Reviewerly/exHarmony huggingface dataset:
A comparison of graph-based semi-supervised learning for data augmentation

In supervised learning, the algorithm accuracy usually improves with the size of the labeled dataset used for training the classifier. However, in many real-life scenarios, obtaining enough labeled data is costly or even not possible. In many circumstances, Data Augmentation (DA) techniques are usually employed, generating more labeled data for training machine learning algorithms. The common DA techniques are applied to already labeled data, generating simple variations of this data. For example, for image classification, image samples are rotated, cropped, flipped or other operators to generate variations of input image samples, and keeping their original labels. Other options are using Neural Networks algorithms that create new synthetic data or to employ Semi-supervised Learning (SSL) that label existing unlabeled data. In this paper, we perform a comparison among graph-based semi-supervised learning (GSSL) algorithms to augment the labeled dataset. The main advantage of using GSSL is that we can increase the training set by adding non-annotated images to the training set, therefore, we can benefit from the huge amount of unlabeled data available. Experiments are performed on five datasets for recognition of handwritten digits and letters (MNIST and EMINIST), animals (Dogs vs Cats), clothes (MNIST-Fashion) and remote sensing images (Brazilian Coffee Scenes), in which we compare different possibilities for DA, including the GSSL, Generative Adversarial Networks (GANs) and traditional Image Transformations (IT) applied on input labeled data. We also evaluated the impact of such techniques on different convolutional neural networks (CNN). Results indicate that, although all DA techniques performed well, GSSL was more robust to different image properties, presenting less accuracy variation across datasets.
Fast Variational Inference for Joint Mixed Sparse Graphical Models

Mixed graphical models are widely implemented to capture interactions among different types of variables. To simultaneously learn the topology of multiple mixed graphical models and encourage common structure, people have developed a variational maximum likelihood inference approach, which takes advantage of the log-determinant relaxation. In this article, we further improve the computational efficiency of this method by exploiting the block diagonal structure of the solution. We present a simple necessary and sufficient condition to identify the connected components in the solution, so as to determine the block diagonal structure. Then, utilizing the idea of “divide-and-conquer”, we are able to adapt the joint structural inference problem for multiple related large sparse networks. We illustrate the merits of the proposed algorithm via experimental comparisons in computational speed.
StreamNet: A DAG System with Streaming Graph Computing

To achieve high throughput in the POW based blockchain systems, researchers proposed a series of methods, and DAG is one of the most active and promising fields. We designed and implemented the StreamNet, aiming to engineer a scalable and endurable DAG system. When attaching a new block in the DAG, only two tips are selected. One is the 'parent' tip whose definition is the same as in Conflux; another is using Markov Chain Monte Carlo (MCMC) technique by which the definition is the same as IOTA. We infer a pivotal chain along the path of each epoch in the graph, and a total order of the graph could be calculated without a centralized authority. To scale up, we leveraged the graph streaming property; high transaction validation speed will be achieved even if the DAG is growing. To scale out, we designed the 'direct signal' gossip protocol to help disseminate block updates in the network, such that messages can be passed in the network more efficiently. We implemented our system based on IOTA's reference code (IRI) and ran comprehensive experiments over the different sizes of clusters of multiple network topologies.
Self-driving Deep Learning System based on Depth Image Based Rendering and LiDAR Point Cloud

We present a “Self-driving deep learning system based on depth image based rendering and LiDAR point clouds” in this paper. Using the depth image based rendering (DIBR) to obtain complete point clouds, combining with the color image modified by histogram equalization and optimal profile compression (HEOPC), and further using MobileNet-YOLO neural network to achieve accurate 3D environment detection and identification. Using the DIBR to generate parallax map information, and obtaining the depth image through the conversion formula. Then combining LiDAR point cloud and the depth image to repair the objects in the point cloud image. We combine HEOPC and the accuracy of deep learning to optimize the color image enhancement of the night environment. According to the restored point cloud information, we perform a cutting algorithm to divide the area of interest. Then through MobileNet-YOLO neural network training, to identify the area of interest, including people, cars and bus.
Augmented reality battleship board game with holographic display

Abstract The board game is a game that played on the board, and the Battleship board game is one of the examples. While developing the virtual board game, multi-user interaction is essential as a board game played by more than one people. To enhance the user experience, the board game has been implemented with Augmented Reality (AR) technology. AR can superimpose it into the real-world since it has the capabilities to overlay the virtual game on the top of the real world. However, AR handheld has limited perspective viewing for the viewers to see the battleship game. Therefore, this project aims to develop an AR battleship board game with a holographic display. Three phases have been carried out, first is to study the multi-user interaction for the AR battleship board game and holographic display with a different perspective. Second is to develop the multi-user interaction for AR Battleship board game. Finally, the third phase is to integrate the AR Battleship board game with a pyramid holographic display. The evaluation has been performed, and the result is based on the usability and user acceptance have been recorded. Based on the result, this project has successfully produced AR Battleship board game with holographic display application and significantly enhance the user experience.
SVD of Shot Boundary Detection Based on Accumulative Difference

Shots are the basic unit of video. Shot boundary detection is a pre-task for video semantic analysis and video retrieval. In this paper we propose a new and effective shot boundary detection (SBD) method to detect cut transition (CT) and gradual transition (GT). By introducing the selection mechanism of candidate segment, a large number of non-boundary frames are avoided. We extract the hue-saturation-value (HSV) features of each frame and use singular value decomposition (SVD) to reduce the dimension of the feature space to save computing overhead. In the SBD stage, we propose a scheme based on accumulative distance. By calculating the maximum distance and accumulative distance of the candidate segment, it can be determined whether the shot boundary is included and the type of the boundary. The maximum distance can directly determine the location of the CTs, and the GTs are defined using inverted triangle pattern matching. Experiments on TRECVID 2007 test data and 100 dual-shots movie clips. Compared with the latest solution, the solution has an excellent accuracy and universally applicable for different types of video.
Bi-DAINet: Bi-Directional Discard-Accept-Integrate Network for salient object detection

Recent deep learning-based salient object detection methods have achieved impressive performance by leveraging multi-level convolutional features, which contain informative and complementary saliency cues. However, it remains a major challenge to make full use of multi-scale feature maps and alleviate the influence of redundant information. To address this problem, we propose a Bi-directional Discard-Accept-Integrate Network (Bi-DAINet) for salient object detection. A Discard-Accept-Integrate (DAI) module is exploited to extract meaningful messages from the feature maps and pass it to the subsequent layer. The DAI module discards redundant information of the previous layer, accepts useful information of the current layer, and integrates the remained information from multiple scales. Therefore, noisy responses are suppressed in this process. A bi-directional Convolutional Neural Network (CNN) is designed to enforce messages flowing from low-to-high level and high-to-low level, respectively. We infer a foreground map and a background map by the bi-directional CNN and then fuse them to predict a final saliency map. On one hand, low-level detailed information and high-level semantic feature are potentially integrated; on the other hand, fine-grained saliency cues around the boundary of target objects are explored. An edge-preserving loss function is further employed to ensure the boundary accuracy in prediction results. Extensive experiments on five benchmark datasets demonstrate the effectiveness and robustness of the proposed method.
3D-NVS: A 3D Supervision Approach for Next View Selection

We present a classification-based approach for the best next view selection and show how we can plausibly obtain a supervisory signal for this task. The proposed approach is end-to-end trainable and aims to get the best possible 3D reconstruction quality with an actively selected second view, given a passively chosen initial view. The proposed model consists of two stages: a classifier and a reconstructor network trained directly from ground truth voxels, as opposed to exhaustively selecting ground truth of best pair views. While testing, the proposed method assumes no prior knowledge of the underlying 3D shape for selecting the next best view. We demonstrate the proposed method’s effectiveness via detailed experiments on synthetic and real images and show how it provides improved reconstruction quality than the existing state of the art 3D reconstruction and the next best view prediction techniques.
The existence of primitive normal elements of quadratic forms over finite fields

For [Formula: see text] ([Formula: see text]), denote by [Formula: see text] the finite field of order [Formula: see text] and for a positive integer [Formula: see text], let [Formula: see text] be its extension field of degree [Formula: see text]. We establish a sufficient condition for existence of a primitive normal element [Formula: see text] such that [Formula: see text] is a primitive element, where [Formula: see text], with [Formula: see text] satisfying [Formula: see text] in [Formula: see text].
Big data or not enough? Zeta test reliability and the attribution of <i>Henry VI</i>

Abstract In 2016, the editors of the New Oxford Shakespeare announced that certain Shakespeare plays could be attributed to co-authors, and certain anonymous plays to Shakespeare, on the basis of non-traditional attribution methods known collectively as computational stylistics, or stylometry. This article investigates the efficacy of a key algorithm used to attribute parts of the Henry VI plays to Christopher Marlowe, the Zeta method invented by John Burrows and adapted by Hugh Craig. Zeta, a test widely used in computational stylistics, is described by Gabriel Egan as ‘by some way the most powerful general-purpose authorship tool currently available’. This article offers extensive independent testing of Zeta. Following criticism of the existing method of Zeta analysis, this article introduces a new, statistically sound method for analysing Zeta results. It investigates a claim that the test is 99.9% reliable in differentiating Shakespeare’s style from Marlowe’s. Examining the conditions under which certain authors were ruled in or out of co-authorship of the Henry VI plays, it determines the effect of disparity in data set size on Zeta’s reliability, as well the effect of small data sets. Several test results confirm that Zeta is unduly influenced by genre. The article concludes that in the light of this study, the small canons of most Early Modern dramatists, particularly where they are genre-skewed like Marlowe’s, do not provide enough data for Zeta to be reliable
Ordinary memory processes in the design of referring expressions

How do speakers produce referential descriptions that satisfy addressees’ informational needs during real-time conversation? A recent proposal is that ordinary memory processes can serve as a proxy for the consideration of common ground. But this is only possible if speakers encode and access sufficiently detailed memory representations. We tested this proposal by having speakers describe referents in contexts varying in perceptual similarity to previous contexts in the dialogue. Based on the analysis of a total of 4,817 descriptions from 112 speakers over three experiments, we found little evidence that contextual similarity modulated the informational content of speakers’ descriptions, regardless of whether that similarity was based on configurational cues (Exps. 1 and 2), or on the perceptual experience of interacting with a conversational partner (Exp. 3). In contrast, speakers did modulate their descriptions when their beliefs about the addressee changed, even when the perceptual match between encoding and retrieval contexts was identical. This suggests that the episodic representations accessed during message generation may be too impoverished to serve as an effective proxy for common ground.
CenterNet-Triplets application: surveillance camera illegal management detection

Abstract There are two types of illegal business in city management: out-of-store management and illegal mobile management, we simply refer to as outmanagement and flowmagement. The main way to deal with illegal buessiness is manual management. However, this requires a lot of costs, and it is impossible for city management personnel to conduct 24hour inspections. In order to improve management efficiency and save labor costs, this article uses city surveillance cameras, combined with deep learning object detection methods, to conduct real-time detection of illegal operations. Aiming at the situation where the amount of surveillance video data is small, a data enhancement method is proposed to improve the detection effect. CenterNet-Triplets is a object detection algorithm without anchor boxes. Its backbone network is Hourglass Network. The AP of CenterNet-Triplets on MS-COCO dataset is 47.0%, and it has a good detection speed. Experimental results show that CenterNet-Triplets is suitable as a method for detecting illegal operations in city management.
A Density Peaks Clustering Method Based on Mutual kNN Graph and Shortest Path

Clustering is a major field in machine learning and its aim is to divide the instances into different groups without any previous knowledge. The Density Peaks Clustering(DPC) has been presented in 2014. Although, DPC is effective, however, existing DPC based methods suffer from several issued such as: its sensitivity to the value user-adjustable parameter, using a simple kernel to calculate the local densities that can cause to choosing impropriate centers, and propagating errors which lead from the chain reaction. To tackle these issues in this paper a better method for selecting centers has been applied so that a set of cluster center candidates will be recognized from the dataset and the most proper one will be identified as main centers. Afterward, we generate a mutual kNN graph. The proposed graph is used to create a strong cluster backbone. We assign remaining candidates by computing their shortest path length to main centers then they will be assigned to which center that they have the least shortest path length, we propagate our labels by a graph-based label propagating method, the mutual kNN graph considers the data distribution, prevent form error propagation and can prevent noise points from taking any label.
Teaching Tactics for Color Image Segmentation Using Otsu and Minimum Cross Entropy

The most fundamental step in image processing is image segmentation and it results in revealing enormous information embedded in most widely used RGB color space image. Excellent result is obtained for bi-level thresholding and the exhaustive search for optimal threshold values, to analyze complex images in multilevel thresholding (MLT), is reduced by promising objective functions such as Otsu and minimum cross entropy MCE aided with teaching-learning based optimization metaheuristic algorithm (TLBO). In TLBO, a teacher shares cognizance to a student. The use of only common control parameters and less-specific control parameters in TLBO achieves exploration and exploitation. The efficiency of TLBO is compared with cuckoo search algorithm (CS) at 4,5,6 and 7 threshold levels. Experimental results reveal that optimal output of TLBO is more successful in precise image segmentation and aids in various real time applications.
RefacTutor: An Interactive Tutoring System for Software Refactoring

While software refactoring is considered important to manage software complexity, it is often perceived as difficult and risky by software developers and thus neglected in practice. In this article, we present refacTutor, an interactive tutoring system for promoting software developers’ practical competences in software refactoring. The tutoring system provides immediate feedback to the users regarding the quality of the software design and the functional correctness of the (modified) source code. In particular, after each code modification (refactoring step), the user can review the results of run-time regression tests and compare the actual software design (as-is) with the targeted design (to-be) in order to check quality improvement. For this purpose, structural and behavioral diagrams of the Unified Modeling Language (UML2) representing the as-is software design are automatically reverse-engineered from source code. The to-be UML design diagrams can be pre-specified by the instructor. To demonstrate the technical feasibility of the approach, we provide a browser-based software prototype in Java accompanied by a collection of exercise examples. Moreover, we specify a viewpoint model for software refactoring, allocate exercises to competence levels and describe an exemplary path for teaching and training.
Geophysical implications for the exploration of concealed orogenic gold deposits: A case study in the Sandy Lake and Favourable Lake Archean greenstone belts, Superior Province, Ontario, Canada

Since 2000, gold discovery rates have not reflected the record levels of exploration expenditure, with those discoveries that are made being predominantly in areas of post-mineral cover. Hence, gold exploration has been increasingly focused on concealed exploration targets where geophysics takes on an increasingly important role in the exploration process. Archean orogenic gold deposits are attractive exploration targets that represent a major proportion of global gold resources. Orogenic gold deposits consistently form late during the tectonic and structural evolution of their host terranes, normally during a transition from compressional to transpressional tectonic stress regimes. Thus, for target generation, the late timing of gold mineralization within the structural history of a prospective orogenic belt is crucial, as it means that current geological maps and sections reflect the geometry of the gold deposits at the time of formation unless there has been subsequent orogeny. Lithological contacts between granitic intrusions and volcano-sedimentary greenstone sequences, as well as between volcanic and sedimentary sequences, are important rheological and geochemical trap sites. Lithosphere-scale faults and dilational jogs in second-order faults act as ore-fluid conduits with anticlinal folds as structural traps for ore-fluid infiltration and gold deposition given suitable host rocks and fluid seals. Geophysical exploration for concealed orogenic gold deposits is challenging due to normally subtle petrophysical contrasts between the gold lodes and their host rocks. Most greenstone-hosted lode-gold deposits do not contain strong conductors as they normally contain <5 vol% of disseminated arsenopyrite and pyrite, which are poor conductors, and rarely contain significant quantities of strong conductors such as pyrrhotite. However, pyrrhotite may be a component of rock sequences such as sulfidic shales or cherts adjacent to the orogenic gold lodes. It is proposed that the combination of HTEM and magnetic surveys be used to explore for concealed orogenic gold deposits, using the Favourable Lake and Sandy Lake greenstone belts in the Superior Province, Canada, as a case study. AIP Cole-Cole time-constants, combined with magnetic and EM data, can efficiently detect disseminated pyrite/magnetite at shallow depths, indicating their potential use as vectoring tools for concealed Archean orogenic gold deposits.
Statistical significance calculations for scenarios in visual inference

Statistical inference provides the protocols for conducting rigorous science, but data plots provide the opportunity to discover the unexpected. These disparate endeavours are bridged by visual inference, where a lineup protocol can be employed for statistical testing. Human observers are needed to assess the lineups, typically using a crowd‐sourcing service. This paper describes a new approach for computing statistical significance associated with the results from applying a lineup protocol. It utilizes a Dirichlet distribution to accommodate different levels of visual interest in individual null panels. The suggested procedures facilitate statistical inference for a broader range of data problems.
Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms

Mathematical modeling of language in Artificial Intelligence is of the utmost importance for many research areas and technological applications. Over the last decade, research on text representation has been directed towards the investigation of dense vectors popularly known as word embeddings. In this paper, we propose a cognitive-emotional scoring and representation framework for text based on word embeddings. This representation framework aims to mathematically model the emotional content of words in short free-form text messages, produced by adults in follow-up due to any mental health condition in the outpatient facilities within the Psychiatry Department of Hospital Fundación Jiménez Díaz in Madrid, Spain. Our contribution is a geometrical-topological framework for Sentiment Analysis, that includes a hybrid method that uses a cognitively-based lexicon together with word embeddings to generate graded sentiment scores for words, and a new topological method for clustering dense vector representations in high-dimensional spaces, where points are very sparsely distributed. Our framework is useful in detecting word association topics, emotional scoring patterns, and embedded vectors’ geometrical behavior, which might be useful in understanding language use in this kind of texts. Our proposed scoring system and representation framework might be helpful in studying relations between language and behavior and their use might have a predictive potential to prevent suicide.