Reviewerly/exHarmony huggingface dataset:
Approximate eigenvalue decompositions of orthonormal and symmetric transformations with a few Householder reflectors

Abstract The ability to decompose a signal in an orthonormal basis (a set of orthogonal components, each normalized to have unit length) using a fast numerical procedure rests at the heart of many signal processing methods and applications. The classic examples are the Fourier and wavelet transforms that enjoy numerically efficient implementations (FFT and FWT, respectively). Unfortunately, orthonormal transformations are in general unstructured, and therefore they do not enjoy low computational complexity properties. In this paper, based on Householder reflectors, we introduce a class of orthonormal matrices that are numerically efficient to manipulate: we control the complexity of matrix-vector multiplications with these matrices using a given parameter. We provide numerical algorithms that approximate any orthonormal or symmetric transform with a new orthonormal or symmetric structure made up of products of a given number of Householder reflectors. We show analyses and numerical evidence to highlight the accuracy of the proposed approximations and provide an application to the case of learning fast Mahalanobis distance metric transformations.
Invisible and Cost-Effective Sensors with a Network Robot for an IoT House for Tourists

This paper presents a novel application of our previously developed sensor system to recognition of behavior patterns using a network robot and cost-effective invisible sensors. We set up the system at an actual house as a concept of an internet of things (IoT) house for tourists. We obtained 14 person-day benchmark datasets from ten people in their 20s. For constructing benchmark datasets, they recorded event times of seven patterns: getting up, sleeping, going out, coming home, emergency calls, opening or closing of a refrigerator, and the use of a TV remote control. As ground truth labels for cross-validation-based evaluation, we integrated them into three patterns: going out, staying at home, and sleeping on the bed. The experimentally obtained results revealed that the mean recognition accuracies with random forests were 99.60%, 99.30%, and 98.54% for the respective three datasets.
A Penetration Metric for Deforming Tetrahedra using Object Norm

In this paper, we propose a novel penetration metric, called deformable penetration depth PD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> , to define a measure of inter-penetration between two linearly deforming tetrahedra using the object norm [1]. First of all, we show that a distance metric for a tetrahedron deforming between two configurations can be found in closed form based on object norm. Then, we show that the PD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> between an intersecting pair of static and deforming tetrahedra can be found by solving a quadratic programming (QP) problem in terms of the distance metric with non-penetration constraints. We also show that the PD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> between two, intersected, deforming tetrahedra can be found by solving a similar QP problem under some assumption on penetrating directions, and it can be also accelerated by an order of magnitude using pre-calculated penetration direction. We have implemented our algorithm on a standard PC platform using an off-the-shelf QP optimizer, and experimentally show that both the static/deformable and deformable/deformable tetrahedra cases can be solvable in from a few to tens of milliseconds. Finally, we demonstrate that our penetration metric is three-times smaller (or tighter) than the classical, rigid penetration depth metric in our experiments.
Adaptive Video Summarization via Robust Representation and Structured Sparsity

To improve faster browsing and more efficient content indexing of huge video collections, video summarization has emerged as an important area of research for the multimedia community. One of the mechanisms to generate video summaries is to extract keyframes which represent the most important content of the video. However, there are still some problems like image imperfection and noise interference, which seriously affect the performance of keyframe selection. Aiming at above problems, in this paper, we propose a linear reconstruction framework to summarize the videos. The first model in our framework seeks the most informative keyframes (base vectors) using the structure sparsity of the $$\ell _{21}$$ norm regularization, to represent all the frames as the linear combination of them in a video. Furthermore, we also propose another more robust model via $$\ell _{21}$$ norm based loss function to suppress the outlier, and form the joint sparsity with $$\ell _{21}$$ norm regularization. For the optimization, we design two efficient algorithms for two proposed models respectively. Finally the extensive experiments on real world video datesets are presented to show the effectiveness of the proposed framework.
The environment of cognitive-contextual software design: from concept to implementation

Abstract Two main points of the objectives to design software systems (SS) have been considered: achieving proper functioning of the SS with consistent results, and the possibility to obtain reliable assessments of the Software System quality depending on the initial requirements. A cognitive-contextual design strategy has been proposed that can significantly improve the perception of actual elements of the design task by creating a context with selected formalized conditions, and enhance cognitive coherence of the project elements throughout the Software System lifecycle.
On the Application of Game theory for Multiagent system-based Cognitive Performance Management in Software-defined networks

Nowadays, cognitive multiagent self-organization is the subject of intensive research in the field of info-communication technology. This state-of-the-art in constructing distributed intelligent systems for telecommunication management is already receiving attention both from researchers and from industrial application developers. The purposes of this work are to present the implementation of a game theory-based cognitive model for network performance management, to analyze the possibilities of using this model in dynamic orchestration and resource allocation use cases in software-defined networks. Thus, special attention has been devoted to the developed multiagent management system architecture, the stages of which form various game-theoretic models with the participation of intelligent software agents, designed to organize automated coordination of requests from the application layer to the corresponding network resources.
Reducing Blokchain Size Using Complementary Block

The size, especially the growth rate, of blockchain has been a major problem since its introduction in 2008. A common blockchain scheme will have exponential growth rate that affect the scalability of blockchain. To overcome this problem, complementary block is proposed. The blockchain is split into multiple chain where each block in a chain is a complementary of a block in another chain. The result shows that complementary block have smaller growth rate compared to original blockchain scheme. Despite its small data duplication, the complementary block still retains the same integrity level as the original blockchain.
Analysis on Power Emergency System' Surveillance Structure with Ubiquitous-Networked Intelligent Techniques

Data Sources are relatively independent in existing IOT applications. Based on restructure of the present surveillance structure, with Ubiquitous-Networked Intelligent techniques, the paper implements a power emergency measuring structure with ubiquitous networked system with various data sources. Survey on power emergency system shows that security system has positive impact on system's surveillance structure, and the result enlargs the concept and scale of it's application and industry.
Survey on Privacy-Preserving and Other Security Issues in Data Mining

In present day, an ever-increasing number of researches in information mining increases the seriousness about security issue. The security issues in information mining can’t just be tended by limiting data integration or by reducing the utilization of information technology. So as to keep up the security of the customer in the process of data mining, different types of strategies have been proposed that are dependent on the probabilistic perturbation of information records. Information mining service requires precise information for their outcomes to be significant; however, protection concern may impact clients to give fake data. Here, we present a detailed survey on privacy and security issues on data mining by analyzing different techniques from standard publishers of the year from 2010 to 2017. Based on the techniques utilized and types of issues are analyzed and classified. Moreover, to indicate the improvement and accuracy of all the research articles is also discussed. Furthermore, the analysis is carried to find the importance of their approaches so that we can develop a new technique to solve the security threats.
Towards Probabilistic Reasoning in Type Theory - The Intersection Type Case

The development of different probabilistic models of uncertainty has been inspired by the rapid progress in various fields, e.g. in AI, probabilistic programming, etc. Lambda calculus is a universal model of computation suitable to express programming languages concepts. Hence, different methods for probabilistic reasoning in lambda calculus have been investigated. In this paper, we develop a formal model for probabilistic reasoning about lambda terms with intersection types, which is a combination of lambda calculus and probabilistic logic. The language of lambda calculus with intersection types is endowed with a probabilistic operator. We propose a semantics based on the possible world approach. An infinitary axiomatization is given for this system and it is proved to be sound with respect to the proposed semantics.
Multi-perspective Network Representation Based on Human Curation

Network representation learning represents nodes in networks as low-dimension vectors which has been attracting increasing attention recently due to its effectiveness in network analysis tasks such as classification and link prediction. In this paper, our focus is on content curation social networks (CCSNs). There are more than one user relation subnetworks formed by different user relations in a social media network. However, most of existing representation learning algorithms usually study only one subnetwork which cannot study users from different views. On the other hand, most of the existing approaches are designed for universal networks which do not consider the unique characteristics of different networks. We propose a multi-perspective network representation based on human curation (MNHC) model, which aims to infer network representations across multiple relations in CCSNs. The network representation model utilizes the unique structure of networks and human curation signals in CCSNs which combines two user relation subnetworks and human curation signals for informative network representations. Experiment results show that the model could obtain good performance in both classification and link prediction tasks.
A Co-saliency Detection Method Based on Attention Model

Existing co-saliency detection algorithms mainly focus on extracting low-level features, failing to deal with variations in background scenes and foreground objects. Thus, we propose a weakly supervised co-classification method based on K-means clustering to decompose the complicated high-level co-salient feature extraction into identifying the common object class(es) and discovering the corresponding neural attention maps. By our approach, the high-level consistency can be mapped to class excitation maps (CEMs), which can capture the common foreground regions. Then we integrate CEMs with bottom-up saliency maps (BUSMs), detecting co-saliency based on both high-level and low-level features combinedly. Finally, we apply a fully connected Conditional Random Field (FC-CRF) model for accurate boundary recovery. Our method is novel in weakly supervised learning and combining high-level and low-level cues. Experiment results show that our method achieves the state-of-the-art results on three benchmark datasets.
A Stable Dimensionality-Reduction Method for Internet-of-Things (IoT) Streaming Data

With the growing popularity of Internet-of-Things (IoT), large amount of IoT devices are widely used in many spheres of life and tremendous volume of data (which are real-time, streaming and high-dimensional) have been collected, exchanged and processed. In order to reduce the computational complexity and communication cost, it is necessary to reduce the high dimension of these collected data. Because of the data complexity and limited capacity of IoT devices, most of dimensionality reduction algorithms are no longer convenient. To overcome this problem, we propose a lightweight Instance-based Neighbor Embedding (INE) method to accomplish the dimensionality reduction for the IoT streaming data on the resource-constrained devices, which not only keeps the results stable but also maintains the similarity structures of data. Besides, we also propose some techniques and detailed proofs for efficient computation. Extensive experimental results validate the effectiveness of our method.
Human Activity Classification Using Convolutional Neural Networks

Visual Lifelogging (VL) usually refers to a technique used by lifeloggers to acquire their information through a wearable sensor (such as a wearable camera) in varying amounts of details. In other words, VL represents a complete and comprehensive black box of human’s daily activities and can offer a great potential to mine or/and extract accurate and opportune knowledge on the way people live their lives. With the advent of sensing technology that allowing efficient sensing of personal activities, both the amount of data available and the ability to process this data had increased. This is well seen in the popularity and growing interest given and lent by the scientific community to the two emerging fields of lifelogging and deep learning. Using features that separate activities are vital for human behavior understanding and characterization. In this paper, we emphasize more particularly on human activity classification (HAC) captured by a low temporal resolution wearable camera. For this goal, we use a Deep Convolutional Neural Network (DCNN) trained on the large Dataset ImageNet, which contains millions of images and transfer this knowledge to classify automatically the daily human activities into one of the categorized activities. The numerical results of the proposed approach are very encouraging with an accuracy of 98.78%.
Development of flood forecasting model and warning systems at Way Ruhu – Ambon

Abstract Way Ruhu has a river with the slope of the land is steep. The heavy rains that often occur in the city of Ambon resulted in floods and landslides in several places thus to give a big loss to the people in the city of Ambon both the loss of property and lives. This study was conducted with the aim to determine the magnitude and time of flooding in the Way Ruhu watershed, the modeling results will be applied in android so that people can evacuate early because information can be received more quickly and the Government can prepare better mitigation programs. Modeling methods are carried out using 2D software HEC-RAS 5.0.7 which requires hydrological and topographical data as the main inputs. The output from HEC-RAS is a flood map, flood height, and duration of the flood. The results of this study indicate that the time between the upstream river rise and the arrival of floodwater in the downstream is less than one hour, with flood characteristics as high as 1-5 meters and receding within 4 to 11 hours.
Systematic Study to Identify the Usage and Role of Mobile Devices for Classroom-Interaction

Mobile devices are pervasive technology that have become a vital part of human life. Wherever we go, all over the place, far and wide, the one thing that is always with us is mobile device. They are no longer used just for the communication, but also for learning and development. With the advent of mobile technologies, these phones have paved a path into our life. There are several uses of phones that are not only essential for classroom interaction but also for erudition outside the class. It allows the learner to interact with the teacher and other colleagues from any geographical location. Since mobile phones are so common these days, it is owned by maximum number of students for various interaction and feedback progression. The wide expansion of mobile devices has given a cost-effective solution for teaching-learning process. This work deals with the systematic study of the mobile devices with its uses and challenges. Introduction of these devices will give a win-win situation for both educator and learner. Also, these devices can help teachers to interact with the students sagaciously inside the classroom. Use of these devices has added wings to classroom-interaction, giving students freedom and time to interact with their teachers.
Asymptotic Parallel Parking for Differential-Drive Vehicles in Presence of Static Obstacles

The problem of generating smooth parallel parking control for non- holonomic robot is addressed in this paper. A two-stage motion planner is designed considering collision avoidance, noholonomic constraints and control convergence. In the first stage, the robot is controlled to trace a double-tangential S-shaped (DTS) curve from the starting point to an intermediate point. To ensure zero curvature, the DTS curve is composed of four segments: two arcs of circumference that forms a S-shape and two extension lines that connects to the initial and final configurations. A reference tracking controller is designed to enable the robot to move smoothly with continuous steering profile along the DTS curve. While in the second stage, leveraging on the singularity property of the non- holonomic system, the robot can be asymptotically stabilized at the pre-determined pose with zero steering angle. The proposed parallel system incorporating two control stages is verified to outperform the existing approaches in terms of efficiency and robustness using a differential-drive model through simulation.
Application of Remote Sensing for Determination of Power Plant Area: Case Study of Lumut Balai, South Sumatra

Abstract Subduction zone located beneath Sumatra, Indonesia causes the formation of volcanic sequences, which can be associated with the potential of geothermal energy. One of them is Lumut Balai area in South Sumatra. A study of using remote sensing method was conducted as one of the preliminary methods to find out the geothermal potential in the Lumut Balai area. In this case, an analysis of regional geology maps, Landsat 8 OLI / TIRS, and digital elevation model (DEM) was carried out. The geothermal potential is obtained based on a combination of anomalies in land surface temperature (LST), the normalized difference vegetation index (NDVI), and distribution of alteration zones. The analysis of NDVI result shows the Lumut Balai area dominated by mostly moderate to high level of vegetation density up to 0.88. Whereas, the LST result shows the maximum value, which is 40.3°C located at the residential area in the north and the open land in the northwest of Lumut Balai. The correlation between the results of LST and NDVI has been shown in geothermal surface manifestations, that have vegetation less than 0.88 while the temperatures show a higher value up to 40°C than the surrounding its area. While the results of the DEM analysis provide topographic information, the slope in the study area used to determine the area for development of power plant. The power plant more likely to be located in the southwest and northeast of Lumut Balai where it has flat terrain and not in the near-fault area.
Robust time-varying Kalman predictor for the systems with random one-step measurement delay and uncertain noises variance

For the networked uncertain systems with random one-step measurement delay, and uncertain noises variance, the robust Kalman prediction problem is solved. By new augmented state approach, the original system is transformed to the standard systems without measurement delays. Applying the minimax robust estimation principle, the robust time-varying Kalman predictor is presented in the sense that for all admissible uncertainties, its actual prediction error variance is guaranteed to have the minimal upper bounds. Its robustness is proved by Lyapunov equation method. A simulation example about continuous stirred tank reactor (CSTR) system verifies the correctness and effectiveness of the proposed results.
SISTEM PAKAR PENENTUAN MAKANAN PENDAMPING AIR SUSU IBU MENGGUNAKAN METODE MIN MAX DAN NAÏVE BAYES

Babies are special gifts from God. Babies' age is around 0-24 months old.They experience physical changes and rapid growth along with the intake of nutritional needs.Therefore, good feeding given by their mother will influence their growth.For this reason, a mother should aware of the importance of providing complimentary food intake besides breast milk.It can be given depends on the appropriate age, its frequency, amount, texture, active, responsive and hygienic.The main discussion in this research was to determine complementary foods for babies by implementing an Expert System.The development of this expert system used the Min Max and Naive Bayes methods, which are methodologies of facts or knowledge to get a conclusion.These methods were done by searching the data application and input information to get a conclusion.Algorithm testing was done using a confusion matrix.The results of this study showed that 20 respondents with the Naive Bayes algorithm and Naïve Bayes with Min-Max were successfully provided recommendations 18 of 20 data following the results of recommendations from experts.Bayes algorithm had an accuracy value, that was 95.83%, a precision value was 95% and a recall value was 95% in providing recommendations for complementary foods.
Level Set for Semantic Segmentation with Edge Compensation

Abstract This paper demonstrated that active contour based on points evolution is not suitable for Objects with blurring boundary segmentation. In irregular areas adjacent points have similar motion trends trapped into ‘piles phenomena’. Level set should be preferred in practice. Whereas, classic level set lacks of perception in distance especially narrow and abnormal region. Consequently, we reported an algorithm localized level set that is able to improve accuracy. Meanwhile, in cases lost boundary of bone, we gave a strategy called edge compensation. Depending on shapes of neighborhood slices, defective section is estimated and restored effectively. Our experiments showed that the algorithm localized level set increases segmental quality with precision 99.74%. Additionally, it could not only rectify mistakes brought by incorrect initialization but also have a robust performance to overcome local region with highly inhomogeneous intensity.