Reviewerly/exHarmony huggingface dataset:
Pattern Spotting in Historical Documents Using Convolutional Models

Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query. Contrary to object detection, no prior information nor predefined class is given about the query so training a model of the object is not feasible. In this paper, a convolutional neural network approach is proposed to tackle this problem. We use RetinaNet as a feature extractor to obtain multiscale embeddings of the regions of the documents and also for the queries. Experiments conducted on the DocExplore dataset show that our proposal is better at locating patterns and requires less storage for indexing images than the state-of-the-art system, but fails at retrieving multiple pages containing instances of the query.
Abstractive meeting summarization by hierarchical adaptive segmental network learning with multiple revising steps

Abstractive meeting summarization is a challenging problem in natural language understanding, which automatically generates the condensed summary covering the important points in the meeting conversation. However, the existing abstractive summarization works mainly focus on the structured text documents, which may be ineffectively applied to the meeting summarization task due to the lack of modeling the unstructured long-form conversational contents. In this paper, we consider the problem of abstractive meeting summarization from the viewpoint of hierarchical adaptive segmental encoder-decoder network learning. We propose the hierarchical neural encoder based on adaptive recurrent networks to learn the semantic representation of meeting conversation with adaptive conversation segmentation. We then devise a multi-step revising mechanism to refine the learned semantic representation. We finally develop the reinforced decoder network to generate the high-quality summaries for abstractive meeting summarization. We conduct the extensive experiments on the well-known AMI meeting conversation dataset to validate the effectiveness of our proposed method.
A Geometric Approach for Cross-View Human Action Recognition using Deep Learning

In this paper we present an approach for the recognition of human actions which is based on a deep Convolutional Neural Network architecture. More specifically, 3D skeletal joint information is used to create 2D (image) representations. To compensate for potential viewpoint changes, these images are pre-processed using geometric transformations. Then, they are transformed to the spectral domain using well-known transforms. We focus on actions that are close to activities of daily living (ADLs), yet we evaluate our approach using a large-scale action dataset. We cover single-view, cross-view and cross subject cases and thoroughly discuss experimental results and the potential of our approach.
Tabu search for min-max edge crossing in graphs

Graph drawing is a key issue in the field of data analysis, given the ever-growing amount of information available today that require the use of automatic tools to represent it. Graph Drawing Problems (GDP) are hard combinatorial problems whose applications have been widely relevant in fields such as social network analysis and project management. While classically in GDPs the main aesthetic concern is related to the minimization of the total sum of crossing in the graph (min-sum), in this paper we focus on a particular variant of the problem, the Min-Max GDP, consisting in the minimization of the maximum crossing among all egdes. Recently proposed in scientific literature, the Min-Max GDP is a challenging variant of the original min-sum GDP arising in the optimization of VLSI circuits and the design of interactive graph drawing tools. We propose a heuristic algorithm based on the tabu search methodology to obtain high-quality solutions. Extensive experimentation on an established benchmark set with both previous heuristics and optimal solutions shows that our method is able to obtain excellent solutions in short computation time.
High-performance Convolutional Neural Network Accelerator Based on Systolic Arrays and Quantization

In recent years, convolutional neural networks (CNN) has achieved great success in computer vision tasks, such as object detection, face recognition and image classification. With the diversification of CNN models, accelerators that only support a single network can no longer meet the needs of applications. Due to the computational intensiveness of convolution operations, the implementation of CNN on the FPGA platform faces many challenges. In this paper, a convolution unit based on systolic arrays is proposed in the design of CNN accelerator, and the fixed-point quantization method is adopted to save a large amount of storage resources and reduce the required transmission bandwidth, thus improving throughput and power efficiency. The performance density and power efficiency of our design can reach 0.165 GOPS/DSP and 36.3 GOPS/W under 100MHz clock frequency.
Unidimensional continuous-variable quantum key distribution with discrete modulation

In this paper, we proposed an unidimensional continuous-variable quantum key distribution (CV-QKD) protocol with discrete modulation, which waives the necessity in one of the quadrature modulations and further simplified the implementation of the CV-QKD protocol. On the basis of the Heisenberg uncertainty relation, we analyze the boundary between the unphysical and physical region. Besides, we utilize a novel proof approach to achieve a lower bound valid of transmission distance. This scheme shows an available method to further simplified the implementation of the QKD.
Deep Convolutional Neural Network with Dilated Convolution Using Small Size Dataset

Since Alex Krizhevsky won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 competition by building a very intelligent deep convolutional neural network (D-CNNs), more and more researchers have been engaged in the research and development of deep convolutional neural network (D-CNNs). However, recent researches on deep convolutional neural networks are mostly based on ImageNet datasets. The network model based on such a large dataset is mostly blind to increase the number of network layers, ignoring that most data sets in application are far from the order of magnitude of ImageNet datasets. Such deep networks tend to perform poorly in small datasets (CIFAR-10), since deep models are easy to overfitting. In this paper, we've applied some of the more efficient methods that have been proposed in recent years to traditional deep convolutional neural networks. We proposed a modified Alex network and used this model to fit CIFAR-10. By adding Batch Normalization, using Dilated Convolution and replacing Fully Connected layer (FC) with Global Average Pooling (GAP), we achieved 8.6% error rate on CIFAR-10 without severe overfitting. Our results show that the deep CNN can be used to fit small datasets with proper modifications and the results are much better than before.
Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment

3D scan registration is a classical, yet a highly useful problem in the context of 3D sensors such as Kinect and Velodyne. While there are several existing methods, the techniques are usually incremental where adjacent scans are registered first to obtain the initial poses, followed by motion averaging and bundle-adjustment refinement. In this paper, we take a different approach and develop minimal solvers for jointly computing the initial poses of cameras in small loops such as 3-, 4-, and 5-cycles. Note that the classical registration of 2 scans can be done using a minimum of 3 point matches to compute 6 degrees of relative motion. On the other hand, to jointly compute the 3D registrations in n-cycles, we take 2 point matches between the first n-1 consecutive pairs (i.e., Scan 1 & Scan 2, ... , and Scan n-1 & Scan n) and 1 or 2 point matches between Scan 1 and Scan n. Overall, we use 5, 7, and 10 point matches for 3-, 4-, and 5-cycles, and recover 12, 18, and 24 degrees of transformation variables, respectively. Using simulations and real-data we show that the 3D registration using mini n-cycles are computationally efficient, and can provide alternate and better initial poses compared to standard pairwise methods.
Fuzzy associative memories with autoencoding mechanisms

Associative memories constructed and operating in the presence of big data offer an effective way to realize association mechanisms aimed at storing and recalling items. In this study, we develop a logic-driven model of two-level fuzzy associative memories augmented by autoencoding processing. It is composed of two functional modules. The first module of this architecture implements an efficient dimensionality reduction of the original high dimensional data with the use of an autoencoder. This helps achieve storing and completing the recall realized by a logic-oriented associative memory which constitutes the second module of the architecture. The optimization of the association matrices studied in the paper involves both gradient-based learning mechanisms and the algorithms of population-based optimization, i.e., particle swarm optimization (PSO) and differential evolution (DE). A suite of experimental studies is presented to quantify the performance of the proposed approach. Comparative studies are also conducted to show and quantify the advantages of the mechanisms of associative recall and storage augmented by the autoencoding process.
What should I document? A preliminary systematic mapping study into API documentation knowledge

Background: Good API documentation facilitates the development process, improving productivity and quality. While the topic of API documentation quality has been of interest for the last two decades, there have been few studies to map the specific constructs needed to create a good document. In effect, we still need a structured taxonomy that captures such knowledge systematically.Aims: This study reports emerging results of a systematic mapping study. We capture key conclusions from previous studies that assess API documentation quality, and synthesise the results into a single framework.Method: By conducting a systematic review of 21 key works, we have developed a five dimensional taxonomy based on 34 categorised weighted recommendations.Results: All studies utilise field study techniques to arrive at their recommendations, with seven studies employing some form of interview and questionnaire, and four conducting documentation analysis. The taxonomy we synthesise reinforces that usage description details (code snippets, tutorials, and reference documents) are generally highly weighted as helpful in API documentation, in addition to design rationale and presentation.Conclusions: We propose extensions to this study aligned to developer utility for each of the taxonomy's categories.
Deep learning enabled Lagrangian particle trajectory simulation

We introduce a deep learning method to simulate the chaotic motion of particles trapped in the recirculation zone of a buoyancy-opposed flame. The Lagrangian trajectories of particles in flame were captured using a high-speed camera and subsequently reconstructed in a 3-dimensional cylindrical space. These experimentally determined trajectories were next used to train the two most widely adopted deep generative models – the variational autoencoder (VAE) and the generative adversarial net (GAN). The performance of both models was then benchmarked according to statistical analysis performed on both the simulated trajectories and the ground truth, regarding accuracy and generalization criteria. Our results show that the GAN model produces non-repeating trajectories precisely capturing the statistical features of those determined in experiments, as revealed in their spatial-temporal scaling relationship and linear pair-correlation values; whereas the VAE model trained with the same amount of data tends to overfit, producing inferior results grappling with the trade-off between accuracy and generalization criteria. The influence of the size of training data on model performance is also evaluated. This paper concludes with a discussion on the potential utility of the deep learning enabled trajectory simulation.
Blended Agents: Manipulation of Physical Objects within Mixed Reality Environments and Beyond

Mixed reality (MR) environments allow real users and virtual agents to coexist within the same virtually augmented physical space. While tracking of different body parts such as the user's head and hands allows virtual objects to show plausible reactions to actions of the real user, virtual agents only have a very limited influence on their physical environment.
UAV Online Path Planning Based on Improved Genetic Algorithm

When the traditional Genetic Algorithm (GA) is used for unmanned aerial vehicle (UAV) path planning, the shortcomings of local search ability are reflected when the planning time is more urgent. To address this shortcoming, this paper proposes an improved genetic algorithm that limits the new gene's generating region. The generating region of the evolution operator is dynamically adjusted. The algorithm is applied to UAV online path planning for tracking moving target. The simulation results show that the method enhances the local search ability of the algorithm and improves the searching efficiency. UAV online path planning can be completed well with the improved genetic algorithm.
A Comparative Study of Small Object Detection Algorithms

The scenario considered in this paper is detecting small targets using an unmanned aerial vehicle(UAV) equipped with a pan-tilt-zoom(PTZ) camera. We reviewed, compared and modified the state-of-the-art target detection algorithms. Different target detection algorithms have different advantages in terms of accuracy and time consumption. The contributions of this paper are that we obtained pictures using the UAV and created a dataset firstly, then a support vector machine(SVM) based classifier and a YOLO-based Neural Network have been designed to detect targets separately, and both algorithms are evaluated on the test dataset finally. In this paper, the advantages and disadvantages of traditional machine learning methods based on SVM and deep learning methods based on YOLO on target detection are analyzed through a series of experiments. Limited by the target area selection methods, machine learning methods based on SVM do not have a high detection accuracy. The deep learning method based on neural network focus on improving the accuracy of target detection, while slightly inferior in time consumption. In addition, we modified the YOLO network structure for the special scene of small objects detection in this paper. Experiments show that the modified network can reduce the time consumption and improve the accuracy of small target detection. In summary, different types of target detection methods should be selected and modified according to the practical application scenarios.
Open-World Person Re-Identification With Deep Hash Feature Embedding

Most existing person re-identification (re-id) methods are designed based on the artificial closed-set assumption that the probe and gallery identities are exactly overlapped with a small search pool. This leads to poor scalability in real-world applications where the task is often to re-id a small set of target people (i.e., watch-list) among a large search pool with unknown ID overlap, namely, an open-set deployment setting. In this paper, we firstly propose a new person re-id setting called Watch-List based Open-Set (WLOS) person re-id, which is characterised by the above open-set deployment and a watch-list available at the training stage. Then, we address such a under-studied WLOS problem by formulating a novel Task Dedicated Deep Hashing (TDDH) approach which learning a purpose-specific deep hash model particularly for the given target people in an efficient end-to-end manner. Extensive experiments on three large-scale re-id benchmarks are conducted to demonstrate the advantages and superiority of the TDDH over a wide range of the state-of-the-art hashing and re-id methods under the more realistic open-set setting.
A fuzzy cognitive mapping approach for the assessment of public-goods governance in agricultural landscapes

In Europe, growing concerns about the environmental impacts of agriculture have stimulated the development of more efficient governance options to be included in, or to complement, current agri-environmental policies. However, a significant hurdle for the implementation of enhanced policy tools is the difficult adaptation of promising approaches, such as collective contracts and private-based payments for environmental services, to the locally specific, socio-economic and institutional contexts of different European regions. In this study, we apply a participatory approach based on the Fuzzy Cognitive Mapping technique, in assessing different policy mechanisms, including improved monetary incentives and the potential for an enhanced design of agricultural landscape governance. Our analysis specifically assesses the interactions between rural society, public goods and policies under different, locally relevant economic and social scenarios. The study is carried out in the Marchfeld, an intensive agriculture case-study area in Eastern Austria, which features a number of environmental problems. The work is based on a two-year-long process including focus groups, mind mapping and scenario co-development, as well as individual interviews with local stakeholders. The results show that integrating private or public, collective or performance-orientated monetary incentives with other non-monetary mechanisms like farmers' partnerships or enhanced awareness building are evaluated as central to an effective agri-environmental governance system. Moreover, the results highlight that different futures have major effects on the effectiveness of mechanisms: in a purely market-driven context, tools based on collaborations among farmers are likely to be ineffective and monetary incentives are less efficient. On the other hand, positive social pressures and the influence of non-monetary governance initiatives expected in a sustainability-driven scenario are able to catalyse an efficient adoption of environmental-friendly practices, also at lower monetary rates. Based on the results, we discuss the problem of public goods in agricultural landscapes, and the relevance of such ancillary factors as social infrastructure, awareness and marketing, in supporting the effectiveness and feasibility of public-goods 'governance'.
Algorithms and System Architecture for Immediate Personalized News Recommendations

Personalization plays an important role in many services, just as news does. Many studies have examined news personalization algorithms, but few have considered practical environments. This paper provides algorithms and system architecture for generating immediate personalized news in a practical environment. Immediacy means changes in news trends and user interests are reflected in recommended news lists quickly. Since news trends and user interests rapidly change, immediacy is critical in news personalization applications. We develop algorithms and system architecture to realize immediacy. Our algorithms are based on collaborative filtering of user clusters and evaluate news articles using click-through rate and decay scores based on the time elapsed since the user’s last access. Existing studies have not fully discussed system architecture, so a major contribution of this paper is that we demonstrate a system architecture and realize our algorithms and a configuration example implemented on top of Amazon Web Services. We evaluate the proposed method both offline and online. The offline experiments are conducted through a real-world dataset from a commercial news delivery service, and online experiments are conducted via A/B testing on production environments. We confirm the effectiveness of our proposed method and also that our system architecture can operate in large-scale production environments.
String-based Multinomial Naïve Bayes for Emotion Detection among Facebook Diabetes Community

This paper determined the emotions among the online diabetes community using the string-based Multinomial Naïve Bayes algorithm. Facebook posts from official diabetes support groups were crawled, for a total of 15 000 pre-processed posts. Of these, 800 were manually annotated by human experts. The posts were first classified according to Plutchik's wheel of emotions, comprising of eight dominant emotions: anger, sadness, fear, joy, surprised, trust, anticipation and disgust using the NRC Emotion Lexicon (Emolex). The emotion classifications were then refined using string-based Multinomial Naïve Bayes algorithm, with results indicating a 6.3% improvement (i.e. 82% vs. 75.7% for average F-score) when compared to the Emolex-approach, and other machine learning algorithms, namely, Naïve Bayes and Multinomial Naïve Bayes. The higher accuracy in emotion classification reflects the feasibility of our approach. Further analysis also revealed emotions such as joy, fear and sadness to be of the highest frequencies for the diabetes community.
A VLOS Compliance Solution to Ground/Aerial Parcel Delivery Problem

This paper presents an exact solution and a heuristic solution to a UAV-assisted parcel delivery problem, in which UAVs can only be operated in Visual-Line-Of-Sight (VLOS) areas. In our proposed problem, we assume that trucks travel on road networks, and UAVs move in Euclidean spaces and can launch at any locations on roads. We first demonstrate the overview of our exact solution that iterates all permutations of destinations for an optimal delivery route. Given a specific delivery order, an intuitive approach needs to check all possible locations on roads in the VLOS areas and find a globally optimal location for every destination if UAVs are used for delivery. To avoid high computational cost of searching the optimal location at runtime, we propose an advanced index-based alternative, which computes optimal delivery routes in a pre-processing stage. Due to the nature of NP-hard problems, we also propose a heuristic approach that utilizes delivery groups for the proposed problem of practical size. All proposed solutions are evaluated through extensive experiments.
Identifying the most valuable developers using artifact traceability graphs

Finding the most valuable and indispensable developers is a crucial task in software development. We categorize these valuable developers into two categories: connector and maven. A typical connector represents a developer who connects different groups of developers in a large-scale project. Mavens represent the developers who are the sole experts in specific modules of the project.