Reviewerly/exHarmony huggingface dataset:
Load Data Compression Based on Integrated Neural Network Model

The smart grid has been widely developed in recent years. The data produced each day are huge, which makes it hard to transform and store data. Efficient load data compressing are required to decrease the data size. This paper analyzes the load data characteristics and divides these data into 3 types, then designs traditional compressing models for different types of data correspondingly. These models are then integrated with neural networks to achieve better details keeping ability. Finally, a well-designed encoding method are given to get higher compression ratio. Experiments have shown our method can achieve high compression ratio, and many details of original data can be well preserved.
Testing the Social Presence Aspect of the Multimodal Presence Scale in a Virtual Reality Game

Presence is a key variable in virtual reality (VR), however, it is a complex variable consisting of different aspects. The Multimodal Presence Scale (MPS) by Makransky, Lilleholt, and Aaby was developed to measure physical, social and self-presence. But how well can it actually detect changes in one aspect of presence, and differentiate between different aspects of presence? To answer these questions, we use a German translation of the MPS in an experiment with a VR game with 45 participants. We examine social presence -the sense of being with another - specifically, and compare three conditions (abstraction levels) that should differ in the degree of social presence, but not in other aspects of presence. Results indicate that the MPS is reliable and useful for measuring social presence, while still correlating strongly with presence overall.
Towards a Marketplace for Secure Outsourced Computations

This paper presents Kosto – a framework that provisions a marketplace for secure outsourced computations, wherein the pool of computing resources aggregates that which are offered by a large cohort of independent compute nodes. Kosto protects the confidentiality of clients’ inputs and the integrity of the outsourced computations using trusted hardware’s enclave execution (e.g., Intel SGX). Furthermore, Kosto mediates exchanges between the clients’ payments and the compute nodes’ work in servicing the clients’ requests without relying on a trusted third party. Empirical evaluation on the prototype implementation of Kosto shows that performance overhead incurred by enclave execution is as small as $$3\%$$ for computation-intensive operations, and $$1.5{\times }$$ for I/O-intensive operations.
Bit-Error Aware Lossless Color Image Compression

In this paper, we propose bit-error aware lossless compression algorithms for color image compression subject to bit-error rate during transmission. Each of our proposed algorithms contains three stages. The first stage converts the RGB images to YCrCb images. The next stage uses the predictive encoder to generate the residue sequence. A genetic algorithm is adopted to search the best combination of the color transformation and prediction. At the last stage, the generated residue sequence by predictive coding is encoded. Three residue encoding methods are investigated, including bi-level block coding, or interval Huffman coding or Huffman coding. To protect the bit stream from bit errors during transmission, (7,4) Hamming code is used to protect the color transformation information, predictor type and parameters, and residue coding parameters. Our experimental results validate the coding performances in terms of the compression ratio (CR) and peak signal to noise ratio (PSNR).
A dialogue-based annotation for activity recognition

This paper presents a method to collect training labels for human activity recognition by using a dialogue system. To show the feasibility of using dialogue-based annotation, we implemented the dialogue system and conducted experiments in the lab setting. The preliminary performance of activity recognition attained the f-measure of 0.76. We also analyze the collected data to provide a better understanding of what users expect from the system, how they interact with it and its other potential uses. Finally, we discussed the results obtained and possible directions for future works.
Designing interactive interfaces by keeping the natural beauty of public places

Natural, public places in cities serve often as a place for recreation and relaxation. Additionally, such places often signify historic and social importance about which visitors would like to know more. Screens and other currently existing technology would, however, destroy the natural beauty of such a place. Attention-aware and unobtrusive interfaces seem to offer a solution to this problem. In our approach, we conducted a qualitative survey with 19 people and an epoché at a decommissioned cemetery which is mainly used for recreation and leisure time. Overall, the results show that the majority would like to know more about the deceased with the information closely placed to the grave, but without disturbing the natural, mystical atmosphere of the cemetery. In this work in progress report, we present our research approach to attention-aware, unobtrusive and context-sensitive interactive prototypes that keep the natural beauty and recreational characteristics of such a place.
Analysis of Coupling Evolution on Open Source Systems

Software evolution is an intrinsic process of the software life cycle. The comprehension of this process is a central research topic in Software Engineering. It is widely accepted that as a software system evolves, its internal quality declines, and its complexity increases. However, there is a gap in the comprehension of how this process occurs in a fine-grained view. In this work, we apply a software metric approach to investigate how the internal quality of object-oriented software systems evolves in the aspect of coupling. More specifically, we analyze (i) how the coupling behavior may be described over the software evolution, (ii) how the coupling behavior affects the reusability and complexity of the systems, and (iii) the percentage of classes from the systems that directly impacts on the coupling evolution. The results and observations of this study are compiled in seven properties of coupling evolution, among which stand out: (i) the coupling behavior is better modeled by a cubic function, (ii) the coupling evolution tends to increase the complexity of the systems, (iii) the systems tend to be designed with a high level of complexity, and (iv) the coupling evolution is affected by a small group of classes.
Can BlockChain Technology Provide Information Systems with Trusted Database? The Case of HyperLedger Fabric

BlockChain technology has imposed a new perspective in the area of data management, i.e., the possibility of realizing immutable and distributed ledgers. Furthermore, the introduction of the concept of smart contract has further extended the potential applicability of this potentially disruptive technology. Although BlockChain was developed to support virtual currencies and is usually associated with them, novel platforms are under development, that are not at all related to the original application context. An example is HyperLedger Fabric. Developed by the Linux Foundation, it is aimed to provide information systems with distributed databases where the transaction log is immutable. This should ensure trusted cooperation among many parties. In this paper, we briefly present main concepts and functionalities provided by HyperLedger Fabric. We then discuss its potential applicability and current limitations.
The EEG visualising pendant for social situations

This abstract discusses the EEG Visualising Pendant (2012) [3], an emotive wearable that maps and visualises EEG data from a NeuroSky MindWave Mobile Bluetooth EEG headset. It has been developed through several iterations as a doctoral research prototype for studies evaluating the use of bespoke, aesthetic wearables in the role of nonverbal communication.
Free-space Polygon Creation based on Occupancy Grid Maps for Trajectory Optimization Methods

In this paper, we present a strategy for incorporating occupancy grid maps into local trajectory optimization methods to allow for safe autonomous navigation. We construct polygons as surface representations of the locally visible area and merge them with the aid of third-party software to form connected free-spaces. The method can be applied to any two-dimensional environments and different representations of vehicle dimensions. It allows direct optimization methods to be used consistently for different problem levels from control to planning. The strategies developed are applied to a rover system that moves in the plane. We evaluate our algorithms on the basis of simulation results and real-world experiments.
Toward digital image processing and eye tracking to promote visual attention for people with autism

Visual attention is guided by the integration of two streams: the global, that rapidly processes the scene, and the local, that processes details. For people with autism, the integration of these two streams can be disrupted by the tendency to privilege details (local processing) instead of seeing the big picture (global processing). Consequently, people with autism struggle with typical visual attention, evidenced by their verbal description of local features when asked to describe overall scenes, disrupting their social understanding. This work aims to explore an augmentation for global processing by digitally filtering visual stimuli. This work contributes initial prototypes to improve global processing and leverages an eye tracking dataset to compare results as a validation technique.
Designing a smart toy

Digitally enriched interactive physical objects, or "smart objects", might enable new educational interventions for persons with Neurodevelopmental Disorder (NDD). This paper presents the experience with SAM, a dolphin-shaped smart toy. SAM is inspired by the practice of Pet Therapy and is designed to engage people with NDD in a variety of tasks through a set of multisensory stimuli and various modes of interaction. SAM can be used alone or integrated with digital content or smart space components such as digitally controlled lights, sounds, and bubble makers. SAM behavior can be customized by therapists to address the specific needs of each person with NDD. We describe the progression of SAM prototypes, from a "stand-alone" tangible object to a multimodal smart toy integrated with multimedia contents or the whole smart environment. The design process of SAM involved NDD specialists at two therapeutic centers as well as persons with NDD. From this experience, we derived some guidelines generalizable to the design of any smart toy for people with NDD.
An Unsupervised Anomaly Detection Method Based on Density Peak Clustering for Rail Vehicle Door System

Frequent failure of the Rail Vehicle Door System (RVDS) has strong negative impact on the safe operation of rail vehicles. It is an urgent need to detect abnormal doors in their early stage of failures for effective condition-based maintenance. This paper proposes an unsupervised anomaly detection method for RVDS based on Density Peak Clustering (DPC) algorithm. The method relies on the real-time information of door's position, motor's speed and current collected from the multiple doors of a railway vehicle. Features related to the doors' health status can be extracted from these data, and then the DPC algorithm is used to identify the abnormal ones. Verification on North Extension of Guangzhou Metro Line 3 shows that this method can detect unhealthy doors accurately and provide a valid technique for the remote intelligent maintenance system for rail vehicles.
Cognitive Fusion for Target Tracking

The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources. In addition, the us
Fuzzy Logic Approach Based on User behavior Trust in Cloud Security

With cloud computing increasing in popularity by providing a massive number of services such as recourses and data center, the number of attacks on the cloud is increasing. Cloud platform security becomes an important factor in cloud development. Basic security protection such as traditional access control (TAC) is not able to satisfy the security requirements within the expansion of cloud computing. For example, TAC is not able to prevent a malicious user from accessing the cloud. Therefore, in this paper, we propose the fuzzy logic approach based on user behavior trust (FUBT). In this model we consider four types of evidence: login, security, operation, and performance to evaluate the cloud users. We start the model by building a user profile based on user' history behavior to compare it with current behavior. Moreover, we consider four types of trust: direct, history, indirect and comprehensive trust. We present a fuzzy approach to calculate direct and comprehensive trust values. FUBT is flexible and scalable as consider more evidence to monitor and evaluate user behavior. Finally, the simulation of FUBT shows that the model can effectively evaluate the users.
Hilbert Vector Convolutional Neural Network: 2D Neural Network on 1D Data

Two-Dimensional Neural Network (2D CNN) has become an alternative method for one-dimensional data classification. Previous studies are focused either only on sequence or vector data. In this paper, we proposed a new 2D CNN classification method that suitable for both, sequence and vector data. The Hilbert space-filling curve was used as a 1D to 2D transfer function in the proposed method. It is used for two reasons: (i) to preserve the spatial locality of 1D data and (ii) to reduce the distance of far-flung data elements. Furthermore, a 1D convolution layer was added in the first stage of our proposed method. It can capture the correlation information of neighboring elements, which is effective for sequence data classification. Consequently, the trainable property of 1D convolutions is very helpful in extracting relevant information for vector data classification. Finally, the performance of the proposed Hilbert Vector Convolutional Neural Network (HVCNN) was compared with two 2D CNN based methods and two non-CNN based methods. Experimental results showed that the proposed HVCNN method delivers better numerical accuracy and generalization property than the other competitive methods. We also did weight distribution analysis to support this claim.
Particle-Based Adaptive-Lag Online Marginal Smoothing in General State-Space Models

We present a novel algorithm, an adaptive-lag smoother, approximating efficiently, in an online fashion, sequences of expectations under the marginal smoothing distributions in general state-space models.The algorithm evolves recursively a bank of estimators, one for each marginal, in resemblance with the so-called particle-based, rapid incremental smoother (PaRIS).Each estimator is propagated until a stopping criterion, measuring the fluctuations of the estimates, is met.The presented algorithm is furnished with theoretical results describing its asymptotic limit and memory usage.
Improved RRT* For Fast Path Planning in Underwater 3D Environment

Path planning for Autonomous Underwater Vehicle(AUV) needs to find a feasible path in three-dimension workspace and is a very difficult and challenging task. Traditional algorithms have failed to find the path effectively in high-dimensional space since it is proven that the complexity of the problem is NP-hard. Consequently, sample-based algorithms, such as Rapidly-exploring Random Tree star (RRT*), have been proposed to find the probabilistically complete and asymptotically optimal solution, which reduces the complexity of the algorithm. However, in underwater environment with undulating terrain and scattered floating obstacles, the global uniform random sampling strategy in RRT* costs too much memory and time, resulting in a slow convergence to optimal path. To deal with these problem, an improved RRT* is proposed in this paper. Goal-biased Gaussian distribution sampling with variable standard deviation is proposed to controls the randomness of search nodes. Furthermore, a focused optimal search strategy is introduced to improve the convergence rate. Finally, some simulations are conducted under various 3D underwater environments. The results show that the improved RRT* outperforms RRT* in search efficiency and convergence rate under water.
An Improved Jump Point Search Algorithm for Home Service Robot Path Planning

When the jump point search algorithm is applied to robot path planning, there are problems such as many redundant path points, large cumulative turning angle and the robot incapable of adjusting its posture at the path point. This paper proposes an improved jump point search algorithm, which checks the connectivity between the previous point and the last point of each original point. It also uses vector cross product to identify the direction of the next path point relative to the current path point. The vector dot product is adopted to calculate the rotation angle of the current path point. Three evaluation indicators, such as the number of path points, the total path length and the cumulative turning angle, are formulated to judge the merits and demerits of the optimized algorithm. The experimental results show that on the map of scene 1, improved jump point search algorithm reduces the path points from 9 to 7, with total path length decreased by about 12%, and cumulative turning angle decreased by about 27%. The improved jump point search algorithm is designed to obtain a shorter final path, a smaller cumulative turning angle, and to able robot to adjust posture at the path point.
Investigating Weak Supervision in Deep Ranking

A number of deep neural networks have been proposed to improve the performance of document ranking in information retrieval studies. However, the training processes of these models usually need a large scale of labeled data, leading to data shortage becoming a major hindrance to the improvement of neural ranking models' performances. Recently, several weakly supervised methods have been proposed to address this challenge with the help of heuristics or users' interaction in the Search Engine Result Pages (SERPs) to generate weak relevance labels. In this work, we adopt two kinds of weakly supervised relevance, BM25-based relevance and click model-based relevance, and make a deep investigation into their differences in the training of neural ranking models. Experimental results show that BM25-based relevance helps models capture more exact matching signals, while click model-based relevance enhances the rankings of documents that may be preferred by users. We further proposed a cascade ranking framework to combine the two weakly supervised relevance, which significantly promotes the ranking performance of neural ranking models and outperforms the best result in the last NTCIR-13 We Want Web (WWW) task. This work reveals the potential of constructing better document retrieval systems based on multiple kinds of weak relevance signals.
Visual techniques to compare predictive models

Predictive analysis is an important part of data analysis. Predictive models, based on Statistics or Machine Learning, are increasingly used to estimate, with a certain probability, future values of the variables that describe a phenomenon. Different models produce different results on a same dataset; thus, several models should be compared in order to identify the most suitable one. The paper is part of a larger research that aims at providing interactive visualizations that help the analysts to compare predictive models and to select the model that best fits the data. Specifically, two visualizations are presented, which support the analysts in performing some tasks of the Keim's Visual Analytics Mantra.
Energy-Based Continuous Inverse Optimal Control

The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model (EBM), where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an "analysis by synthesis" scheme, which iterates: 1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time and 2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the EBM simultaneously with a top-down trajectory generator via cooperative learning, where the trajectory generator is used to fast initialize the synthesis step of the EBM. We demonstrate the proposed methods on autonomous driving tasks and show that they can learn suitable cost functions for optimal control.
Integrating geological and airborne geophysical data to review the cartography of Rio Itanguá Batholith, Araçuaí Orogen, Brazil

Rio Itanguá Batholith (RIB) is a plutonic body located in the Northern border of Guanhães block, one of the various basement blocks reworked within the Araçuaí orogen, Eastern Brazil. Although its existence is known since the beginning of the 20th century, its tectonic history remains poorly understood. The RIB was mapped at 1:100,000 scale in the 1990s, before geophysical and satellite data became available. This work aims to re-evaluate the cartography of the RIB, as an initial step towards understanding its genesis and evolution. In order to do so, field and petrographic data, together with airborne gamma-ray spectrometry and magnetometry data, were integrated, resulting in a geologic map that shows different features from the previous maps produced in the area, such as the batholith division into two different petrographic and geophysical units; the presence of a mafic dike intruding the batholith rocks and the reduction in size of the batholith area. The magnetometric and gamma-spectrometric data proved to be important tools for geological mapping, and the substantial data obtained in the analysis of this tiny area points to the importance of re-evaluating older geological maps, taking geophysical data into account.
Global Path Planning Algorithm for USV Based on IPSO-SA

In order to solve the problem of USV global path planning, a new path planning algorithm is designed, combining particle swarm optimization(PSO) with simulated annealing(SA) algorithm. The algorithm improves the shortcomings of the PSO method, which is easy to fall into the local minimum and the non-global optimality. Based on improved PSO and SA(IPSO-SA), a double-layer USV global path planning algorithm is designed, considering both convergence speed and solution optimization. And then, the IPSO-SA algorithm is applied to the USV maritime global path planning, it only needs to use the global path planning algorithm in a local circle to obtain a small number of point sequences, with low search volume and space-time overhead. Theoretical derivation and simulation experiments verify the effectiveness of the algorithm.