Reviewerly/exHarmony huggingface dataset:
DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slide Image Segmentation

Automatic medical image segmentation has wide applications for disease diagnosing. However, it is much more challenging than natural optical image segmentation due to the high-resolution of medical images and the corresponding huge computation cost. The sliding window is a commonly used technique for whole slide image (WSI) segmentation, however, for these methods based on the sliding window, the main drawback is lacking global contextual information for supervision. In this paper, we propose a dual-inputs attention network (denoted as DA-RefineNet) for WSI segmentation, where both local fine-grained information and global coarse information can be efficiently utilized. Sufficient comparative experiments are conducted to evaluate the effectiveness of the proposed method, the results prove that the proposed method can achieve better performance on WSI segmentation compared to methods relying on single-input.
Investigation of tuning control for musical saw based on hit sound feedback motion with an industrial humanod robot

We attempt to construct a novel automation technology based on the hammering task and its sound feedback process with an industrial humanoid robot equipped with an integrated system of vision, sound and dual arm motion. First, we focus on a musical saw to achieve a tuning motion as its basic study because it is considered to be difficult to control a kind of unknown and unclear tool under beginning operation. Second, we discuss its characteristics to develop the tuning motion based on dual arm motion and sound feedback system. Third, we attempt to cooperate on hammering task by right arm and bending task by left arm for the musical saw with estimating its hit sound. As a result, it can be seen that the proposed system using an industrial humanoid robot makes it feasible to achieve sufficient tuning motion for the musical saw such as unknown and unclear tool. Therefore it is demonstrated that the proposed approach is found to be useful to construct the automation technology based on the hammering task and its sound feedback process with an industrial humanoid robot.
You Ought to Look Around: Precise, Large Span Action Detection

For the action localization task, pre-defined action anchors are the cornerstone of mainstream techniques. State-of-the-art models mostly rely on a dense segmenting scheme, where anchors are sampled uniformly over the temporal domain with a predefined set of scales. However, it is not sufficient because action duration varies greatly. Therefore, it is necessary for the anchors or proposals to have a variable receptive field. In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features. In this part, we employ a stronger backbone I3D with SPN (Segment Pyramid Network) instead of C3D to obtain multi-scale features; 2) a simple but useful feature fusion module named LFE (Local Feature Extraction). Compared with the fully connected layer and global average pooling, our LFE model is more advantageous for network to fit and fuse features. 3) a new feature segment aligning method called TPGC (Two Pathway Graph Convolution), which allows one proposal to leverage semantic features of adjacent proposals to update its content and make sure the proposals have a variable receptive field. YOLA add only a small overhead to the baseline network, and is easy to train in an end-to-end manner, running at a speed of 1097 fps. YOLA achieves a mAP of 58.3%, outperforming all existing models including both RGB-based and two stream on THUMOS'14, and achieves competitive results on ActivityNet 1.3.
A novel m-Health system for epidemic tracking and prediction using Big Data and Electronic health record

In modern cities, the investigation of an outbreak using the traditional systems is a time- consuming task that requires collecting and processing complex information in order to track the origin of the disease. The COVID-19 Virus shows the current system limitation. For this reason, establishing an early detection and prediction is essential to halt the spreading of epidemics in an earlier stage. The aim of our work is to design a system capable of detecting and forecast people carrying of an epidemic in the first days of incubation using geolocation of the patient's and big data.
ECCL: Explicit Correlation-Based Convolution Boundary Locator for Moment Localization

Moment localization in videos using natural language refers to finding the most relevant segment from the video with given a query in natural language form. In this paper, we present a new boundary-determining strategy called explicit correlation-based convolution boundary locator (ECCL), which can handle any lengths of videos and moments while leveraging fine-grained matching relationships. In this method, we first train a deep network to obtain the correlation scores between video clips and query statements. Subsequently, with the correlation scores, we utilize a convolution kernel to generate the boundary probability distribution. Finally, the start and end time indexes of the video moment are calculated with an optimization problem. Experiments on two publicly available datasets demonstrate the feasibility of ECCL.
Objects Counting in Videos via Deep Learning and Image Processing

Objects counting in video frames is an important field of research that provides appropriate information about objects and their groups in video streams. An adequate technique to achieve this goal is using digital image/video processing methods with a detection method called CenterN et that is based on a deep learning architecture (i.e., a ConvNet architecture). This paper presents an objects counting method based on a combination of a detection model and optical flow generated between video frames.
Obstacle avoidance dynamic control of manipulator based on space operator algebra

The traditional manipulator obstacle avoidance dynamics control method ignores the acceleration constraints and speed constraints of the joint obstacle avoidance, and judges the equivalent moment of inertia inaccurately, which leads to large fluctuations in the driving torque of the manipulator motor, and high maximum driving torque and acceleration peaks. Therefore, a dynamic control method of manipulator avoiding obstacles based on space operator algebra is proposed. Using the space recursion method, according to the dynamic space operator algebra system, the manipulator dynamics model is obtained, PID parameters are adjusted, and the model is controlled by three-loop closed-loop PID control to obtain the joint torque under different obstacle avoidance states, and calculate the torque feedforward. The channel transfer function enables the moment to act on the joints of the manipulator to achieve the equivalent moment of inertia control target, to identify and finely control the moment parameters, and to plan the obstacle avoidance trajectory of the manipulator. A parallel manipulator was selected for comparison experiments. The results show that the design method reduces the motor torque ripple, the maximum driving torque, and the acceleration peak in the motion trajectory plane, so that the manipulator has better motion smoothness, reduces motor energy loss, and improves dynamic performance.
Leaky Integrator Dynamical Systems and Reachable Sets

Reservoir computers are a fast training variant of recurrent neural networks, excelling at approximation of nonlinear dynamical systems and time series prediction. These machine learning models act as self-organizing nonlinear fading memory filters. While these models benefit from low overall complexity, the matrix computations are a complexity bottleneck. This work applies the controllability matrix of control theory to quickly identify a reduced size replacement reservoir. Given a large, task-effective reservoir matrix, we calculate the rank of the associated controllability matrix. This simple calculation identifies the required rank for a reduced size replacement, resulting in time speed-ups to an already fast deep learning model. Additionally, this rank calculation speaks to the state space reachable set required to model the input data.
Multi-Scale Keypoint Matching

We propose a new hierarchical method to match keypoints by exploiting information across multiple scales. Traditionally, for each keypoint a single scale is detected and the matching process is done in the specific scale. We replace this approach with matching across scale-space. The holistic information from higher scales are used for early rejection of candidates that are far away in the feature space. The more localized and finer details of lower scale are then used to decide between remaining possible points. The proposed multi-scale solution is more consistent with the multi-scale processing that is present in the human visual system and is therefore biologically plausible. We evaluate our method on several datasets and achieve state of the art accuracy, while significantly outperforming others in extraction time.
Augmented Reality in Spain: Heritage Education, Cultural Tourism and Museums

This chapter analyses how Spanish institutions, archaeological sites and publishers in the field of formal education are using Augmented Reality (AR) as an educational tool. After a decade since its arrival in the cultural world of Spain, augmented reality has undergone a process of implementation and settlement characterized by being in the background of activities, exhibitions and by apps with such short life cycles that only a few specific studies have been conducted. The use of AR in some Roman sites in Spain, the Sorolla museum in Madrid and the Alhambra in Granada are examples that are analysed in this chapter based on significant educational theories within the field of Heritage Education.
VOA*: Fast Angle-Based Outlier Detection over High-Dimensional Data Streams

Outlier detection in the high-dimensional data stream is a challenging data mining task. In high-dimensional data, the distance-based measures of outlierness become less effective and unreliable. Angle-based outlier detection ABOD technique was proposed as a more suitable scheme for high-dimensional data. However, ABOD is designed for static datasets and its naive application on a sliding window over data streams will result in poor performance. In this research, we propose two incremental algorithms for fast outlier detection based on an outlier threshold value in high-dimensional data streams: IncrementalVOA and \(VOA^{*}\). IncrementalVOA is a basic incremental algorithm for computing outlier factor of each data point in each window. \(VOA^{*}\) enhances the incremental computation by using a bound-based pruning method and a retrospect-based incremental computation technique. The effectiveness and efficiency of the proposed algorithms are experimentally evaluated on synthetic and real world datasets where \(VOA^{*}\) outperformed other methods.
Towards Avatars for Remote Communication using Mobile Augmented Reality

Social experiences that use handheld mobile Augmented Reality with 6DOF tracking can potentially recreate the dynamics of in-person meetings, while still being more accessible than HMD based solutions. We describe a design space of possible avatars for such a platform along the dimensions of body part visibility and the type of movement. We propose a study that aims to measure the effect of five avatar types on social presence during group collaboration.
Similarity Search, Recommendation and Explainability over Graphs in Different Domains: Social Media, News, and Health Industry

In this tutorial, we provide a rich blend of theory and practice regarding graph algorithms, to deal with challenging issues such as scalability, data noise, and sparsity in recommender systems. We also demonstrate real-life systems that use the graph algorithms for Social Media (http://delab.csd.auth.gr/moviexplain/), News (http://metarec.inf.unibz.it) and Health (https://drugrec.inf.unibz.it) industry along with user studies which were used to evaluate the acceptance of the users for these systems.
A New High Quality Trajectory Tiling Based Hybrid TTS In Real Time

A trajectory tiling based, hybrid TTS is revisited in this study for improving its synthesis performance. A combination of Transformer encoder and RNN based decoder architecture where two-level, at both word and Chinese phonetic alphabet letter levels, linguistic representation is exploited to generate a cogent and smooth speech parameter trajectory. And then a segment candidate lattice is constructed by minimizing the log spectral distortion of mel-spectrograms and RMSE of F0 between the generated trajectory and candidates. Normalized cross-correlation is used to find the best sequence of "wave-form tiles" in the lattice for synthesizing the final speech waveforms. Subjective A/B preference tests show that the new hybrid system outperforms our earlier trajectory-tiling hybrid baseline TTS (67% vs 11%) and the state-of-the-art, real-time TTS system constructed with Tacotron 2 and LPC-Net (56% vs 27%).
A Sparse Local Preserving Projection Method Based On Graph Embedding

Dimensionality reduction plays an important role in pattern classification. In this paper, a robust unsupervised dimensionality reduction method termed robust sparse locality preserving projection with adaptive graph embedding is proposed. Specifically, the proposed method integrates the adaptive graph learning and projection learning into a framework, which can capture the intrinsic locality structure of data and in turn promotes the method to achieve the global optimal projection. To capture the global information of data, a variant PCA term is introduced, which can decrease the information loss during dimensionality reduction. Importantly, a row-sparsity constraint is imposed on the projection to select the most important features for dimensionality reduction, so as to improve the robustness of the proposed method to noises. Extensive experiments are performed on three representative face databases and an object database, which sufficiently validates the superiority of the proposed method in comparison with some state-of-the-art-methods.
Study on the New Models of Music Industry in the Era of AI and Blockchain

Artificial intelligence (AI) technology is developing rapidly with the continuous improvement of the level of information and scientific technology, which has had a profound impact on the entire society. The arrival of the AI era has had a huge impact on the development of technology and business and the entire society. Besides, the blockchain technology has been effectively protecting the legal rights and creative enthusiasm of original musical creators. The business model of the music industry has also changed accordingly. Through AI technology, the music industry has been upgraded and transformed and its traditional business model has been changed.
Compression Artifacts Reduction Using Fusion of Multiple Restoration Networks

Lossy video compression achieves coding gains at the expense of the quality loss of the decoded images. Owing to the success of deep learning techniques, especially convolutional neural networks (CNNs), many compression artifacts reduction (CAR) techniques have been used to significantly improve the quality of decoded images by applying CNNs which are trained to predict the original artifact-free images from the decoded images. Most existing video compression standards control the compression ratio using a quantization parameter (QP), so the quality of the decoded images is strongly QP-dependent. Training individual CNNs for predetermined QPs is one of the common approaches to dealing with different levels of compression artifacts. However, compression artifacts are also dependent on the local characteristics of an image. Therefore, a CNN trained for specific QP cannot fully remove the compression artifacts of all images, even those encoded using the same QP. In this paper, we introduce a pixel-precise network selection network (PNSNet). From multiple reconstructed images obtained using multiple QP-specific CAR networks, PNSNet is trained to find the best CAR network for each pixel. The output of PNSNet is then used as an explicit spatial attention channel for an image fusion network that combines multiple reconstructed images. Experimental results demonstrated that the quality of decoded images can be significantly improved by the proposed multiple CAR network fusion method.
Comparison of Decision Tree C4.5 Algorithm with K-Nearest Neighbor (KNN) Algorithm in Hadith Classification

Previous scholars always made an effort to make various formulations that were used to categorize and calcify hadith. At present, the process of categorization or classification is facilitated by the process of text mining technology. In the study of text mining itself, there are various kinds of tools and methods or algorithms that can be used and also help provide maximum results in the process of mining information from a text. An example is the Decision Tree C4.5 and K-Nearest Neighbor algorithm. Based on that, the author wants to make research and this final project to compare the performance resulting from the classification process of text documents using Decision Tree C4.5 and K-Nearest Neighbor algorithm for the classification of Imam At- Tirmidzi hadith. With this research, it is expected to be knowledgeable about the process of classifying text documents along with the performance of the two algorithms. Based on testing that has been done, the Decision Tree C4.5 algorithm produces an average accuracy value of 70.53% with an average processing time of 0.083 seconds. While the K-Nearest Neighbor algorithm produces an average accuracy value of 66.36% with an average processing time of 0,03 seconds.
Semi-Supervised Perception Augmentation for Aerial Photo Topologies Understanding

Intelligently understanding the sophisticated topological structures from aerial photographs is a useful technique in aerial image analysis. Conventional methods cannot fulfill this task due to the following challenges: 1) the topology number of an aerial photo increases exponentially with the topology size, which requires a fine-grained visual descriptor to discriminatively represent each topology; 2) identifying visually/semantically salient topologies within each aerial photo in a weakly-labeled context, owing to the unaffordable human resources required for pixel-level annotation; and 3) designing a cross-domain knowledge transferal module to augment aerial photo perception, since multi-resolution aerial photos are taken asynchronistically in practice. To handle the above problems, we propose a unified framework to understand aerial photo topologies, focusing on representing each aerial photo by a set of visually/semantically salient topologies based on human visual perception and further employing them for visual categorization. Specifically, we first extract multiple atomic regions from each aerial photo, and thereby graphlets are built to capture the each aerial photo topologically. Then, a weakly-supervised ranking algorithm selects a few semantically salient graphlets by seamlessly encoding multiple image-level attributes. Toward a visualizable and perception-aware framework, we construct gaze shifting path (GSP) by linking the top-ranking graphlets. Finally, we derive the deep GSP representation, and formulate a semi-supervised and cross-domain SVM to partition each aerial photo into multiple categories. The SVM utilizes the global composition from low-resolution counterparts to enhance the deep GSP features from high-resolution aerial photos which are partially-annotated. Extensive visualization results and categorization performance comparisons have demonstrated the competitiveness of our approach.
The Human-Computer Interaction Online Oral English Teaching Mode Based on Moodle Platform

This paper focuses on the study of human-computer interaction online oral English teaching mode based on Moodle platform. The research targets are the two classes of art students from the integrated model of international cooperative education in the author's university. Before the experiment, the students are divided into experimented class and controlled class. The college students' online oral English practice is established based on the Moodle platform. The experimental class have the access to use this platform to practice their oral English, but the controlled class just use the traditional way to study and practice. The experiment result shows that the experimented class students are interested in the human-computer interaction online oral English and have improved their oral English greatly.
QAAN: Question Answering Attention Network for Community Question Classification

Community Question Answering (CQA) provides platforms for users with various backgrounds to obtain information and share knowledge. In recent years, with the rapid development of such online platforms, an enormous amount of archive data has accumulated, it becomes more and more difficult for expert users to identify desirable questions. In order to reduce the proportion of unanswered questions in CQA, facilitate expert users to find the questions they are interested in, question classification becomes an important task of CQA, which aims to assign a newly posted question to a specific preset category. In this paper, we propose a novel question answering attention network (QAAN) for investigating the role of the paired answer of questions for classification. Specifically, QAAN studies the correlation between question and paired answer, taking the questions as the primary part of the question representation, and the answer information is aggregated based on similarity and disparity with the answer. Our experiment is implemented on Yahoo! Answers dataset. The results show that QAAN outperforms all the baseline models.
Integrated Classification and Localization of Targets Using Bayesian Framework In Automotive Radars

Automatic radar based classification of automotive targets, such as pedestrians and cyclist, poses several challenges due to low inter-class variations among different classes and large intra-class variations. Further, different targets required to track in typical automotive scenario can have completely varying dynamics which gets challenging for tracker using conventional state vectors. Compared to state-of-the-art using independent classification and tracking, in this paper, we propose an integrated tracker and classifier leading to a novel Bayesian framework. The tracker’s state vector in the proposed framework not only includes the localization parameters of the targets but is also augmented with the targets’s feature embedding vector. In consequence, the tracker’s performance is optimized due to a better separability of the targets. Furthermore, the classifier’s performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of classifier’s embedding vector.
A Survey on Method Naming Standards: Questions and Responses Artifact

The artifacts of a large (+1100 responses) survey of professional software developers concerning standards for naming source code methods is presented. The artifact consists of the survey questions along with all the responses from participants. The artifact allows other researchers to examine and study the responses to the survey.