Reviewerly/exHarmony huggingface dataset:
Tree-seed algorithm in solving real-life optimization problems

Abstract Tree-seed algorithm (TSA) is a nature-inspired and population-based algorithm for solving continuous optimization problems. The tree-seed relationship is the main motivation of this algorithm. TSA has only two peculiar parameters which are the total number of trees in the stand (pop) and the controller of the seed production (search tendency). Although many problems have been solved in the literature by TSA which is the successful optimizer for low dimensional unconstrained continuous problems, real-life problems have not been addressed yet. In this study, six continuous unconstrained real-life optimization problems (gas transmission compressor design, optimal capacity of gas production facility, gear train design, frequency modulation sounds parameter identification, the spread spectrum radar polyphase code design with 10 decision variables, the spread spectrum radar polyphase code design with 20 decision variables) have been solved. It is seen that choosing the number of the population as 50 and the value of search tendency as 0.1 is appropriate according to experimental results for these problems.
Similarity-Aware Network Embedding with Self-Paced Learning

Network embedding, which aims to learn low-dimensional vector representations for nodes in a network, has shown promising performance for many real-world applications, such as node classification and clustering. While various embedding methods have been developed for network data, they are limited in their assumption that nodes are correlated with their neighboring nodes with the same similarity degree. As such, these methods can be suboptimal for embedding network data. In this paper, we propose a new method named SANE, short for Similarity-Aware Network Embedding, to learn node representations by explicitly considering different similarity degrees between connected nodes in a network. In particular, we develop a new framework based on self-paced learning by accounting for both the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved node similarities) in network representation learning. To justify our proposed model, we perform experiments on two real-world network data. Experiments results show that SNAE outperforms state-of-the-art embedding models on the tasks of node classification and node clustering.
User Interface Code Retrieval: A Novel Visual-Representation-Aware Approach

Code reuse has been perceived to be an effective tool for user interface development that is known to be complex and messy. A fundamental problem in user interface code reuse is how to effectively retrieve working code from existing code repositories, which renders functional interfaces similar to a programmer's input sketch. Existing generic code retrieval techniques are normally inadequate for user interface code due to its associated visual information. In this paper, we put forward a novel approach that retrieves matching candidate code of an interface sketch based on their visual-representation trees. To better capture sub-region similarities, we propose to decompose a visual-representation tree into an ordered list of paths (or sequences). We then devise a novel algorithm to measure the similarity between two sequences by leveraging various visual information and another algorithm to calculate the similarity of two ordered lists by considering the relative positions of different widgets. To evaluate our solution, we carefully design an experiment to generate the similarity scores of 6,750 user interface pairs based on real users' judgement. Based on this ground truth, we show that our method is able to generate accurate similarity scores, outperforming several state-of-the-art competitors.
Women's Health, Wellbeing, &amp; Empowerment

Research in the fields of Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) is increasingly examining topics surrounding women's health. Many of these works convey that women's health cannot be isolated from various social and affective aspects of health, strongly suggesting a more holistic engagement with women's wellbeing instead. Extending beyond wellbeing, we find that much prior work is committed to promoting women's empowerment along various dimensions, such as being more in control of their bodies, having the freedom to express themselves online, being able to secure access to computing education, among other goals. In this panel, we bring together the voices of members of the CSCW community who actively care about women's health, wellbeing, and empowerment in the worlds of research, design, and practice.
EPA

Infection source identification is a well-established problem, having gained a substantial scale of research attention over the years. In this paper, we study the problem by exploiting the idea of the source being the oldest node. For the same, we propose a novel algorithm called Exoneration and Prominence based Age (EPA), which calculates the age of an infected node by considering its prominence in terms of its both infected and non-infected neighbors. These non-infected neighbors hold the key in exonerating an infected node from being the infection source. We also propose a computationally inexpensive variant of EPA, called EPA-LW. Extensive experiments are performed on seven datasets, including 5 real-world and 2 synthetic, of different topologies and varying sizes to demonstrate the effectiveness of the proposed algorithms. We consistently outperform the state-of-the-art single source identification methods in terms of average error distance. To the best of our knowledge, this is the largest scale performance evaluation of the considered problem till date. We also extend EPA to identify multiple sources by developing two new algorithms - one based on K-Means, called EPA_K-Means, and another based on successive identification of sources, called EPA_SSI. Our results show that both EPA_K-Means and EPA_SSI outperform the other multi-source heuristic approaches.
Adversarial Feature Sampling Learning for Efficient Visual Tracking

The tracking-by-detection tracking framework usually consists of two stages: drawing samples around the target object and classifying each sample as either the target object or background. Current popular trackers under this framework typically draw many samples from the raw image and feed them into the deep neural networks, resulting in high computational burden and low tracking speed. In this article, we propose an adversarial feature sampling learning (AFSL) method to address this problem. A convolutional neural network is designed, which takes only one cropped image around the target object as input, and samples are collected from the feature maps with spatial bilinear resampling. To enrich the appearance variations of positive samples in the feature space, which has limited spatial resolution, we fuse the high-level features and low-level features to better describe the target by using a generative adversarial network. Extensive experiments on benchmark data sets demonstrate that the proposed ASFL achieves leading tracking accuracy while significantly accelerating the speed of tracking-by-detection trackers.
Multi-UAV Cooperative Patrol Task Planning Novel Method Based on Improved PFIH Algorithm

This paper investigates the cooperative patrol task planning problems for multiple unmanned aerial vehicles by considering the patrol task requirements.A new cooperative patrol task planning approach is proposed to complete the patrol task.Firstly, the cooperative patrol task planning model for multiple unmanned aerial vehicles is established.Then, the corresponding complex constraint condition processing method with two steps is designed; that is, obtaining the initial result via Floyd algorithm firstly and then generating the optimal solution by adopting the improved Push Forward Insertion Heuristic (IPFIH) algorithm.It is proved that the proposed algorithm can obtain feasible solutions that meet all constraints and maximize the patrol payoff under the limited resources conditions.Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed cooperative patrol task planning approach.
Plant inspection by using a ground vehicle and an aerial robot: lessons learned from plant disaster prevention challenge in world robot summit 2018

In recent years, to prevent accidents and disaster are desired by implementing maintenance and management of facilities, such as conducting periodic inspections with appropriate frequency at plants. However, because the dangerous materials such as flammable gas and explosives is used in a plant, and there are many dangerous places in a plant such as high-temperature environment and high places and narrow spaces, it is desirable to use a remote-controlled robot for safety work and short inspections. Against this background, the Disaster Robotics Category-Plant Disaster Prevention Challenge was held in Japan at the World Robot Summit 2018. Our team was ranked 3rd in this competition, because our strategy of ‘inspection and investigation in cooperation with UGV and UAV’ was effective. In this paper, the competition contents of World Robot Summit 2018 and the robot inspection system that we are studying are explained. And what kind of strategy was challenged and result for these given competition tasks by using our robot system are introduced. And the lessons learned such as advantages and issues in UGV and UAV collaboration work at this competition are described for evaluate a robot investigation system for disaster response and inspection work at plants.
Training Google Neural Machine Translation on an Intel CPU Cluster

Google's neural machine translation (GNMT) is state-of-the-art recurrent neural network (RNN/LSTM) based language translation application. It is computationally more demanding than well-studied convolutional neural networks (CNNs). Also, in contrast to CNNs, RNNs heavily mix compute and memory bound layers which requires careful tuning on a latency machine to optimally use fast on-die memories for best single processor performance. Additionally, due to massive compute demand, it is essential to distribute the entire workload among several processors and even compute nodes. To the best of our knowledge, this is the first work which attempts to scale this application on an Intel CPU cluster. Our CPU-based GNMT optimization, the first of its kind, achieves this by the following steps: (i) we choose a monolithic long short-term memory (LSTM) cell implementation from LIBXSMM library (specifically tuned for CPUs) and integrate it into TensorFlow, (ii) we modify GNMT code to use fused time step LSTM op for the encoding stage, (iii) we combine Horovod and Intel MLSL scaling libraries for improved performance on multiple nodes, and (iv) we extend the bucketing logic for grouping similar length sentences together to multiple nodes for achieving load balance across multiple ranks. In summary, we demonstrate that due to these changes we are able to outperform Google's stock CPU-based GNMT implementation by ~2x on single node and potentially enable more than 25x speedup using 16 node CPU cluster.
Document Detection in Videos Captured by Smartphones using a Saliency-Based Method

Smartphones are now widely used to digitize paper documents. Document detection is the first important step of the digitization process. Whereas many methods extract lines from contours as candidates for the document boundary, we present in this paper a region-based approach. A key feature of our method is that it relies on visual saliency, using a recent distance existing in mathematical morphology. We show that the performance of our method is competitive with state-ofthe- art methods on the ICDAR Smartdoc 2015 Competition dataset.
Walk-in-Place Navigation in VR

Navigation in the virtual environment is one of the interactions which happens very frequently. Physical locomotion method is popular due to its naturalness in VR but it requires wide area. To use minimum space for the navigation, walk-in-place method is suggested as an alternative way for the virtual environment navigation. In this tutorial, we will cover the following topics. 1) Treadmill based walk-in-place methods 2) Non-treadmill based walk-in-place methods
The design of sound wave and optic marker for physics learning based-on augmented reality technology

Abstract This research aims to design and develop the augmented reality marker of sound wave and optic topics for physics learning. Augmented reality is a technology that can provide visualization and integration a perspective. The develop stages consist of determine the instructional needs and goals, develop the markers, and evaluate the design from peers. This research generated eleven markers: (1) how sound wave made a stress and strain in a percussion phenomenon, (2) the Doppler Effect, (3) the application of organ pipe, (4) the diffraction of the light, (5) the interference of the light, (6) the polarization of the light, (7) how the eyes make object, (8) how the camera make object, (9) how the magnifying glass work, (10) how the microscope work, and (11) how the telescope work. The markers expected to assist in the achievement of the instructional objectives. The peer assessment results indicate that the marker design on sound wave and optic topics supports the physics learning through technology rich environment.
Multiclass discriminant analysis via adaptive weighted scheme

Under homoscedastic Gaussian assumption, it is demonstrated that conventional LDA is formulated by maximizing the weighted arithmetic mean of the Kullback–Leibler (KL) divergences between different classes. However, the calculation of projection directions is dominated by class pairs with large KL divergence, which causes class pairs with small KL divergence overlapping in the low-dimensional subspace when the dimensionality of subspace is strictly lower than c−1, wherein c is the class number. Therefore, the classification accuracy is degraded significantly when dealing with classification task. In this paper, we propose a novel method, namely Multiclass Discriminant Analysis via Adaptive Weighted Scheme (MDAAWS), to alleviate the problem mentioned above by assigning weights to all between-class pairs adaptively. Since the proposed problem is a challenging task, an iterative algorithm is exploited to solve it and the corresponding theoretical analysis is presented as well. Extensive experiments conducted on various data sets demonstrate the effectiveness of MDAAWS when compared with some state-of-the-art supervised dimensionality reduction methods.
Triangular Gaussian mutation to differential evolution

Differential evolution (DE) has been a popular algorithm for its simple structure and few control parameters. However, there are some open issues in DE regrading its mutation strategies. An interesting one is how to balance the exploration and exploitation behaviour when performing mutation, and this has attracted a growing number of research interests over a decade. To address this issue, this paper presents a triangular Gaussian mutation strategy. This strategy utilizes the physical positions and the fitness differences of the vertices in the triangular structure. Based on this strategy, a triangular Gaussian mutation to DE and its improved version (ITGDE) are suggested. Empirical studies are carried out on the 20 benchmark functions and show that, in comparison with several state-of-the-art DE variants, ITGDE obtains significantly better or at least comparable results, suggesting the proposed mutation strategy is promising for DE.
Recognition of Handwritten Chemical Organic Ring Structure Symbols Using Convolutional Neural Networks

Many types of data exhibit characteristic of rotational symmetry. Chemical Organic Ring Structure(ORS) Symbol is such a case. In this paper, we focus on offline handwritten chemical ORS Symbols recognition using convolutional neural networks(CNNs), from application point of view, in order to relax the inconvenience and ineffectiveness of the traditional clickand-drag style of interaction when input chemical notations into electronic devices; from scientific point of view, to explore the capacity of rotation invariance of CNNs using data augmentation. We propose a VGGNet-based classifier for offline handwritten chemical ORS Symbols. To evaluate it, a new dataset of 3600 samples are collected of which 90% is for training while 10% is for test. The recognition accuracy is 84.3% with VGGNet-16 and 92.4% with VGGNet-19.
PIEs

We present a new primitive supporting file replication in distributed storage networks (DSNs) called a Public Incompressible Encoding (PIE). PIEs operate in the challenging public DSN setting where files must be encoded and decoded with public randomness-i.e., without encryption-and retention of redundant data must be publicly verifiable. They prevent undetectable data compression, allowing DSNs to use monetary rewards or penalties in incentivizing economically rational servers to properly replicate data. Their definition also precludes critical, demonstrated attacks involving parallelism via ASICs and other custom hardware. Our PIE construction is the first to achieve experimentally validated near-optimal performance-within a factor of 4 of optimal by one metric. It also allows decoding orders of magnitude faster than encoding, unlike other comparable constructions. We achieve this high security and performance using a graph construction called a Dagwood Sandwich Graph (DSaG), built from a novel interleaving of depth-robust graphs and superconcentrators. PIEs' performance makes them appealing for DSNs, such as the proposed Filecoin system and Ethereum data sharding. Conversely, their near-optimality establishes concerning bounds on the practical financial and energy costs of DSNs allowing arbitrary data.
Seeding on Samples for Accelerating K-Means Clustering

K-means clustering with random seeds results in arbitrarily poor clusters. Much work as been done to improve initial centroid selection, also known as seeding, however better seeding algorithms are not scalable to large or unloadable datasets. In this paper, we first show that running the D2 seeding used in k-means++ on a random sample then clustering the whole dataset results in faster runtime and comparable accuracy compared to the original algorithm. We then propose a new method that performs the D2 seeding and clustering on the random sample. This method essentially runs k-means++ on the sample, then extends cluster assignments to every other point using nearest centroid classification. This results in faster clustering and comparable clustering quality compared to the original algorithm. We demonstrate the performance of both algorithms on synthetic and real datasets.
Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing

Sequential incentive marketing is an important approach for online businesses to acquire customers, increase loyalty and boost sales. How to effectively allocate the incentives so as to maximize the return (e.g., business objectives) under the budget constraint, however, is less studied in the literature. This problem is technically challenging due to the facts that 1) the allocation strategy has to be learned using historically logged data, which is counterfactual in nature, and 2) both the optimality and feasibility (i.e., that cost cannot exceed budget) needs to be assessed before being deployed to online systems. In this paper, we formulate the problem as a constrained Markov decision process (CMDP). To solve the CMDP problem with logged counterfactual data, we propose an efficient learning algorithm which combines bisection search and model-based planning. First, the CMDP is converted into its dual using Lagrangian relaxation, which is proved to be monotonic with respect to the dual variable. Furthermore, we show that the dual problem can be solved by policy learning, with the optimal dual variable being found efficiently via bisection search (i.e., by taking advantage of the monotonicity). Lastly, we show that model-based planing can be used to effectively accelerate the joint optimization process without retraining the policy for every dual variable. Empirical results on synthetic and real marketing datasets confirm the effectiveness of our methods.
User Account Access Graphs

The primary authentication method for a user account is rarely the only way to access that account. Accounts can often be accessed through other accounts, using recovery methods, password managers, or single sign-on. This increases each account's attack surface, giving rise to subtle security problems. These problems cannot be detected by considering each account in isolation, but require analyzing the links between a user's accounts. Furthermore, to accurately assess the security of accounts, the physical world must also be considered. For example, an attacker with access to a physical mailbox could obtain credentials sent by post.
Dynamic Collaborative Recurrent Learning

In this paper, we provide a unified learning algorithm, dynamic collaborative recurrent learning, DCRL, of two directions of recommendations: temporal recommendations focusing on tracking the evolution of users' long-term preference and sequential recommendations focusing on capturing short-term preferences given a short time window. Our DCRL builds based on RNN and Sate Space Model (SSM), and thus it is not only able to collaboratively capture users' short-term and long-term preferences as in sequential recommendations, but also can dynamically track the evolution of users' long-term preferences as in temporal recommendations in a unified framework. In addition, we introduce two smoothing and filtering scalable inference algorithms for DCRL's offline and online learning, respectively, based on amortized variational inference, allowing us to effectively train the model jointly over all time. Experiments demonstrate DCRL outperforms the temporal and sequential recommender models, and does capture users' short-term preferences and track the evolution of long-term preferences.
Intrusion Detection and Tracking at Railway Crossing

Railway crossing safety is an issue of great public concern. With the development of computer vision, intelligent video surveillance was widely used for object detection. However, most existing object detection methods could not perform well in outdoor environment, especially under the bad weather conditions. In this paper, an intrusion detecting algorithm was proposed for railway crossing. Our method consists of the following three steps. First, three nonparametric background models with different learning rates are designed for the detection of moving and static objects. Second, an object tracking strategy is introduced for reducing false positives in the detection. Finally, a feedback mechanism is introduced to selectively update the background models when static objects are removed. Our method was tested on real railway sequences and the public i-LIDS datasets. Experimental results showed the proposed method achieves accurate detection for both moving and static objects and some false positives caused by rains and partial occlusion could be reduced.