Reviewerly/exHarmony huggingface dataset:
A Semantic k-Anonymity Privacy Protection Method for Publishing Sparse Location Data

With the development of location technology, location-based services greatly facilitate people's life . However, due to the location information contains a large amount of user sensitive informations, the servicer in location-based services published location data also be subject to the risk of privacy disclosure. In particular, it is more easy to lead to privacy leaks without considering the attacker's semantic background knowledge while the publish sparse location data. So, we proposed semantic k-anonymity privacy protection method to against above problem in this paper. In this method, we first proposed multi-user compressing sensing method to reconstruct the missing location data . To balance the availability and privacy requirment of anonymity set, We use semantic translation and multi-view fusion to selected non-sensitive data to join anonymous set. Experiment results on two real world datasets demonstrate that our solution improve the quality of privacy protection to against semantic attacks.
Chameleon accumulator and its applications

Static accumulators allow to succinctly represent a set into a single accumulator value. For every accumulated element, one can efficiently compute a witness to prove its membership in the accumulator. Dynamic accumulators are an extension of the static accumulator, which additionally support insert and remove operations. To the best of our knowledge, all of the existing accumulator schemes may disclose the accumulated set to an outsider. This may be undesirable in many business situations. To this end, we introduce a more powerful primitive called chameleon accumulator (CA), which is compatible with the previous accumulator scheme. It additionally allows to update the accumulated set without changing the accumulator. For this new property, we formalize its corresponding security called indistinguishability: arbitrary adversary cannot find out whether the collision finding algorithm had been used or not. Next, we provide the first construction of CA and prove its security under Square-CDH assumption. To additionally support non-membership witness, we formally define and construct the first practical universal CA (UCA). We extend the construction of UCA to support subset operations with the constant-size witness. Finally, we show how to use the chameleon accumulator to reduce update efficiency to constant in verifiable keyword searchable encryption.
Efficiency analysis of Muskingum-Cunge method and kinematic wave method on the stream routing (Study case: upper Ciliwung watershed, Indonesia)

Abstract Flood routing is needed to determine the distribution of flood peak time and magnitude of peak flooding that occurs. Flood routing can be done on the river channel using the transform function principle. There are two methods of flood routing in reach that have significant differences based on general concepts and number of data requirements needed, namely Muskingum-Cunge method and Kinematic Wave method. This study aims to evaluate the efficiency of two flood routing methods, Muskingum-Cunge method and Kinematic Wave method using HEC-Geo HMS, the efficiency of each routing method is obtained from comparison of simulation’s hydrograph peak discharge. results with the observation’s hydrograph peak discharge. Results of the comparisons show that Kinematic Wave method give a large peak discharge which is closer to the observation than simulation results with Muskingum-Cunge method. However, difference of result from two methods are not too far from observation, although Kinematic Wave method uses more detailed data. Based on these results, it can be concluded that Muskingum-Cunge method is more efficient than Kinematic Wave method in conducting flood routing because it provides a hydrograph that is close enough to fewer data requirements.
Towards Robust Web Service Composition with Stochastic Service Failures Based on a Genetic Algorithm

Web service composition aims to loosely couple web services to accommodate complex goals, which can not be accomplished by any existing web service. Many researchers have been working on such service composition problems with the aim to find composite services with optimized Quality of Service (QoS) and/or Quality of Semantic Matchmaking (QoSM). Due to the huge search space of this NP-hard problem, Evolutionary Computation techniques have been popularly utilized to search for solutions with near-optimal QoS and QoSM. A majority of these works share a common assumption that QoS of web services seldom or never changes. However, the execution of composite services obtained from the design stage may fail due to unexpected service failures at the execution stage. In this paper, we introduce a robust service composition approach with the goal to build robust composite services that serve as the blueprint/baseline for service execution. These baseline composite services can cope with unexpected interruptions in a robust manner, by applying local search to resume their feasibility while maintaining high quality at the time of execution. Our experiments show that our new approach can significantly outperform a state-of-the-art service composition method (without explicitly considering the robustness) in terms of both effectiveness and efficiency in the event of unexpected service failures.
Global and Local Oriented Gabor Texture Histogram for Person Re-identification

Automated person re-identification is a key process in global distributed camera systems.This paper proposes a new feature, the Global and Local-Oriented Gabor Texture Histogram (GLOGTH), for person re-identification.GLOGTH is a combination of the local texture and global structure information of a given input image.This feature aims at representing the human appearance traits with low-dimensional feature extraction.The proposed feature extracts the texture information of input images based on the orientation of the weighted gradient from the global representation.In GLOGTH, the principal orientation is determined by the gradient of the pixels.Based on the principal orientation, the Gabor feature is extracted and imbues GLOGTH with the strong ability to express edge information, apart from making it robust to lighting variances.The experimental results acquired from the databases demonstrate that the proposed GLOGTH framework is capable of achieving notable improvements, in many cases reaching higher classification accuracy than traditional frameworks. HIGHLIGHTS• Novel feature extraction method for person re-identification.• Texture and shape based feature extraction.• Global and local feature extraction.• Performance analysis for different person re-identification issues.
From User-Centred Design to Human-Centred Design and the User Experience

The chapter describes the evolution of the User-Centred approach, from the theoretical and methodological body aimed at understanding and interpreting the needs of users in the use of the product/system, to the Human-Centred approach aimed at assessment and interpretation of the overall quality of the interaction between people and the products/systems with which they relate. It also dealt with the User Experience approach, the definition of which has spread in recent years in many different contexts and disciplines; However, this rapid adoption was not accompanied by a precise definition of the concept, which is therefore attributed to partially different meanings, all of which share an ambition to overcome the concept of usability and to study the experience systems-services. In particular, strategies considered most significant for the evaluation of perceptual and emotional experience in the use of products-systems-services are addressed. And how these aspects are strategic within the Human-Centred Design approach.
CNN-Based Stereoscopic Image Inpainting

CNN has proved powerful in many tasks, including single image inpainting. The paper presents an end-to-end network for stereoscopic image inpainting. The proposed network is composed of two encoders for independent feature extraction of a pair of stereo images with missing regions, a feature fusion module for stereo coherent structure prediction, and two decoders to generate a pair of completed images. In order to train the model, besides a reconstruction and an adversarial loss for content recovery, a local consistency loss is defined to constrain stereo coherent detail prediction. Moreover, we present a transfer-learning based training strategy to solve the issue of stereoscopic data scarcity. To the best of our knowledge, we are the first to solve the stereoscopic inpainting problem in the framework of CNN. Compared to traditional stereoscopic inpainting and available CNN-based single image inpainting (repairing stereo views one by one) methods, our network generates results of higher image quality and stereo consistency.
Application of Gait Recognition Technology in Badminton Action Analysis

Abstract In order to improve the quality of teaching, achieve better training results and achieve excellent results in athletics competitions, it is especially important to carry out the analysis of badminton routine teaching and athletic competitions. Gait recognition technology is identified by the individual’s unique biological characteristics. With the improvement of computer performance, under the background of the rapid development of big data and artificial intelligence, the video data of badminton is collected by deploying high-performance camera. Video image segmentation, preprocessing, feature extraction, get the badminton player’s three-dimensional gait characteristics data. Using the acquired gait data as a sample, a gait-based machine learning model is constructed, and the sample data is trained. Finally, identification information of badminton players is automatically identified, which facilitates the comparison and analysis of different athletes’ movement data.
3D Hand Joints Position Estimation with Graph Convolutional Networks: A GraphHands Baseline

State-of-the-art deep learning-based models used to address hand challenges, e.g. 3D hand joint estimation, need a vast amount of annotated data to achieve a good performance. The lack of data is a problem of paramount importance. Consequently, the use of synthetic datasets for training deep learning models is a trend and represents a promising avenue to improve existing approaches. Nevertheless, currently existing synthetic datasets lack of accurate and complete annotations, realism, and also rich hand-object interactions. For this purpose, in our work we present a synthetic dataset featuring rich hand-object interactions in photorealistic scenarios. The applications of our dataset for hand-related challenges are unlimited. To validate our data, we propose an initial approach to 3D hand joint estimation using a graph convolutional network feeded with point cloud data. Another point in favour of our dataset is that interactions are performed using realistic objects extracted from the YCB dataset. This could allow to test trained systems with our synthetic dataset using images/videos manipulating the same objects in real life.
Evolved Art with Transparent, Overlapping, and Geometric Shapes

In this work, an evolutionary art project is presented where images are approximated by transparent, overlapping and geometric shapes of different types, e.g., polygons, circles, lines. Genotypes representing features and order of the geometric shapes are evolved with a fitness function that has the corresponding pixels of an input image as a target goal. A genotype-to-phenotype mapping is therefore applied to render images, as the chosen genetic representation is indirect, i.e., genotypes do not include pixels but a combination of shapes with their properties. Different combinations of shapes, quantity of shapes, mutation types and populations are tested. The goal of the work herein is twofold: (1) to approximate images as precisely as possible with evolved indirect encodings, (2) to produce visually appealing results and novel artistic styles.
Applying MDD in the Content Management System Domain: Scenarios and Empirical Assessment

Content Management Systems (CMSs) such as Joomla and WordPress dominate today's web. Enabled by standardized extensions, administrators can build powerful web applications for diverse customer demands. However, developing CMS extensions requires sophisticated technical knowledge, and the highly schematic code structure of an extension gives rise to errors during typical development and migration scenarios. Model-driven development (MDD) seems to be a promising paradigm to address these challenges, however it has not found adoption in the CMS domain yet. Systematic evidence of the benefit of applying MDD in this domain could facilitate its adoption; however, an empirical investigation of this benefit is currently lacking. In this paper, we present a mixed-method empirical investigation of applying MDD in the CMS domain, based on an interview suite, a controlled experiment, and a field experiment. We consider three scenarios of developing new (both independent and dependent) CMS extensions and of migrating existing ones to a new major platform version. The experienced developers in our interviews acknowledge the relevance of these scenarios and report on experiences that render them suitable candidates for a successful application of MDD. We found a particularly high relevance of the migration scenario. Our experiments largely confirm the potentials and limits of MDD as identified for other domains. In particular, we found a productivity increase up to factor 17 during the development of CMS extensions. Furthermore, our observations highlight the importance of good tooling that seamlessly integrates with already used tool environments and processes.
Enabling Care Continuity using a Digital Health Wallet

Clinical records capture the temporal, participatory, and interventional details of the care provision process. The exchange of these records plays a critical role in care continuity. Recently, there has been increasing attention on health data privacy and confidentiality which translates to questions on ownership and accessibility of clinical records. Traditional approaches to remedy this stand the risk of reducing the accessibility of these records, making care continuity across facilities more difficult. This poses a need for mechanisms that would enable the secure exchange of health data without adversely affecting the access to clinical records. This paper presents the Digital Health Wallet (DHW); a blockchain-enabled system that allows seamless clinical workflow orchestration and patient-mediated data exchange through consent management in a privacy-preserving manner. We conducted a preliminary test to benchmark the performance of DHW in resource-constrained healthcare facilities in developing countries.
Tight bounds on ℓ1 approximation and learning of self-bounding functions

We study the complexity of learning and approximation of self-bounding functions over the uniform distribution on the Boolean hypercube {0,1}n. Informally, a function f:{0,1}n→R is self-bounding if for every x∈{0,1}n, f(x) upper bounds the sum of all the n marginal decreases in the value of the function at x. Self-bounding functions include such well-known classes of functions as submodular and fractionally-subadditive (XOS) functions. They were introduced by Boucheron et al. (2010) in the context of concentration of measure inequalities. Our main result is a nearly tight ℓ1-approximation of self-bounding functions by low-degree juntas. Specifically, all self-bounding functions can be ϵ-approximated in ℓ1 by a polynomial of degree O˜(1/ϵ) over 2O˜(1/ϵ) variables. We show that both the degree and junta-size are optimal up to logarithmic terms. Previous techniques considered stronger ℓ2 approximation and proved nearly tight bounds of Θ(1/ϵ2) on the degree and 2Θ(1/ϵ2) on the number of variables. Our bounds rely on the analysis of noise stability of self-bounding functions together with a stronger connection between noise stability and ℓ1 approximation by low-degree polynomials. This technique can also be used to get tighter bounds on ℓ1 approximation by low-degree polynomials and a faster learning algorithm for halfspaces. These results lead to improved and in several cases almost tight bounds for PAC and agnostic learning of self-bounding functions relative to the uniform distribution. In particular, assuming hardness of learning juntas, we show that PAC and agnostic learning of self-bounding functions have complexity of nΘ˜(1/ϵ).
Mamdani Fuzzy Inference System using Three Parameters for Flood Disaster Forecasting in Bandung region

Abstract This study aims to investigate the flood events forecast by applying the Mamdani fuzzy inference system and rule-based reasoning. For input and output linguistic variable of the model based on three parameters such as population density, altitude, as well the rainfall. Each variable has three to four membership functions. The data used come from the Statistic Bureau of Indonesia, National Disaster Management Authority, and Meteorology and Climatology Center of Indonesia for 2014 to present. A different model for each region was constructed depending on available data sets. We determine based our model on forty-eight fuzzy IF-THEN rules and fuzzy reasoning. The output variable which has three membership function takes from 220 to 402 as representative flooding level warning such as normal, alert, and danger. The results of the study have high agreements with recorded data in on site with increases the output values toward the real-time flood event. Therefore, if that area will be used for living, this will have risks that flood always is a threat to those areas.
Adaptive Multi-swarm Bat Algorithm (AMBA)

Modified shuffled multi-population bat algorithm (MSMPBat) is a recently proposed swarm algorithm. It divides its population into multiple sub-populations (SPs), each of which uses different parameter settings and evolves independently using an enhanced search mechanism. For information exchange among these SPs, a solution from one SP is copied to the next after every generation. This process leads to duplication of solutions over time. To overcome this drawback, different techniques are introduced. Opposition-based learning is used to generate a diverse starting population. For information exchange, if a solution comes too close to the swarm best, only then it is sent (moved, not copied) to another swarm. Four techniques are proposed to select this second swarm. Initially, the selection probability of each technique is same. The algorithm adaptively updates these probabilities based on their success rate. The swarm which gave up the solution uses a modified opposition-based learning technique to generate a new solution. These changes help to maintain the overall diversity of the population. The proposed approach, namely, adaptive multi-swarm bat algorithm (AMBA), is compared to six algorithms over 20 benchmark functions. Results establish the superiority of adaptive multi-swarm bat algorithm.
Interactive geological visualization based on quadratic-surface distance query

Seismic data visualization and analysis can help the domain experts, e.g., geologists and oil or gas exploration experts, to explore the distribution of petroleum or gas. It assists them to get a better understanding of stratigraphic structures and the distribution of the geological materials, e.g., underground flow path (UFP) and the contexts of UFPs (river delta, floodplain, slump fan, oil well, etc.). UFPs are one of the significant stratigraphic structures according to the domain experts, because they are closely related to the distribution and the migration of oil or gas. We design a quadratic-surface distance query scheme to explore UFPs and their contexts within a local region. First, it just needs to share parameters of quadratic surfaces to the rendering modules instead of all volume data or all subvolume data to conduct distance queries, and it is flexible to perform multiple complex logic operations through the quadratic surface-based queries. Second, it enables one to perform domain-specific interactions after distance queries such as the flexible switching of multiple display modes. Third, it enables one to perform local transfer function on different subvolumes different query results or their arbitrary combinations. We have evaluated the approach by comparing them with existing methods by performance evaluation and result evaluation. Results show that the proposed approach is capable of performing complex distance queries and fulfilling the domain-specific interactions getting better results and timing performance.
Time Series Data Prediction Based on Sequence to Sequence Model

Abstract Home appliance test is an essential part of the R&amp;D process. However, since home appliance test must be carried out under certain circumstances, it makes the development cycle longer. If it is possible to predicting the home appliance test data, it will improve the efficiency of home appliance development. Sequence to sequence model has been proved that it has a strong ability to map the input sequence and output sequence and it has been widely used in machine translation tasks. In order to solve the home appliance test data prediction problem, we firstly tried to apply sequence to sequence model to predict numerically continuous time series data. Our experiments proved that our model can predict the data of relatively long-time steps when inputting data of relatively short time steps. In our experiment, the mean absolute percentage error of the prediction of our model is about 0.1%. Our approach has a strong generalization ability and performs well in different experimental scenarios. Finally, we believe that the sequence to sequence method performs satisfactorily on prediction problems for continuous time series data. With this model, we can obtain more accurate results than traditional methods and it is meaningful for the R&amp;D of home appliances.
Consistency Control for Model Versions in Evolving Model-Driven Software Product Lines

Model-driven software product lines evolve in both time and space. Consistency control for model versions constitutes a key challenge. We propose a novel approach to consistency control called well-formedness analysis and repair: Instead of attempting to guarantee consistency of each configurable version a priori (which is hard and restrictive), consistency is controlled only when a product version is actually configured. Conflicts, i.e., violation of well-formedness constraints, are detected and repaired, driven by configurable strategies. This approach is generic; it is instantiated for feature models (for the variability model) and EMF models (as domain artifacts).
Decision support system design of higher education scholarship recipients with android-based

Abstract The purpose of this study is to present the design of Decision Support System (DSS) to analyse the college scholarship recipients on the android-based. The design methodology for selecting scholarship recipients is used Fuzzy Multiple Attribute Decision Making (Fuzzy MADM). Fuzzy MADM is an algorithm that used to find the optimal result of a number of alternatives with certain criteria. This method is chosen because the calculation of this method uses multiplication to correlate the attribute rating and only yields the largest value that will be selected as an alternative scholarship recipient. The object set in this research is UIN Sunan Gunung Djati Bandung student. The design system is made based on android with hardware in the form of smart phone. The design results have a level of compliance with regulatory standards that have been established. The results also indicate the logical and appropriate target selection mechanism for scholarship recipients for the most eligible students who received scholarship assistance.