Reviewerly/exHarmony huggingface dataset:
A steganography approach for hiding privacy in video surveillance systems

In today's environment, video surveillance systems have become widely used in almost all facilities, either public or private. At any given time, individuals can be observed and captured on video without their knowledge and/or permission. This can happen in public facilities such as train stations, stores, elevators, locker rooms, and even school hallways. This should raise some concerns about privacy. Therefore a fundamental challenge is designing surveillance systems used for security reasons that should protect the privacy of individuals. Most of the existing techniques focus on modifying the original scene either by adding noise and/or replacing objects using black boxes, blurring, or in-painting. Such techniques usually destroy the integrity of the original video, which may cause errors in the reidentification process when needed. For that reason, steganography techniques can be beneficial and convenient. In this chapter, we propose embedding the original video into the in-painted one. The proposed technique is carried out in the discrete cosine transform (DCT) domain of the cover video based on the H.264 video compression concept. Experimental results showed that the proposed method successfully introduced low distortion in the stego-video while maintaining an acceptable visual quality for the retrieved original frames.
Research on Real-Time Multi-object Detections Based on Template Matching

This paper had discussed how to identify a special part of a work-piece being processed on the products assemble line. The template matching algorithm based on geometric feathers, especially those algorithms based on gradient fields had been presented. How to use the algorithms to detect multi-objects had been treated in detail. A concrete example had demonstrated the practical effects of the algorithms.
Research on Information Security Evaluation Based on Artificial Neural Network

In order to improve the information security ability of the network information platform, the information security evaluation method is proposed based on artificial neural network. Based on the comprehensive analysis of the security events in the construction of the network information platform, the risk assessment model of the network information platform is constructed based on the artificial neural network theory. The weight calculation algorithm of artificial neural network and the minimum artificial neural network pruning algorithm are also given, which can realize the quantitative evaluation of network information security. The fuzzy neural network weighted control method is used to control the information security, and the non-recursive traversal method is adopted to realize the adaptive training of information security assessment process. The adaptive learning of the artificial neural network is carried out according to the conditions, and the ability of information encryption and transmission is improved. The information security assessment is realized. The simulation results show that the method is accurate and ensures the information security.
Chatbot Implementation with Semantic Technology for Drugs Information Searching System

Abstract This research aims to design medicine information based on the ontology scheme and to display the relationship between information through chatbot as search media. The diversity of information does not guarantee to gather a relevant search result with user’s needs. A conventional search engine would be substantially beneficial in information searching yet could not able to comprehend meaning of found information and its relationship with other information. However a machine could understand the relationship and the meaning of information through the implementation of semantic technology, which is ontology. Ontology is implemented to represent semantic metadata information. The information designed with the ontology model would be very assistive in searching for data which is relevant to users’ need. The result of implementing ontology as a knowledge base in this research able to display the information of a disease and its relationship with medicine used as the medication for such a particular disease. The evaluation of our system shows that 90% of queries from users generate a valid response from the chatbot. Therefore, this research could be used as a reference in search of medical information and describe its relationship with a particular disease or related to medicine.
Circular HCI

This paper reflects on the process of embedding circular thinking within a multidisciplinary research project on future smart materials. We have identified a series of complexities and saw the need for facilitating a shared material understanding between stakeholders. As an attempt to tackle these complexities and integrate circularity as core part of the material development process, we report on the creation of two design tools; a (i) material properties capturing tool to classify smart materials based on their technical properties and a (ii) scenario mapping tool to articulate future use-case scenarios where smart materials circulate in closed loop systems. Following numerous iterations and feedback sessions with our collaborators, we discuss a range of emerging challenges and how we tackled them.
Sentiment Analysis using Deep Learning - A survey

Opinion Mining and Sentiment analysis on textual data are widely carried out. The main aspect is what techniques we use to accentuate the performance of these models. In this paper, we are going to perform multi-class sentiment analysis using Deep Learning models like Long Short Term Memory (LSTM) and C-LSTM on textual data and compare its performance. We also see its results as compared to other Machine Learning models. Performance evaluation technique like parameter tuning using Dropout regularization is carried out to analyze its effect on the accuracy of the model. The models were also trained using three different datasets for observing the results.
Decision tree classification algorithm for non-equilibrium data set based on random forests

In order to overcome the problems of poor accuracy and high complexity of current classification algorithm for non-equilibrium data set, this paper proposes a decision tree classification algorithm for non-equilibrium data set based on random forest. Wavelet packet decomposition is used to denoise non-equilibrium data, and SNM algorithm and RFID are combined to remove redundant data from data sets. Based on the results of data processing, the non-equilibrium data sets are classified by random forest method. According to Bootstrap resampling method with certain constraints, the majority and minority samples of each sample subset are sampled, CART is used to train the data set, and a decision tree is constructed. Obtain the final classification results by voting on the CART decision tree classification. Experimental results show that the proposed algorithm has the characteristics of high classification accuracy and low complexity, and it is a feasible classification algorithm for non-equilibrium data set.
The Optimization and Application of Blended Teaching Based on Artificial Intelligence

The intelligent learning space, logical reasoning, deep learning, processing and perception of complex situations contained in artificial intelligence can bring significant changes to the development of blended learning. The influence of artificial intelligence on the blended learning is mainly reflected in breaking the traditional physical space to create the ecology of teacher-student interaction, promoting the personalized development of students' thinking, and optimizing the evaluation methods of students' quality. The paper explores the development of artificial intelligence related to the construction of personalized teaching resources, the promotion of interactive teaching and the integration of classroom teaching and online learning.
Building Energy-Cost Maps From Aerial Images and Ground Robot Measurements With Semi-Supervised Deep Learning

Planning energy-efficient paths is an important capability in many robotics applications. Obtaining an energy-cost map for a given environment enables planning such paths between any given pair of locations within the environment. However, efficiently building an energy map is challenging, especially for large environments. Some of the prior work uses physics-based laws (friction and gravity force) to model energy costs across environments. These methods work well for uniform surfaces, but they do not generalize well to uneven terrains. In this letter, we present a method to address this mapping problem in a data-driven fashion for the cases where an aerial image of the environment can be obtained. To efficiently build an energy-cost map, we train a neural network that learns to predict the complete energy maps by combining aerial images and sparse ground robot energy-consumption measurements. Field experiments are performed to validate our results. We show that our method can efficiently build an energy-cost map accurately even across different types of ground robots.
Experimental demonstration of quantum-to-quantum Bernoulli factory

A Bernoulli factory is a model of randomness processing. Recently, the quantum generalization of a Bernoulli factory with a quantum input and classical output offers a clear advantage over classical means in simulating classical randomness. A more ``quantum'' version of a Bernoulli factory with a quantum input and quantum output has been proposed and shows applications in simulating quantum randomness using quantum resources, the so-called quantum-to-quantum Bernoulli factory, which simulates probability amplitudes rather than probability. In this paper, we report an experimental implementation of a quantum-to-quantum Bernoulli factory with linear optics. We implement three elements of a quantum-to-quantum Bernoulli factory, including inversion, multiplication, and addition, and demonstrate how to compare quantum states via a quantum-to-quantum Bernoulli factory. Our results provide a thorough understanding of the Bernoulli factory problem in the quantum world and will stimulate the quantum advantages in simulating a wider range of classically infeasible random processes.
Food Allergens Monitoring System Backed-up by Blockchain Technology

In the near future, the evolution of technology will be able to offer a wide range of solutions for healthcare specific to each individual. It will be possible to create miniaturized portable medical instruments, of small size and cost, having functions of recognition for different “dangerous substances or environments”: gluten, drugs, alcohol, sleeping pills, stimulants, molds, volatile substances etc. The development of such equipment must be accompanied by the implementation of scalable and secure software solutions for data collection, storage and visualization. In the current paper, a solution based on blockchain technology for the safe storage, management and access of the measurements data is presented.
Quantifying the Uncertainty of Parameters Measured in Spontaneous Speech of Speakers With Dementia

Purpose Corpus analyses of spontaneous language fragments of varying length provide useful insights in the language change caused by brain damage, such as caused by some forms of dementia. Sample size is an important experimental parameter to consider when designing spontaneous language analyses studies. Sample length influences the confidence levels of analyses. Machine learning approaches often favor to use as much language as available, whereas language evaluation in a clinical setting is often based on truncated samples to minimize annotation labor and to limit any discomfort for participants. This article investigates, using Bayesian estimation of machine learned models, what the ideal text length should be to minimize model uncertainty. Method We use the Stanford parser to extract linguistic variables and train a statistic model to distinguish samples by speakers with no brain damage from samples by speakers with probable Alzheimer's disease. We compare the results to previously published models that used CLAN for linguistic analysis. Results The uncertainty around six individual variables and its relation to sample length are reported. The same model with linguistic variables that is used in all three experiments can predict group membership better than a model without them. One variable (concept density) is more informative when measured using the Stanford tools than when measured using CLAN. Conclusion For our corpus of German speech, the optimal sample length is found to be around 700 words long. Longer samples do not provide more information.
DARPA's Impact on Artificial Intelligence

The Defense Advanced Research Project Agency's (DARPA) mission is to make pivotal investments leading to research breakthroughs that support national security. DARPA artificial intelligence (AI) programs have emphasized the need for machines to perceive and interact with the world around them; to frame problems and to arrive at solutions and decisions based on reasoning; to implement those decisions, perhaps through consultation with a human or another machine; to learn; to explain the rationale for decisions; to adhere to rules of ethical behavior defined for humans; to adapt to dynamic environments; and, to do all of this in real‐time. In short, DARPA has always been interested in AI frameworks that integrate AI and computer science technologies, and the application of those frameworks to DARPA‐hard problems. In this article, we describe the significant role that DARPA has played in the establishment of AI, and introduce six articles that explore DARPA's Three Waves of AI.
Semantic Fusion-based Pedestrian Detection for Supporting Autonomous Vehicles

To increase traffic safety and transportation efficiency, adopting intelligent transportation systems (ITS) has become a trend. As an important component of ITS, one essential task of autonomous vehicles is to detect pedestrians accurately, which is of great significance for improving traffic safety and building a smart city. In this paper, we propose an anchor-free pedestrian detection model named Bi-Center Network (BCNet) by fusing the full body center and visible part center for each pedestrian. Experimental results show that the performance of pedestrian detection can be improved with a strengthened heatmap, which combines the full body with the visible part semantic. We compare our BCNet with state-of-the-art models on the CityPersons dataset and the ETH dataset, which shows that our approach is effective. Compared to the backbone model, our BCNet improves the detection accuracy by 1.2% on the Reasonable setup and Partial Setup of the CityPersons dataset.
Dual Quaternion Sample Reduction for SE(2) Estimation

We present a novel sample reduction scheme for random variables belonging to the SE(2) group by means of Dirac mixture approximation. For this, dual quaternions are employed to represent uncertain planar transformations. The Cramér-von Mises distance is modified as a smooth metric to measure the statistical distance between Dirac mixtures on the manifold of planar dual quaternions. Samples of reduced size are then obtained by minimizing the probability divergence via Riemannian optimization while interpreting the correlation between rotation and translation. We further deploy the proposed scheme for nonparametric modeling of estimates for nonlinear SE(2) estimation. Simulations show superior tracking performance of the sample reduction-based filter compared with Monte Carlo-based as well as parametric model-based planar dual quaternion filters.
Utilization of Augmented Reality to Showcase Historical Buildings in Medan City

Abstract Medan is the capital of the province of North Sumatera, Indonesia. Medan city is known as a city that has a lot of old buildings heritage legacy of the former Dutch. It is unfortunate as a result of the lack of maintenance and attention from the government and community making the historic buildings gradually demolished and replaced with new buildings. By using Image-Based-Modelling in the process of making 3D buildings. This application of Augmented Reality Tour is expected to be a recognition media, and can increase the interest in studying history.
Answering Islamic Questions with a Chatbot using Fuzzy String-Matching Algorithm

Abstract The guidance of Muslims in worship refers to the holy Quran and the hadith. Not all people understand the law related to a case in accordance with Islamic teachings. Questions relating to a matter based on Islamic law are widely circulated on the Internet with long and detailed answers. This is good but for some people, a short and direct answer to the core is the desired answer, of course in accordance with the majority of Muslim scholars. One of the many technologies that can be used to answer questions is chatbot. A chatbot is one of many implementations of Natural Language Preprocessing. In this study, the chatbot can find answers to questions in accordance with Islamic law using the fuzzy string-matching algorithm. The research test data were obtained from several people who used chatbot directly by looking at pairs of questions and answers whether they were appropriate or not. The accuracy of the test is 70.37% and the chatbot’s performance is quite good.
Algorithm For Stabilizing Video Images

The basic methods for stabilizing video images are considered. The requirements for the image stabilization algorithm in the video streaming processing system for unmanned aerial vehicles (UAVs) are formulated. An improved modification of the video stabilization algorithm is proposed based on a comparison of key image points. As a descriptor of the found local features, the modern high-performance FREAK method was applied, surpassing existing algorithms both in the speed of generation and comparison of feature vectors and in the accuracy of comparison and resistance to image transformations. This made it possible to significantly increase the performance of the stabilization algorithm and improve the quality of stabilized video images due to a more accurate calculation of the parameters of inter-frame transformations, which, in turn, depends on the accuracy of matching sets of singular points. The results of testing the proposed algorithm on real and synthesized video sequences are presented.
Classification Using C4.5 Algorithm in Election Participation Prediction

Abstract The General Election of the President, DPD, DPR, Provincial DPRD and Regency / City DPRD in 2019, in Indonesia, is carried out by an independent institution namely the General Election Commission (KPU), where there is a measure of the success of the holding by holding the principle of direct, general, free of secrecy. Another component of the election will be the implementation of contestants and voters. In the electoral factor, this is also a measure for success in the overall implementation process, which is a success if the participation of the community is high in the holding of elections. And conversely, if the community participation is low, one of them is the level of public trust in the organizer (government) decreases. Analysis of classification mining data with C4.5 algorithm places the prediction class “Present” and “Not Present” on the permanent voter list (DPT) taken from form C7 (voter attendance list form). C7 data was obtained from polling stations (TPS) located in the village of Wonokasian, Wonoayu District, Sidoarjo Regency, East Java. The number of datasets is 4249 instances, and the number of attributes is 10. With split datasets, for training data are 2965 instances, and testing data are 1284 instances. From testing, the testing data obtained the results of true or appropriate accuracy of 1151 instances (90%) and obtained incorrect or incompatible data as many as 134 instances (10%). This shows a good value for the results of 90% of the corresponding data in the prediction class in the Presidential Election, DPD, DPR, Provincial DPRD, and Regency / City DPRD in 2019.
Retention Pond and Pump Station as an Alternative To Flood Management In Bengawan Jero Irrigation Area

Abstract Water is the source of life to all living organisms. But in a very large volume, water can be problematic to life. One of problems generated by a large volume of water is flood disaster. River, as a water source, is one of natural resources with multifunction to human beings. Some areas in Indonesia have increasing flood intensity as the rainy season comes. Retention pond and pump station is one solution to dealing with flood puddle occurring in area. One of areas experiencing flood routinely annually is Bengawan Jero Irrigation Area (D.I. Bengawan Jero). The design of retention pond and pump station in DI Bengawan Jero was calculated using flood flow rate with repeated period of 10 years. From the result of rainfall data processing with repeated period of 10 years using Nakayasu method, it could be found Q planngci of 7.37 m 3 /s at hour 1. The storage volume needed to deal with flood in the Retention Pond in Bengawan Jero Irrigation area is 3.909 m 3 , using 2 pumps with capacity of 2.5 m 3 /s. From hydrograph analysis, it could be concluded that the use of retention pond and pump station can control the peak flow rate by suppressing or cutting the flood peak to occur.
Sentinel-2A and Landsat 8 OLI to model benthic habitat biodiversity index

Biodiversity of benthic habitats is among the highest of all ecological communities. This study was conducted to model benthic habitat biodiversity indices using a remote sensing approach in optically shallow waters in Karimunjawa Islands-Indonesia. These islands have a wide variety of benthic environments. Two multispectral imagers, namely Sentinel-2A and Landsat 8 OLI, were used. A series of statistical tests were applied in the empirical modeling using the pixel values of both images with in situ Shannon index (H), Simpson index (D), and Shannon's Equitability (EH) calculations. The modeling inputs were sunglint-corrected bands, water column-corrected bands, PCA-transformed bands, MNF bands, and occurrence texture bands. The results indicate that multispectral remote sensing images can be used to map benthic habitat biodiversity indices. However, the difference between the concepts of H, D, and EH calculations and the reflectance value recorded by the sensor remove the possibility of obtaining higher accuracy. H, D, and EH maps derived from Sentinel-2A had varying levels of accuracy, namely 46.8%, 59.1%, and 54.5%, respectively, while Landsat 8 OLI produced these three maps with 45.81%, 57.34%, and 53.81% accuracy.
Disabled and Design Researcher

This paper aims to start a conversation about rethinking de- sign research environments and practices, as socially and physically accessible and engaging for design researchers with mixed abilities. We report a first-person account of how a design researcher can face physical and social exclusion in related environments. We recall several instances from a disabled design researcher's experiences in a design lab, in a design conference, and in the design research process itself. We recommend ways to transition design labs and research practices to more inclusive infrastructures and practices.
The DigForSim Agent Based Simulator of People Movements in Crime Scenes

Evidence analysis is one of the Digital Forensics tasks and involves examining fragmented incomplete knowledge and reasoning on it, in order to reconstruct plausible crime scenarios. After more than one year of activity within the DigForASP COST Action, the lack of real data about movements of people in crime scenes emerged as a major limitation to the need of testing the DigForASP prototypes that exploit Artificial Intelligence and Automated Reasoning for evidence analysis. In this paper we present DigForSim, an Agent Based Modeling and Simulation tool aimed at producing synthetic, controllable data on the movements of agents in the crime scene, in form of files logging the agents' position at given time points. These log files serve as benchmarks for the DigForASP reasoning prototypes.