Reviewerly/exHarmony huggingface dataset:
Concrete Image Captioning by Integrating Content Sensitive and Global Discriminative Objective

Current methods for image captioning tend to generate sentences that are generally overly rigid and composed of some most frequent words/phrases, leading to inaccurate and indistinguishable descriptions. This is primarily due to the uneven word distribution of the ground truth captions that encourages to generate high frequent words/phrases while suppressing the less frequent but more concrete ones. In this work, we propose a new Content Sensitive and Global Discriminative objective, which is formulated as two constraints on top of a reference model to facilitate generating concrete and discriminative image captions. More specifically, the content sensitive constraint is designed to place greater focus on the less frequent and more concrete words/phrases, thus facilitating the generation of sentences that better describe visual details of the given images. To further improve the discriminability, the global discriminative constraint is designed to pull the generated sentence to better discern the corresponding image from others. We evaluate the proposed method on the widely used MS-COCO dataset, where it achieves superior performance over existing competing methods. We also conduct self-retrieval experiments to demonstrate the discriminability of the proposed method.
Research on Improvement of Ant Colony Algorithm for Multi-Robot Task Allocation

In this paper, an improved ant colony algorithm is proposed to study the multi-robot task allocation problem. By introducing a backhaul optimization factor, a new task point selection method is designed. The pheromone update method uses a combination of local pheromone update and global pheromone update. In order to further improve the quality of the solution, a cross path removal operation is designed. Simulation results show that the proposed algorithm can obtain high-quality solution and fast convergence, the effect is significant.
A Minerals Boundary Enhancement Method in Petrographic Thin Sections Polarization Images

Optical properties of crystals are one of the most important features of mineral identification. Therefore, distinguish of colors become an important segment. In this study, 20 images under plane-polarized (PPL) and cross-polarized (XPL) are obtained by rotating the analyzer. The first image is under PPL and other images are captured in 19 numbers through 90° polarizers and analyzer rotation with 5° intervals while the microscope table is fixed. RGB bands of 19 images build three new multiband files respectively using layer stacking. After various statistical calculation of multiband files and comparative analysis between Max, Min, Max-Min and Mean images, all result showed the best one is the Max-Min image that distinguish color clearly and efficiently.
Space-Time Slicing: Visualizing Object Detector Performance in Driving Video Sequences

Development of object detectors for video in applications such as autonomous driving requires an iterative training process with data that initially requires human labeling. Later stages of development require tuning a large set of parameters that may not have labeled data available. For each training iteration and parameter selection decision, insight is needed into object detector performance. This work presents a visualization method called Space-Time Slicing to assist a human developer in the development of object detectors for driving applications without requiring labeled data. Space-Time Slicing reveals patterns in the detection data that can suggest the presence of false positives and false negatives. It may be used to set such parameters as image pixel size in data preprocessing and confidence thresholds for object classifiers by comparing performance across different conditions.
Parallel Conflict-Free Ordered Access Memory Based Programmable Hardware Accelerator Structure

Programmable hardware accelerator structure and information processing method based on a new model of computation with parallel conflict-free ordered data and command access is proposed in a paper. The advantages of the proposed hardware accelerator structure over traditional ones are highlighted.
Post-Analysis Framework for Mining Actionable Patterns Using Clustering and Genetic Algorithms

Mining association rules is an important technique in data analysis. Many approaches for rule analysis have been designed to address different problems. Among them, some works developed from multiobjective genetic algorithms derive a set of Pareto solutions, each of which contains a set of membership functions for fuzzy data mining from quantitative transactions with taxonomy. However, because more than one solution exists in a Pareto set, finding a method to determine the appropriate membership functions and combine them with useful knowledge for mining actionable patterns (such as fuzzy generalized association rules and fuzzy utility itemsets) is a useful research problem. Hence, this paper presents a post-analysis-based genetic-fuzzy mining (PA-GFM) framework for mining actionable patterns that involves two phases: membership-function mining and actionable pattern mining. In the first phase, an existing approach is utilized to derive the Pareto solutions with objective functions. In the second phase, a clustering technique using clustering attributes selected by the users is employed to group the Pareto solutions. The representative solution from each group is then exploited to mine actionable patterns based on the users' requirements. Experiments were conducted on both a simulated dataset and a real one to investigate the performance of the PA-GFM framework.
BLISS-A CPSS-like Application for Lifelong Learning

This paper presents a Cyber-Physical-SocialSpace (CPSS)-like application known as BLISS. The system supports a kind of lifelong learning where people who need to be educated have dropped out of school for various reasons but wish to complete their education through individual training. The active components of BLISS called personal assistants are implemented as intelligent agents. The agents' environment consists of three parts - an event-driven BLISS server, a set of personal assistants that will be described in the publication and a School Diary implemented as a blockchain. Furthermore, the reference architecture named Virtual Physical Space (ViPS), used to develop the application, is briefly described.
Application of the Mobile Terminals in the Long-Distance Continuing Education for Preschool Teachers

The mobile learning is considered as a future learning mode, or an indispensable learning mode for the future learning. It is another new learning mode after the digital learning and a new hot spot in the field of the educational technology. Under the background of the popularization of the concept of the lifelong learning, China’s continuing education is also facing the significant opportunities for transformation and development. Based on the characteristics of the continuing education students, the previous education model has been unable to meet the requirements of the continuing education students for the diversification of the learning forms at any time, anywhere, and at the same time, it has restricted the students’ freedom and initiatives in their learning.
Project Work Division in Agile Distributed Student Teams - Who Develops What?

This paper focuses on studying how distributed student teams organize their project work on a software product and how is their work division related to their architectural choices and geographical locations. We present our findings based on the data collected during five years of the long-lasting Distributed Software Development course running among three European universities. Student team decisions are analyzed on 19 distributed projects, carried out using the Scrum framework, and students' insights into this topic are discussed based on the data obtained from students' questionnaires and other course-related data sources. In addition, we investigate how this work division is related to the team dynamics, by analyzing teams' collaboration patterns and collaboration links between different team locations and work roles. The results show that although the students, in general, perceive the strong positive influence of architecture to the work division, younger generations of students tend to move away from dividing the work by location in order to form the sub-teams mixed by location, thus even improving the performance of their projects. A balanced collaboration, regardless of the physical location of sub-teams, might have a more beneficial effect on the quality of the resulting work than a strict work division over a distance.
The Application of Big Data Mining Prediction Based on Improved K-Means Algorithm

In order to solve the problem of low efficiency of K-Means algorithm in processing the data mining prediction problem of big data with more attributes, an annual income prediction method of residents based on improved K-Means algorithm is proposed. The improved K-Means algorithm combines the principal component analysis method with the traditional K-Means algorithm. After reducing the dimensionality of various data attributes, the data are classified with K-Means algorithm. The research makes use of 1994 U.S. census database and conducts a contrastive analysis of the two algorithms. The results show that the prediction accuracy has been significantly improved by 13.3313%, from 53.1016% to 66.4329%. It is clear the improved algorithm can effectively improve the accuracy of clustering and annual income prediction.
Domain Adaptation by Stacked Local Constraint Auto-Encoder Learning

Domain adaptation (DA), a particular case of transfer learning, is an effective technology for learning a discriminative model in scenarios where the data from the training (source) and the testing (target) domains share common class labels but follow different distributions. The differences between domains, called domain shifts, are caused by variations in the acquisition devices and environmental conditions, such as changing illuminations, pose, and collecting-device noises, that are related to a specific domain, denoted as domain-specific noises in this paper. The research on stacked denoising autoencoder (SDA) has demonstrated that noise-robust features could be learned through training a model to reduce the man-made (simulated) noises. However, little research has been conducted to learn the domain-invariant features through training SDA to reduce the domain-specific noises from the real word. In this paper, we propose a novel variant of SDA for DA, called the stacked local constraint auto-encoder (SLC-AE), which aims to learn domain-invariant features through iteratively optimizing the SDA and the low-dimensional manifold. The core idea behind the SLC-AE is that both the source and target samples are corrupted due to the domain-specific noises, and each corrupted sample could be de-noised by calculating the weighted sum of its neighbor samples defined on the intrinsic manifold. Because the neighbor samples on the intrinsic manifold are semantically similar, their weighted sum preserves the generic information and reduces the domain-specific noises. To properly evaluate the performance of the SLC-AE, we conducted extensive experiments using seven benchmark data sets, i.e., MNIST, USPS, COIL20, SYN SIGNS, GTSRB, MSRC and VOC 2007. Compared to twelve different state-of-the-art methods, the experimental results demonstrated that the proposed SLC-AE model made significant improvement over the performance of SDA and achieved the best average performance on the seven data sets.
Wrangling Rogues

The Rogues Gallery is a new experimental testbed that is focused on tackling rogue architectures for the post-Moore era of computing. While some of these devices have roots in the embedded and high-performance computing spaces, managing current and emerging technologies provides a challenge for system administration that are not always foreseen in traditional data center environments.
An Interactive Visual Analytics System for Incremental Classification Based on Semi-supervised Topic Modeling

Text labeling for classification is a time-consuming and unintuitive process. Given an unannotated text collection, it is difficult for users to determine what label to create and how to label the initial training set for classification. Thus, we present an interactive visual analytics system for incremental text classification based on a semi-supervised topic modeling method, modified Gibbs sampling maximum entropy discrimination latent Dirichlet allocation (Gibbs MedLDA). Given a text collection, Gibbs MedLDA generates topics as a summary of the text collection. We design a scatter plot to display documents and topics simultaneously to show the topic information, and this helps users explore the text collection structurally and find labels for creating. After labeling documents, Gibbs MedLDA is applied to the text collection with labels again, and it generates both the topic and classification information. We also provide a scatter plot with the classifier boundary and a matrix view to present weights of classifiers. Users can iteratively label documents to refine each classifier. We evaluate our system via a user study with a benchmark corpus for text classification and case studies with two unannotated text collections.
Time-Invariant 3D Human Action Recognition with Positive and Negative Movement Memory using Convolutional Neural Networks

Developing time-invariant solutions for recognition of human action is still an important and open challenge. Three issues make time-invariant solutions so important: different speed of performing the same action by different people, latency in doing the actions and the existence of redundant frames in the recorded video. To overcome these problems, we propose a method based on the so-called memory of the joints to remember only the cumulative positive and negative movement of each joint. Hence, we transform action recognition from time-space to shape-space and the action recognition becomes the problem of shape classification. These shape features contain highly discriminative information and are robust against execution rate. After extracting these features, convolutional neural networks are used to classify the actions. UTKinect and UTD_MHAD skeleton dataset are used for testing this method and the results demonstrate the effectiveness of the proposed method.
Truncated Gradient Confidence-Weighted Based Online Learning for Imbalance Streaming Data

Online learning for imbalanced streaming data is an important and challenging problem for many classification tasks in the machine learning research field. Traditional online learning algorithms are mainly focused on classification tasks with balanced data, and with little consideration about the characteristics of imbalanced streaming data. In this paper, we propose a novel online learning algorithm called Truncated Gradient Confidence-Weighted (TGCW), which integrate the truncated gradient algorithm with the confidence weighted algorithm together to improve the feature selection ability while reducing the dimensions of imbalanced streaming data effectively. We study a number of classification tasks with various imbalance data ratio including the pedestrian detection application and compare the performance of the TGCW algorithm with traditional online learning algorithms, and empirical results show that the TGCW algorithm can achieve better performance consistently than other baseline approaches.
Content-adaptive selective steganographer detection via embedding probability estimation deep networks

Steganographer detection is to detect culprit users, who attempt to hide confidential information with steganography, among many innocent users. By incorporating the knowledge of true embedding probability map that illustrates the probability distribution of embedding messages in the corresponding image, content-adaptive steganography and steganalysis have made great progress. Unfortunately, true embedding probability map is inappropriate for steganographer detection method due to the significant challenges that the steganographic algorithm and the embedding payload are usually unknown in the task of steganographer detection. In this paper, we propose a novel content-adaptive selective steganographer detection method incorporated with learning-based embedding probability estimation. The embedding probability estimation is first formulated as a pixel-wise segmentation and recognition problem and is integrated into multi-class dilated residual learning model to extract the discriminative features. In the end, the steganographer is identified by local factor outlier with the selective strategy. Extensive experiments demonstrate that the estimated embedding probability map shows robustness against different steganographic algorithms and different payloads. From our experiments, we also find that the proposed content-adaptive selective steganographer detection framework integrated by the estimated embedding probability map achieves low detection error rates in both spatial and frequency domains.
Reconstruct as Far as You Can: Consensus of Non-Rigid Reconstruction from Feasible Regions

Much progress has been made for non-rigid structure from motion (NRSfM) during the last two decades, which made it possible to provide reasonable solutions for synthetically-created benchmark data. In order to utilize these NRSfM techniques in more realistic situations, however, we are now facing two important problems that must be solved: First, general scenes contain complex deformations as well as multiple objects, which violates the usual assumptions of previous NRSfM proposals. Second, there are many unreconstructable regions in the video, either because of the discontinued tracks of 2D trajectories or those regions static towards the camera, which require careful manipulations. In this paper, we show that a consensus-based reconstruction framework can handle these issues effectively. Even though the entire scene is complex, its parts usually have simpler deformations, and even though there are some unreconstructable parts, they can be weeded out to reduce their harmful effect on the entire reconstruction. The main difficulty of this approach lies in identifying appropriate parts, however, it can be effectively avoided by sampling parts stochastically and then aggregate their reconstructions afterwards. Experimental results show that the proposed method renews the state-of-the-art for popular benchmark data under much harsher environments, i.e., narrow camera view ranges, and it can reconstruct video-based real-world data effectively for as many areas as it can without an elaborated user input.
Variational Learning for Finite Generalized Inverted Dirichlet Mixture Models with a Component Splitting Approach

Mixture models play a crucial role in pattern recognition methods based on clustering. In this paper, we propose a finite generalized inverted Dirichlet mixture model with a variational learning method for parameter estimation. The highlight of our model is the component splitting approach which handles the problem of model selection in an incremental fashion within the variational framework. Efficiency of proposed model is tested for image categorization tasks.
Planetary Rover Path Planning Based on Improved A* Algorithm

Developing a space rover with ability to explore robustly and autonomously the unknown outer space landscape like Moon and Mars has always been a major challenge, since the first roving remote-controlled robot, Lunokhod 1, landed on the moon. Path planning is one of important task when the rover travels a certain distance without the human control. To traverse safely on the harsh and complex planetary surface, the terrain environmental information and the ability of the roving vehicle to overcome obstacles should be taken into account. In this paper, an improved A* algorithm is proposed via introducing both the environmental characteristics (such as surface slope and surface toughness) and the rover’s traversability as the constraint conditions. Comparison of the performance of the proposed A* algorithm relative to the original A* algorithm is conducted based on MATLAB platform. Numerical simulations indicate that the improved A* algorithm has shorter path and higher planning success rate.
A Weights and Improved Adaptive Artificial Fish Swarm Algorithm for Path Planning

A weight and improved adaptive artificial fish swarm algorithm(WIA-AFSA) is proposed to deal with the problem of mobile robots path planning have low optimization accuracy and premature convergence under real environment. Firstly, introduced an improved aggregation degree factor to obtain an adaptive step and visual strategy, which can reflect the actual changes in the optimal state of the artificial fish swarm search, and better balance the global and local search capabilities. At the same time, the weight evaluation factor is introduced in the pray, swarm and follow behavior of the artificial fish, which effectively avoids the algorithm falling into the local optimum and premature. The benchmark function is used to test the performance of the algorithm. The results show that the algorithm has good searching ability and convergence. Simulation experiments of path planning based on raster model were carried out to verify the superiority of wia-afsa algorithm in robot navigation. Finally, the path planning experiment of the robot in real environment proves the effectiveness and practicability of the proposed algorithm.
The State of Prototyping Practice in the Industrial Setting: Potential, Challenges and Implications

Abstract Prototyping as a central method within innovation- and product development processes has a high acceptance in industry. Various prototyping tools provide impressive visualizations of product ideas in early development stages and especially low fidelity prototyping methods seem easily applicable. However, a closer look at prototyping in practice reveals a number of misunderstandings and barriers regarding effective prototyping, often related to different stakeholders’ (e.g., developer, designer, client) ideas about the purpose of prototyping. Based on a combination of literature analysis, adapting existing models and methods in user centered design (e.g., personas, double diamond design process) and empirical results from industrial research cooperation, we introduce a focus group format and a first model of prototyping maturity, which can help organizations to reflect on their state of practice in prototyping on an individual, team, and organizational level. The maturity model also forms a valuable theoretical lens for design research. Thus, our research aims at researchers in the field of prototyping as well as practitioners involved in prototyping and innovation processes.
Image-to-Tree: A Tree-Structured Decoder for Image Captioning

Automatically generating natural language descriptions of images is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In recent years tremendous success has been shown in image captioning under the encoder-decoder framework, in which decoders are often chain-structured with Recurrent Neural Networks(RNNs), treating sentences as sequences. However, natural sentences are not inherently linear structures, but hierarchical structures. In this paper, we for the first time proposed a model with tree-structured decoder for image captioning(Image-to-Tree), which does not directly generate sentences but instead explicitly generates their dependency trees in a top-down manner. Inspired by the success of attention mechanism in image captioning, we also proposed a corresponding attention-based model for Image-to-Tree. Experiments on MSCOCO dataset demonstrate that our model can achieve comparable results to chain-structured models of different language metrics.