Reviewerly/exHarmony huggingface dataset:
Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Feature extraction and matching is a key component in image stitching and a critical step in advancing image reconstructions, machine vision and robotic perception algorithms. This paper presents a fast and robust underwater image mosaicking system based on (2D)2PCA and A-KAZE key-points extraction and optimal seam-line methods. The system utilizes image enhancement as a preprocessing step to improve quality and allow for greater keyframe extraction and matching performance, leading to better quality mosaicking. The application focus of this paper is underwater imaging and it demonstrates the suitability of the developed system in advanced underwater reconstructions. The results show that the proposed method can address the problems of noise, mismatching and quality issues which are typically found in underwater image datasets. The results demonstrate the proposed method as scale-invariant and show improvements in terms of processing speed and system robustness over other methods found in the literature.
The Application of Artificial Intelligence Technology in the Quality Evaluation of Dance Multimedia Teaching in Higher Vocational Colleges

Abstract Through scientific and effective evaluation of dance classroom teaching quality, teachers can objectively and accurately evaluate teaching methods, attitudes and effects, and lay a cognitive foundation for the improvement of dance teaching quality. The purpose of this article is to evaluate the teaching quality of dance multimedia in vocational colleges based on artificial intelligence technology. The application of BP neural network in teaching evaluation and the algorithm of BP neural network teaching quality evaluation model are studied, and the construction of dance classroom teaching quality evaluation system is discussed. First, the same teacher uses the same teaching materials and uses different teaching methods to teach the students in the two classes respectively, and secondly, an interest survey on dance classes. The experimental results show that multimedia teaching has brought great convenience to teachers in preparing lessons and improved their professional level. In addition, the use of courseware to demonstrate actions in teaching avoids the negative impact on student presentations due to differences in teacher age, technology, and personal understanding. Compared with the control class, the experimental class increased the density of free practice by 10.93%, and the heart rate increased by 8 times / min.
Generating attentive goals for prioritized hindsight reinforcement learning

Typical reinforcement learning (RL) performs a single task and does not scale to problems in which an agent must perform multiple tasks, such as moving a robot arm to different locations. The multi-goal framework extends typical RL using a goal-conditional value function and policy, whereby the agent pursues different goals in different episodes. By treating a virtual goal as the desired one, and frequently giving the agent rewards, hindsight experience replay has achieved promising results in the sparse-reward setting of multi-goal RL. However, these virtual goals are uniformly sampled after the replay state from experiences, regardless of their significance. We propose a novel prioritized hindsight model for multi-goal RL in which the agent is provided with more valuable goals, as measured by the expected temporal-difference (TD) error. An attentive goals generation (AGG) network, which consists of temporal convolutions, multi-head dot product attentions, and a last-attention network, is structured to generate the virtual goals to replay. The AGG network is trained by following the gradient of TD-error calculated by an actor–critic model, and generates goals to maximize the expected TD-error with replay transitions. The whole network is fully differentiable and can be learned in an end-to-end manner. The proposed method is evaluated on several robotic manipulating tasks and demonstrates improved sample efficiency and performance.
Robust and Fast 3-D Saliency Mapping for Industrial Modeling Applications

New generation 3-D scanning technologies are expected to create a revolution at the Industry 4.0, facilitating a large number of virtual manufacturing tools and systems. Such applications require the accurate representation of physical objects and/or systems achieved through saliency estimation mechanisms that identify certain areas of the 3-D model, leading to a meaningful and easier to analyze representation of a 3-D object. 3-D saliency mapping is, therefore, guiding the selection of feature locations and is adopted in a large number of low-level 3-D processing applications including denoising, compression, simplification, and registration. In this article, we propose a robust and fast method for creating 3-D saliency maps that accurately identifies sharp and small-scale geometric features in various industrial 3-D models. An extensive experimental study using a large number of 3-D scanned and CAD models verifies the effectiveness of the proposed method as compared to other recent and relevant approaches despite the constraints posed by complex geometry patterns or the presence of noise.
IMAGE ANALYSIS FOR AUTOMATIC ENUMERATION OF RBC INFECTED WITH PLASMODIUM PARASITES-IMPLICATIONS FOR MALARIA DIAGNOSIS

In this paper, we show that the power function of sub E-function f n (x) is sub E-function.Furthermore, we establish some new integral inequalities of Hadamard type involving sub E-functions and concave E-functions.
Identifying expertise through semantic modeling: A modified BBPSO algorithm for the reviewer assignment problem

Reviewers play a significant role in academic peer review activities, including conference paper assignment and funding selection, because their evaluation of proposals impacts the final decision. Several studies have proposed reviewer selection strategies or reviewer evaluation methods for solving the problem of selecting appropriate reviewers. Identifying reviewers who are familiar with the proposals to be reviewed is the objective of the reviewer assignment problem. However, the majority of the existing studies ignore quantitative constraints with respect to the articles assigned to the reviewers during the review process. In this study, we propose a novel optimization model with several review condition constraints to address the reviewer assignment problem. In the proposed model, the expertise and research areas of the candidate reviewers and proposals are identified using semantic topic models, which are demonstrated to be effective when measuring the relevance of the reviewers with respect to the proposals to be reviewed; further, the computational efficiency is improved owing to the reduced representation dimensionality. Herein, an improved heuristic algorithm is proposed to match reviewers and papers based on specific topic areas, and candidate reviewers are assigned to each proposal under the global optimum condition based on their overall performance values. Subsequently, an empirical test is conducted using a conference reviewer dataset. The obtained results show that the proposed model can help the managers to efficiently and effectively select reviewers in terms of the convergence rate and convergence level when compared with several classic benchmarks.
Learning ordered pooling weights in image classification

Spatial pooling is an important step in computer vision systems like Convolutional Neural Networks or the Bag-of-Words method. The spatial pooling purpose is to combine neighbouring descriptors to obtain a single descriptor for a given region (local or global). The resultant combined vector must be as discriminant as possible, in other words, must contain relevant information, while removing irrelevant and confusing details. Maximum and average are the most common aggregation functions used in the pooling step. To improve the aggregation of relevant information without degrading their discriminative power for image classification, we introduce a simple but effective scheme based on Ordered Weighted Average (OWA) aggregation operators. We present a method to learn the weights of the OWA aggregation operator in a Bag-of-Words framework and in Convolutional Neural Networks, and provide an extensive evaluation showing that OWA based pooling outperforms classical aggregation operators.
Analisis Penggunaan Media Gambar Berseri Untuk Meningkatkan Kemampuan Membaca Siswa Disleksia Di Sekolsh Dasar

Abstract&#x0D; &#x0D; Children with special needs require different learning services with children in general. One kind of children with special needs is a child who has difficulty in learning and it is called dyslexia. Generally, children with dyslexia have some difficulties in learning and one of it is difficulty reading. The study aims to analyze the use of radiant images as media in reading for Dyslexia children. The subject in this study is Dyslexia students grade IV at SDN Repok Puyung. The methods used in this study is qualitative descriptive. Data collection was conducted through reading and observation tests. Data presented in the form of diagrams, then analyzed descriptively. The main problem in this research is whether the serial image media can help Dyslexia students in improving their reading skill. The results showed that the use of serial image media could improve their reading skill. The increasing of reading skills as reaction for memory improvement of Dyslexia student through pictures. Other findings in this study are improvement in learning activity and reading motivation for Dyslexia students.
Speculative text mining for document-level sentiment classification

Many existing solutions perform document-level sentiment classification based on local document only, ignoring other texts that might contribute to better classification accuracy. In this paper, we propose a novel speculative sentiment classification model named SSC. In SSC, we speculate that users with similar rating behaviours are more likely to write documents of similar sentiments toward a product. The motivation of SSC, therefore, is to exploit those speculative similar documents for improving classification accuracy. The proposed SSC model consists of three main components, namely, user-product interaction (UPI) component, document encoding (DE) component, and speculative similar document (SSD) component. The UPI component models user-product interactions, and encodes user/product ratings behaviours into user/product embeddings. The DE component utilizes learned user/product embeddings to capture the informative word vectors for comprising more accurate document representations. The SSD component aggregates documents written by similar users toward the same product for speculative sentiment classification. Because the user similarities are calculated based on user embeddings that encode user rating behaviours, the aggregated documents are more likely to have similar sentiments. The three components are seamlessly integrated into a unified model. In the unified manner, these three components are jointly optimized, and they mutually complement each other to enhance sentiment classification. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed SSC model over state-of-the-art baselines.
Entity-Enriched Neural Models for Clinical Question Answering

We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time.We enable this by learning to predict logical forms as an auxiliary task along with the main task of answer span detection.The predicted logical forms also serve as a rationale for the answer.Further, we also incorporate medical entity information in these models via the ERNIE (Zhang et al., 2019a) architecture.We train our models on the large-scale emrQA dataset and observe that our multi-task entity-enriched models generalize to paraphrased questions ∼ 5% better than the baseline BERT model.
Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)

Multiple-Choice Question Answering (MCQA) is the most challenging area of Machine Reading Comprehension (MRC) and Question Answering (QA), since it not only requires natural language understanding, but also problem-solving techniques. We propose a novel method, Wrong Answer Ensemble (WAE), which can be applied to various MCQA tasks easily. To improve performance of MCQA tasks, humans intuitively exclude unlikely options to solve the MCQA problem. Mimicking this strategy, we train our model with the wrong answer loss and correct answer loss to generalize the features of our model, and exclude likely but wrong options. An experiment on a dialogue-based examination dataset shows the effectiveness of our approach. Our method improves the results on a fine-tuned transformer by 2.7%.
Trembling triggers: exploring the sensitivity of backdoors in DNN-based face recognition

Abstract Backdoor attacks against supervised machine learning methods seek to modify the training samples in such a way that, at inference time, the presence of a specific pattern (trigger) in the input data causes misclassifications to a target class chosen by the adversary. Successful backdoor attacks have been presented in particular for face recognition systems based on deep neural networks (DNNs). These attacks were evaluated for identical triggers at training and inference time. However, the vulnerability to backdoor attacks in practice crucially depends on the sensitivity of the backdoored classifier to approximate trigger inputs. To assess this, we study the response of a backdoored DNN for face recognition to trigger signals that have been transformed with typical image processing operators of varying strength. Results for different kinds of geometric and color transformations suggest that in particular geometric misplacements and partial occlusions of the trigger limit the effectiveness of the backdoor attacks considered. Moreover, our analysis reveals that the spatial interaction of the trigger with the subject’s face affects the success of the attack. Experiments with physical triggers inserted in live acquisitions validate the observed response of the DNN when triggers are inserted digitally.
An Adaptive Thresholding Algorithm-Based Optical Character Recognition System for Information Extraction in Complex Images

Extracting texts from images with complex backgrounds is a major challenge today.Many existing Optical Character Recognition (OCR) systems could not handle this problem.As reported in the literature, some existing methods that can handle the problem still encounter major difficulties with extracting texts from images with sharp varying contours, touching word and skewed words from scanned documents and images with such complex backgrounds.There is, therefore, a need for new methods that could easily and efficiently extract texts from these images with complex backgrounds, which is the primary reason for this work.This study collected image data and investigated the processes involved in image processing and the techniques applied for data segmentation.It employed an adaptive thresholding algorithm to the selected images to properly segment text characters from the image's complex background.It then used Tesseract, a machine learning product, to extract the text from the image file.The images used were coloured images sourced from the internet with different formats like jpg, png, webp and different resolutions.A custom adaptive algorithm was applied to the images to unify their complex backgrounds.This algorithm leveraged on the Gaussian thresholding algorithm.The algorithm differs from the conventional Gaussian algorithm as it dynamically generated the blocksize to apply threshing to the image.This ensured that, unlike conventional image segmentation, images were processed area-wise (in pixels) as specified by the algorithm at each instance.The system was implemented using Python 3.6 programming language.Experimentation involved fifty different images with complex backgrounds.The results showed that the system was able to extract English character-based texts from images with complex backgrounds with 69.7% word-level accuracy and 81.9% character-level accuracy.The proposed method in this study proved to be more efficient as it outperformed the existing methods in terms of the character level percentage accuracy.
Leveraging BERT with Mixup for Sentence Classification (Student Abstract)

Good generalization capability is an important quality of well-trained and robust neural networks. However, networks usually struggle when faced with samples outside the training distribution. Mixup is a technique that improves generalization, reduces memorization, and increases adversarial robustness. We apply a variant of Mixup called Manifold Mixup to the sentence classification problem, and present the results along with an ablation study. Our methodology outperforms CNN, LSTM, and vanilla BERT models in generalization.
Becoming Linguistically Mature: Modeling English and German Children’s Writing Development Across School Grades

In this paper we employ a novel approach to advancing our understanding of the development of writing in English and German children across school grades using classification tasks.The data used come from two recently compiled corpora: The English data come from the the GiC corpus (983 school children in second-, sixth-, ninth-and eleventh-grade) and the German data are from the FD-LEX corpus (930 school children in fifth-and ninthgrade).The key to this paper is the combined use of what we refer to as 'complexity contours', i.e. series of measurements that capture the progression of linguistic complexity within a text, and Recurrent Neural Network (RNN) classifiers that adequately capture the sequential information in those contours.Our experiments demonstrate that RNN classifiers trained on complexity contours achieve higher classification accuracy than one trained on text-average complexity scores.In a second step, we determine the relative importance of the features from four distinct categories through a Sensitivity-Based Pruning approach.
JLVEA: Lightweight Real-Time Video Stream Encryption Algorithm for Internet of Things

Along with the recent growth of Internet of Things (IoT) security camera market, there have been a number of personal information leakage incidents from security attacks targeting such cameras. Therefore, a permutation-based video encryption algorithm was proposed to secure video streams in low-performance processors such as IoT security cameras. However, existing permutation-based video encryption algorithms are vulnerable to known-plaintext attacks since they use the same permutation list for every frame. Moreover, situation deduction based on the color composition is possible. In this paper, we propose a new permutation-based video encryption algorithm that updates the permutation list for every frame using a crypto secure pseudo-random number generator without significantly increasing memory usage. By doing so, the algorithm becomes robust to known-plaintext attacks, which has been a common problem with existing permutation-based video encryption algorithms. In addition, color channel separation can prevent attackers from deducing situations through color composition. Pre-compression encryption is applied to make the algorithm robust to data loss because of packet loss. We implement the proposed algorithm and conduct an experiment to show its performance in terms of probability of data loss because of packet loss, encryption speed, and memory usage.
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems

Neural networks of ads systems usually take input from multiple resources, e.g., query-ad relevance, ad features and user portraits. These inputs are encoded into one-hot or multi-hot binary features, with typically only a tiny fraction of nonzero feature values per example. Deep learning models in online advertising industries can have terabyte-scale parameters that do not fit in the GPU memory nor the CPU main memory on a computing node. For example, a sponsored online advertising system can contain more than $10^{11}$ sparse features, making the neural network a massive model with around 10 TB parameters. In this paper, we introduce a distributed GPU hierarchical parameter server for massive scale deep learning ads systems. We propose a hierarchical workflow that utilizes GPU High-Bandwidth Memory, CPU main memory and SSD as 3-layer hierarchical storage. All the neural network training computations are contained in GPUs. Extensive experiments on real-world data confirm the effectiveness and the scalability of the proposed system. A 4-node hierarchical GPU parameter server can train a model more than 2X faster than a 150-node in-memory distributed parameter server in an MPI cluster. In addition, the price-performance ratio of our proposed system is 4-9 times better than an MPI-cluster solution.
Generating Explanations for Temporal Logic Planner Decisions

Although temporal logic has been touted as a fruitful language for specifying interpretable agent objectives, there has been little emphasis on generating explanations for agents with temporal logic objectives. In this paper, we develop an approach to generating explanations for the behavior of agents planning with several temporal logic objectives. We focus on agents operating in deterministic Markov decision processes (MDPs), and specify objectives using linear temporal logic (LTL). Given an agent planning to maximally satisfy some set of LTL objectives (with an associated preference structure) in a deterministic MDP, we introduce an algorithm for constructing explanations answering both factual and “why” queries, which queries are also specified in LTL.
Optimal quantum search on truncated simplex lattices

First-order truncated simplex lattices have been used as interesting data structures to explore the properties of quantum search such as the effect of connectivity on search speed [D. A. Meyer and T. G. Wong, Phys. Rev. Lett. 114, 110503 (2015)]. In this paper, we further discuss quantum search algorithms for truncated simplex lattices. We first propose a multistage quantum algorithm for an $r\mathrm{th}$-order truncated simplex lattice with $N$ vertices based on the numerical calculation up to the fifth-order lattice, which requires an $(r+1)$-stage search process and consumes a run time $\mathrm{\ensuremath{\Theta}}({N}^{(2r+1)/(2r+2)})$ in general. Furthermore, with edge weights suitably adjusted (which increases the connectivity according to certain definitions), we merge the multistage search process into a single stage and achieve a fast quantum search algorithm with an optimal run time $\mathrm{\ensuremath{\Theta}}(\sqrt{N})$ for first-order truncated simplex lattices, which we conjecture is generally true for any-order lattice. Under small noise of the lattice structure, both our multistage and our single-stage search algorithms are quite robust.