a type graph to represent the multi-agent system organization and a set of rules that define the different actions that can be performed so that reorganizing the system. We evaluated our approach on a case study related to a Conference Management System which is implemented as a multi-agent system.
The Application of Deep Learning in Automated Essay Evaluation

The shift from Automated Essay Scoring (AES) to Automated Essay Evaluation (AEE) indicates that natural language processing (NLP) researchers respond positively to the request from language teaching field. Writers and teachers need more feedback about writing content and language use from AEE software beside a precise evaluative score. This requirement can be met by the neural network based deep learning technique. Deep learning has been applied in many NLP fields and great success has been made, such as machine translation, emotional analysis, question answering, and automatic summarization. Neural network based deep learning is suitable for AES research and development since AES requires mainly a precise score of writing quality. This can be accomplished with human accurately scored essays as input and scoring model as output with deep learning technology. However, AEE requires more than a score and deep learning can be used to select linguistically meaningful features for writing quality and apply in the AEE model construction. Related experiments already show the feasibility and further research is worth exploring.
Implementation of Linear Prediction Coefficients in G.729E Using VHDL for Man Mission Applications

Effective audio communication is essential in supporting space flight programs. Information exchange between space and ground links plays a vital role to ensure a successful space mission. However, the availability of bandwidth for data transmission is mission specific. A compression technique, which achieves data compression by removing data redundancy, is used to reduce transmission bandwidth required for huge data transfer. On comparing various Consultative Committees for Space Data Systems (CCSDS) audio compression standards, it has been inferred that the speech codec G.729E manages to achieve “toll” quality speech. The bit rate for this codec is as low as 8 Kbps. It delivers audio signals in small packets of only 10ms time duration. A compression ratio of 16:1 can be achieved using this codec. The algorithm used in G.729E is Conjugate-Structure Algebraic Code Excited Linear Prediction (CS-ACELP) for extracting speech-characterizing parameters. Implementation of this highly complex algorithm on an FPGA would reduce processing delays through parallel processing and pipelining. In this paper linear prediction coefficient in G.729E encoder is implemented on a space graded FPGA RTAX2000S.
Self-Emerging Data Infrastructures

Timed release of data is an important security primitive for various applications. Such timed data release can be supported using a self-emerging data infrastructure that protects the data until a prescribed data release time and automatically releases to the recipient at the release time. While straight-forward centralized approaches such as cloud storage services may provide a straight-forward solution to implement self-emerging data release, unfortunately, they are limited to a single point of trust and involves a single point of control. In this vision paper, we discuss and review new decentralized designs of self-emerging data release systems using large-scale peer-to-peer (P2P) networks as the underlying infrastructure. We analyze the design of decentralized self-emerging data release systems using two P2P network infrastructures, namely Distributed Hash Tables (DHTs) and blockchains. We demonstrate how self-emerging data release can be used to support gradual release of private data in a decentralized infrastructure. Finally, we present some promising directions of future research on security primitives and protocols for timed release of private data in decentralized environments.
SiGOAM: Web system for developing quality mobile learning objects

Abstract Mobile learning (m‐learning) refers to knowledge and abilities acquisition through the use of mobile technology anywhere at any time. Mobile learning objects (MLOs) are key elements for this teaching paradigm; several software tools have been proposed to support the construction of MLOs; however, they do not take into account pedagogical and technological aspects simultaneously during the construction cycle of MLOs. This paper presents the analysis, design, and development of a web system called SiGOAM, which supports specialized services to professors to generate quality MLOs. These services follow a formal MLO's construction cycle integrated into four phases: analysis, design, development, and product‐oriented assessment. As a result of this formal approach, the proposed system generates quality MLOs that are embedded in mobile devices and accessed through web browsers. We performed a usability assessment of our system SiGOAM based on a field study. The results show that professors assigned acceptable values for effectiveness, efficiency, and satisfaction.