Reviewerly/exHarmony huggingface dataset:
An Estimation-Domain Approach to MEMS Multi-IMU Fusion for sUAS

Small unmanned aerial systems (sUAS) require precise inertial state information at high temporal rates for the purposes of stability and navigation as well as to provide accurate attitude estimates to scientific payloads. Common sUAS inertial navigation systems implement a single strapdown inertial measurement unit (IMU), typically made up of an orthogonal triad of linear accelerometers and a similar triad of rate gyroscopes alongside a single GPS/GNSS receiver to accomplish this task. These outputs are fused optimally, sometimes with other sensors, in an extended Kalman filter (EKF) architecture to generate reliable estimates of the vehicle states. This work develops an estimation-domain fusion strategy for combining the state estimates from multiple inertial navigation systems (INS) for the purposes of sUAS navigation and imaging. The algorithm is implemented in MATLAB and the results are presented.
Enhanced Short Video Understanding by Integrating User Behavior and Multimedia Content Information

The focus of ICME 2019 Grand Challenge is short video understanding and recommendation system based on user-video interaction data and multi-modal video features, including visual, text, and audio features. This paper provides the solution of our team hanhan to track 2 of this challenge. We cast this problem as a binary classification problem and addressed it by careful feature engineering and gradient boosted decision trees. To fully exploit the implicit feedback and multi-model content information, we created truncated SVD-based and neural-net-based embedding features for users, videos and authors. Furthermore, ensemble of a collection of models that take into consideration the cold-start and imbalanced nature of the recommendation task can further significantly improve upon the best single model. By using the proposed approach, our team was able to attain the 1st place in track 2 of the competition.
Equivalence for negabent functions and their relative difference sets

A bent function from F2n to F2, n even, can be transformed into a negabent function, or slightly more general into a bent4, also called shifted bent function, by adding a certain quadratic term. If n is odd, then negabent functions similarly correspond to semibent functions with some additional property. Whereas bent functions induce relative difference sets in F2n×F2, negabent functions induce relative difference sets in F2n−1×Z4. We analyse equivalence of negabent functions respectively of their relative difference sets. We show that equivalent bent functions can correspond to inequivalent negabent functions, hence one can obtain inequivalent relative difference sets in F2n−1×Z4 with EA-equivalence. We also show that this is not the case when n is odd. Finally we analyse the class of semibent functions that corresponds to negabent functions and show that though partially bent semibent functions always can be shifted to negabent or bent4 functions, there are many semibent functions which do not correspond to negabent and bent4 functions.
The Influences of Capability and Character on Human’s Manual Dexterity

In order to investigate the impact of human's capability and human's character on manual dexterity, 83 college students as subjects were tested by BD-II-601 manual dexterity testing instruments. Two comprehensive evaluation parameters, namely "general athletic capability" and "artistic capability," were constructed. Boxplots, two-factor ANOVA with repeated measures, t-test and other statistical methods were employed to analyze and test the experiment data. Statistical results show that the main effects of "general athletic capability" and "artistic capability" on the results of manual dexterity tests are statistically "very significant" (p < 0.01) and statistically "significant" (p < 0.05), respectively. However, the interaction of two comprehensive evaluation parameters is not statistically significant (p > 0.05). And, "character" should be considered as an interference factor to the results of manual dexterity tests.
Pinball loss based extreme learning machines

Abstract Extreme Learning Machine (ELM) is a novel machine learning method by training single hidden layer feedforward neural network. It employs squared loss function to minimize the mean squares error, which is sensitive to noises and outliers. In this paper, pinball loss function with quantile error is introduced into ELM in order to improve the robustness of ELM. An ELM model based on squared pinball loss function (SPELM) and an ELM model based on pinball loss function (PELM) are proposed. The corresponding optimization problem are solved by iterative reweighted algorithm. Three simulated datasets and nine Benchmark datasets are used to verify the validity of the proposed models. It is concluded that the proposed SPELM and PELM are superior to other comparisons, especially for datasets containing larger proportion of outliers.
Exploring Social Validation on A Collaborative Curation Platform

In recent years, social curation technology has been successfully promoted as a means of addressing the problem of involving citizens with news content. Sites like Facebook., Reddit and Storify., just to name a few, allow their users to discuss, comment., share, identify and review news content from various sources. A key goal of this paper is to analyze the process of social validation in the context of Acropolis, a social computing platform that allows citizens to build and share their own narratives about complex or long-term news stories. The sense of social validation determines an increase in self-esteem, self-confidence, and instant happiness, when conveniently engendered in virtual environments.
Neuromorphic Silicon Photonics on Foundry and Cryogenic Platforms

Silicon photonics presents an opportunity for complex, multi-purpose information processing with optoelectronics. Using foundry devices, some photonic neural networks support sub-nanosecond signals. Using cryogenic devices, others support single-photon signals. We compare these platforms and summarize recent experimental results on programmability in photonic neurons and networks.
Video Shake Detection

Video surveillance systems play an irreplaceable role in power scene monitoring. However, in the process of image acquisition, video surveillance equipment will inevitably have various external interference factors. This paper proposes an effective method for video shake detection. This method incorporates the classical BOW (bag-of-words) model into the traditional projection method to get the acquired feature representation in a higher level and make it more robust. In addition, we focus on the local characteristics of the image and use a block operation. The experimental results show the effectiveness of our method.
Algorithmic Matching Attacks on Optimally Suppressed Tabular Data

The objective of the cell suppression problem (CSP) is to protect sensitive cell values in tabular data under the presence of linear relations concerning marginal sums. Previous algorithms for solving CSPs ensure that every sensitive cell has enough uncertainty on its values based on the interval width of all possible values. However, we find that every deterministic CSP algorithm is vulnerable to an adversary who possesses the knowledge of that algorithm. We devise a matching attack scheme that narrows down the ranges of sensitive cell values by matching the suppression pattern of an original table with that of each candidate table. Our experiments show that actual ranges of sensitive cell values are significantly narrower than those assumed by the previous CSP algorithms.
Semantic Labeling and Object Registration for Augmented Reality Language Learning

We propose an Augmented Reality vocabulary learning interface in which objects in a user's environment are automatically recognized and labeled in a foreign language. Using AR for language learning in this manner is still impractical for a number of reasons. Scalable object recognition and consistent labeling of objects is still a significant challenge, and interaction with arbitrary physical objects in AR scenes has consequently not been well explored. To help address these challenges, we present a system that utilizes real-time object recognition to perform semantic labeling and object registration in Augmented Reality. We discuss its implementation, our motivations in designing it, and how it can be applied to AR language learning applications.
Argument Structure Mining in Scientific Articles: A Comparative Analysis

Scientific articles serve as examples to demonstrate the standard functional units of argumentation, extracting its argument structure would benefit the knowledge discovery, representation and retrieval. This study proposes a coding schema based on the argumentation ontologies, and uses the schema to annotate 40 articles in two research field, then combines with sequential pattern mining to find argument structure model. The results expose a similar linear construction with differences in the specific argument structures of each knowledge domain. Therefore, our models have strong applicability in these two fields.
XCS with Combined Reward Method (XCSCR) for Policy Search in Multistep Problems

A reward mechanism is critical for a Reinforcement Learning agent to learn action policies from rewards. The reward mechanism establishes a policy by estimating contributions of constituents of the policy to a reward. Traditionally, rewards from an environment have two categories: long-term rewards for guiding the policy learning process, and short-term rewards for optimisation. However, long-term, positive rewards are scarce at the initial learning phase in multistep problems such that existing reward mechanisms lack sufficient stimulus to learn policies effectively. This paper proposes XCSCR, an Accuracy-based Learning Classifier System (XCS) algorithm with a combined reward (CR) method, to guide the search for global optimal policies in multistep maze problems. The XCSCR discriminates long-term and short-term rewards through four novel rewardassignment mechanisms: 1) A short-term reward mechanism encourages exploration of the RL agent searching for policies based on short-term rewards. 2) An imprinting mechanism amends the negative impact of indiscriminate rewards between exploration and exploitation. 3) A learning-rate switching mechanism emphasises the impact of long-term positive rewards in the policy searching process. 4) A learning step-threshold mechanism creates an optimisation pressure for policies. Experiments were conducted in three maze environments as this enabled the effects of XCSCR on policies to interpreted easily. Results show that the XCSCR enables learning the optimum path-finding policies quicker and more often than previous XCS algorithms. The XCSCR's improvements for the policy search will facilitate realworld applications, e.g. robotic applications.
Objective Quality Assessment Method for Stereoscopic Image Retargeting

The popularity and variety of stereoscopic display devices have led to the need for stereoscopic image retargeting (SIR). Evaluating the quality of retargeted stereoscopic images objectively has become of great importance for optimizing the retargeting operation. The previous works have leveraged 2D image retargeting quality assessment methods to address this issue. However, these methods cannot effectively detect image structure distortion, which is a crucial factor for quality degradation of retargeted stereoscopic images. In this paper, we first propose a novel triangulation-based method to effectively represent the structure distortion and image quality. And then we further evaluate the retargeted stereoscopic images from the perspective of image quality, depth perception and visual comfort. Experimental results demonstrate that our method outperforms the state-of-the-art methods in the consistency level of the predicted scores with subjective scores.
Flame Image Segmentation Algorithm Based on Motion and Color Saliency

This paper proposed a flame segmentation algorithm based on the saliency of motion and color. First, feature point detection is performed on the video image using the scale-invariant feature transform (SIFT) algorithm, and the optical flow field of moving object in the adjacent frame is acquired by the optical flow method. According to the optical flow vector difference between the target pixel point and the surrounding neighborhood pixels, the motion saliency map is obtained based on the Munsell color system. Then, the LSI flame color statistical model based on the Lab and HSI space is used to extract color saliency map of video images. Finally, under the Bayes framework, the motion saliency map and the color saliency map are fused in an interactive manner to obtain the final flame segmentation map. Experimental results show that the proposed algorithm can effectively segment the flame image in different scenarios.
How Much Enhancing Confidentiality and Integrity on Data Can Affect Mobile Multi-Cloud: The "ARIANNA" Experience

Ensuring confidentiality and integrity on data in Cloud computing is still a big challenge. With the advent of the 'Mobile Cloud Computing (MCC)", confidentiality and integrity issues has reemerged inheriting all the limitations introduced by the use of the mobile devices. ARIANNA is an Android application representing the software enabler which allows to extend the SSME Cloud Service, the experimental multi-Cloud system deployed and maintained at the "Cloud Data Center - University of Messina", towards the mobile world represented by the smart devices. In this paper, we present a quantitative performance analysis comparing some commercial Cloud storage services such as Google Drive, Dropbox and OpenStack Swift, with the multi-Cloud approach enabled by the ARIANNA application. In order to evaluate "how much" the overhead introduced by ARIANNA approach costs, we conducted several experiments taking into account the mobile application in real and dynamic multi-Cloud scenarios. The paper discusses some consideration about the actual adoptability of this approach in real application domain and some software and architectural improvements to further improve ARIANNA's performance.
Fitness Landscape Analysis in Data-Driven Optimization: An Investigation of Clustering Problems

Data-driven optimization problems such as clustering provide a real-world representative source of instances for benchmarking continuous metaheuristic optimization algorithms. Although many metaheuristics and other algorithms have been applied to clustering problems, relatively little research has attempted to explore the structure of the problem space and/or fitness landscape for these problems. In contrast, problem-specific analysis and insights for several classes of combinatorial problems have been developed. This paper investigates the structure of the fitness landscapes of clustering problems, focusing on the fundamental parameters that define problem instances (the dimensionality, number and distribution of the data points and the number of clusters). The paper also provides a general method for active, targeted generation of problem instances based on real-world datasets. The results provide a number of new insights into this family of continuous optimization problems as well as methods and guidelines intended to facilitate better experimental evaluation and comparison of continuous optimization algorithms.
Single Image Haze Removal by Feature Mapping

To restore a clear image from a single hazy image, many priors have been proposed for transmission estimation, but these priors often fail in some cases. In this paper, we propose a regression mapping model based on hazy features for haze removal. We fisrt propose sixteen hazy features for mapping, which include the dark channel prior, hazy measures and statistical features. Support Vector Regression (SVR) is adopted to learn the mapping between these hazy features and corresponding transmissions. Then we randomly select patches of synthetic haze and corresponding transmissions from a synthetic dataset to train the mapping model. Experimental results on both natural and synthetic images show that our regression based method achieves significantly better performance than existing state-of-the-art methods.
Research and Design of an Improved ElGamal Digital Signature Algorithm

Abstract Firstly, the digital signature technology and its basic principle are introduced. Then, an ElGamal digital signature algorithm based on discrete logarithm is described, and two aspects of security and signature efficiency are improved on the basis of this algorithm, and the correctness, security and complexity of the improved scheme are analyzed. The results show that the improved ElGamal algorithm achieves the purpose of improvement.
Automatic Generation of Dynamically Relightable Virtual Objects with Consumer-Grade Depth Cameras

This research demo showcases the results of novel approach for estimating the illumination and reflectance properties of virtual objects captured using consumer-grade RGB-D cameras. This method is implemented within a fully automatic content creation pipeline that generates photo realistic objects in real-time virtual reality scenes with dynamic lighting. The geometry of the target object is first reconstructed from depth images captured using a handheld camera. To get nearly drift-free texture maps of the virtual object, a set of selected images from the original color stream is used for camera pose optimization. Our approach further separates these images into diffuse (view-independent) and specular (view-dependent) components using low-rank decomposition. The lighting conditions during capture and reflectance properties of the virtual object are subsequently estimated from the specular maps. By combining these parameters with the diffuse texture, reconstructed objects are then rendered in a real-time virtual reality demo that plausibly replicates the real world illumination and showcases dynamic lighting with varying direction, intensity, and color.
Interactive Planning and Sensing in Spatiotemporally Varying Uncertain Environments

We address planar path-planning for a mobile vehicle to traverse a workspace with minimum exposure to an unknown spatially and temporally varying scalar field, which we call the threat field. The threat field is estimated by a finite number of mobile sensors that can take pointwise measurements with finite sampling frequency. We address this problem using an iterative bidirectional interaction between planning and sensor placement phases. Owing to this interaction, sensors are placed at locations where the threat field measurements are of most relevance to the path-planning problem. We provide theoretical conditions under which this iterative sensing-planning algorithm is convergent. This algorithm includes considerations for finite durations of time required for the various computations involved therein. Numerical simulation results and prior theoretical results with time-invariant threat fields indicate that not only is the proposed algorithm convergent, but it also produces near optimal paths.
Two-Stream Sparse Network for Accurate Image Super-Resolution

Deep convolutional neural networks (CNN) have achieved significant success in single image super-resolution (SISR) task. Though previous works have proved that deeper networks yield better performance, simply stacking more layers to deepen networks ignores the complementarity between different feature levels and largely increases the computation cost, and leads to limited image reconstruction quality. In this paper, we aim to separately extract shallow and deep features to enforce their respective contribution to the recovering of the super-resolution image. We propose a Two-Stream Sparse Network (TSSN) in which we design sparse residual block (SRB) to efficiently extract local features and explicitly learn shallow and deep features via two-stream architecture. Besides, we apply attention mechanism to effectively aggregate shallow and deep features. Extensive experiments demonstrate that the proposed method outperforms most of existing state-of-the-art methods on benchmark datasets in both quantitative metric and visual quality.
Dynamic Profiles Using Sentiment Analysis for VAAs

Traditional VAAs provide recommendations of political parties and candidates focusing on static profiles of users. The goal of this work is to develop a candidate profile based on different parameters, such as the perspective of voters, social network activities, a nd expert opinions, to construct a more accurate dynamic profile of candidates. Understanding the elements that compose a candidate profile will help citizens in the decision-making process when facing a lack of information related to the behavior and thinking of future public authorities.
A Real-Time Programming Battle Web Application by Using WebRTC

We are developing a web system for learning Python programming. The multiple users can join the system simultaneously through programming battles. This system consists of two parts which are web browsers and a web server. The browser side program written in JavaScript uses an idea from Robocode, which is a open sourced programming learning tool. A user of Robocode plays a battle by writing robot programs in Java. On the other hand, the user of our system writes a program in Python, and the program is converted to a JavaScript of robot program. The conversion is done on the web sever as soon as the user changes his or her code in real-time. In our previous work, we used WebSocket to implement a client-server model that is one-on-one fight. However, since the CPU usage ratio of both browsers and server were high, it was difficult to increase the number of players. Therefore, in this implementation, we use WebRTC to build an application on communicating among browsers that supports more than two players efficiently. We will compare CPU usage ratio on WebSocket model over WebRTC model. Moreover, we will consider which method is suitable for our system.
Feature Selection via Adaptive Spectral Clustering based on Joint Mutual Information

Feature selection plays an important role in big data mining and pattern recognition, which includes selecting a subset of the most informative features that produces compatible results as the original entire set of features. A new similarity measure is proposed in terms of information theory, in particular joint mutual information between features with respect to class labels. Based on the similarity measure, an adaptive spectral clustering that could determine cluster number adaptively is proposed. Furthermore, we propose a new feature selection algorithm called Adaptive Spectral Clustering based on Joint Mutual Information (ASC-JMI) which can effectively and efficiently deal with both irrelevant and redundant features, and select a high-quality feature subset. The experimental results on five high-dimensional benchmark datasets demonstrate that the ASC-JMI is effective in selecting the informative features and obtains competitive classification performance.
Leveraging Temporal Reasoning for Policy Selection in Learning from Demonstration

High-level human activities often have rich temporal structures that determine the order in which atomic actions are executed. We propose the Temporal Context Graph (TCG), a temporal reasoning model that integrates probabilistic inference with Allen's interval algebra, to capture these temporal structures. TCGs are capable of modeling tasks with cyclical atomic actions and consisting of sequential and parallel temporal relations. We present Learning from Demonstration as the application domain where the use of TCGs can improve policy selection and address the problem of perceptual aliasing. Experiments validating the model are presented for learning two tasks from demonstration that involve structured human-robot interactions. The source code for this implementation is available at https://github.com/AssistiveRoboticsUNH/TCG.