Reviewerly/exHarmony huggingface dataset:
Local and Global Search-Based Planning for Object Rearrangement in Clutter

We propose two algorithms based on local and global searches for a Task and Motion Planning (TAMP) problem, which considers a robotic manipulator to rearrange obstacles and grasp a target in clutter.In the problem, no collision-free path for a robotic manipulator is available unless some obstacles blocking a target are relocated.The two algorithms determine the sequence of obstacles to be removed for grasping a target without collisions.The local search algorithm determines the sequence quickly in an online manner but could be suboptimal in the number of removed obstacles.The global search algorithm based on tree search needs upfront computation but runs in polynomial time.In numerical simulation settings, we consider objects in various shapes, which could make reachable directions bounded.From the simulations the planning time of local search algorithm is faster than that of global search, whereas the global search algorithm removes the less number of obstacles than the local search.In addition, we show that the global search algorithm with a heuristic cost is faster than without the cost but the minimum number of removed obstacles is still obtained from the global search algorithm without the heuristic cost.Practical experiments show the applicability of our algorithms in real environments
Unsteady Flow Field Prediction: Understanding the Dynamics in an Intuitive Physics Way

The prediction of unsteady flow field is a challenge in the area of fluid dynamics due to the spatiotemporally dynamic behavior. Given high-dimensional spatial information of the unsteady flow field for a number of previous time steps, the subsequent multi-step or even multi-cycle information is to be obtained. Inspired by human intuitive physics, we investigate a purely visual approach for unsteady flow field prediction directly from images which are obtained by mapping physical quantities to the color space, and propose a novel stochastic latent variable model based on deep neural networks. Our model is evaluated on two numerical simulation datasets of different complexities in terms of conditions and scopes of the flow field. The results show that trained with two-cycle data, over ten-cycle future flow fields are accurately predicted by our model on the low-complexity dataset. For the high-complexity one, our model also gives accurate multi-cycle predictions after trained with data of more cycles.
Augmenting Images with a Mid-Processing Unit to Enhance Classification Accuracy

Expression recognition is a challenging task. This paper aims to improve upon the accuracy of an existing Machine Learning classification system, with no-retraining of the existing model, by augmenting the images to improve the classification accuracy. A Mid-Processing Unit is used to manipulate data from the first pass of the classifier, this enhances the original image and improves the overall accuracy result. Three, dimensional reduction algorithms are explored as methods to augment the images; Principal Component Analysis, T-distributed Stochastic Neighbour Embedding, and Non-Negative Matrix Factorisation. Facial Landmarks are also explored as an additional data source. Two phased testing was used; 1. to identify which method combination most improved accuracy, and 2. to fine tune the applied weight to the original images. The final results showed that T-distributed Stochastic Neighbour Embedding in combination with a weight set to 0.024, achieved an almost 1% increase in classifier accuracy.
Learning to Zoom Inside Camera Imaging Pipeline

Existing single image super-resolution methods are either designed for synthetic data, or for real data but in the RGB-to-RGB or the RAW-to-RGB domain. This paper proposes to zoom an image from RAW to RAW inside the camera imaging pipeline. The RAW-to-RAW domain closes the gap between the ideal and the real degradation models. It also excludes the image signal processing pipeline, which refocuses the model learning onto the super-resolution. To these ends, we design a method that receives a low-resolution RAW as the input and estimates the desired higher-resolution RAW jointly with the degradation model. In our method, two convolutional neural networks are learned to constrain the high-resolution image and the degradation model in lower-dimensional subspaces. This subspace constraint converts the ill-posed SISR problem to a well-posed one. To demonstrate the superiority of the proposed method and the RAW-to-RAW domain, we conduct evaluations on the RealSR and the SR-RAW datasets. The results show that our method performs superiorly over the state-of-the-arts both qualitatively and quantitatively, and it also generalizes well and enables zero-shot transfer across different sensors.
Fuzzy Utility Mining on Temporal Data

Compared with traditional high-utility itemset mining (HUIM) algorithms, the fuzzy utility mining (FUM) algorithm not only considers the quantity and the unit utility of an itemset but also provides a comprehensible way of transforming quantity into several semantic terms. However, in real-life applications, items often have time attributes, such as seasonal vegetables, weather information, and accidents, for which the HUIM and FUM algorithms do not work very well. Especially in large cities, traffic congestion always occurs during the peak time. This case will seriously lower the roads' usage. Hence, researchers proposed the temporal-based fuzzy utility itemset mining (TFUIM) technology to analyze these temporal data. In this paper, we first develop a novel list-based TFUIM algorithm, namely FUMT. The newly designed temporal fuzzy-list structure compresses all key information about temporal fuzzy itemsets to reduce the number of database scans. In addition, according to the list structure, FUMT is also a one-phase algorithm, which avoids the generate-and-test paradigm. Finally, extensive experiments show that the novel algorithm performs better than the state-of-the-art algorithms in terms of runtime and memory consumption on sparse and dense datasets.
Integrating Title and Citation Context Semantics of Citing Paper via Weighted Attentions for Local Citation Recommendation

Citing comprehensively and appropriately has become a challenging task with the rapid growth of academic publications. Citation recommendation helps alleviate the burden of finding relevant and appropriate citations by recommending a list of academic papers for a given text. Existing approaches tried to determine whether a paper should be cited by measuring its relevance or similarity to a given citation circumstance. Citation circumstance modeling should consider many factors, such as the content of the citing paper, citation context words, citation network, and so on. However, most models only focus on citation context words and learn representations of citing paper through citation networks, putting little attention on citing paper content. In this paper, we regard the title as a summative and informative sentence relative to paper content and propose a novel model based on Bidirectional Gated Recurrent Units (BiGRUs) and Attentions. Our approach uses sequential embedding of paper title words for paper semantic representation and models citation circumstances by integrating citing paper title and citation context. Moreover, we introduce a semantic weight parameter to distinguish the importance of academic papers and citation contexts. Experiments on the ACL anthology network dataset show that our approach outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG criteria.
Differential Privacy Mechanisms: A State-of-the-Art Survey

The advantage of digitization is the availability of enormous data that make decision-making efficient and accurate. However, the data that improve the decision-making create a wide range of privacy concerns for users. The privacy-preserving data analysis is becoming a crucial research topic in the domain of computer science. One of the popular procedure used to ensure the privacy of data is anonymization where the identifiable information related to the users are removed before using the data for analysis. However, there are several issues associated with anonymization. In this article, we discuss the differential privacy mechanisms used to ensure the privacy of data. We formally discuss the definition of differential privacy, and then provide the seminal algorithms in the domain of differential privacy that enables the privacy-preserving data analysis. We discuss the applications of differential privacy. In addition, we present the state-of-the-art issues (or research gaps) in the domain of differential privacy and provide the future research directions.
Mobile Teaching of College Tourism English Based on Android Platform

In order to better implement the mobile teaching of college Tourism English, this paper will focus on the Android platform to carry out relevant research, mainly discussing the basic concepts of mobile teaching of college tourism English and the Android platform environment, and then put forward the mobile platform design scheme of college Tourism English, and finally put forward the teaching strategy. Using the strategies in this paper, mobile teaching can be better realized in the Android platform environment, and the English level of college students can be improved through tourism.
Learning the Space of Deep Models

Embedding of large but redundant data, such as images or text, in a hierarchy of lower-dimensional spaces is one of the key features of representation learning approaches, which nowadays provide state-of-the-art solutions to problems once believed hard or impossible to solve. In this work <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , in a plot twist with a strong meta aftertaste, we show how trained deep models are as redundant as the data they are optimized to process, and how it is therefore possible to use deep learning models to embed deep learning models. In particular, we show that it is possible to use representation learning to learn a fixed-size, low-dimensional embedding space of trained deep models and that such space can be explored by interpolation or optimization to attain ready-to-use models. We find that it is possible to learn an embedding space of multiple instances of the same architecture and of multiple architectures. We address image classification and neural representation of signals, showing how our embedding space can be learnt so as to capture the notions of performance and 3D shape, respectively. In the Multi-Architecture setting we also show how an embedding trained only on a subset of architectures can learn to generate already-trained instances of architectures it never sees instantiated at training time.
EG-Four$\mathbb {Q}$: An Embedded GPU-Based Efficient ECC Cryptography Accelerator for Edge Computing

With the continuous development of Industry 4.0 technology, the embedded devices in Industrial Internet of Things (IIoT) are showing explosive growth, and large-scale cyber attacks or related security incidents continue to sound the alarm bell of information security. IIoT has strict requirements on computing performance and energy consumption, which poses severe challenges to cryptographic algorithms, especially public key cryptographic algorithms with high computational complexity. Embedded graphic processing unit (GPU) devices, always as edge computing nodes or AI accelerators, are widely deployed in IIoT applications. In this article, we propose an embedded GPU-based Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> (EG-Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> ) elliptic curve public key cryptographic acceleration scheme. As far as we know, EG-Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> is the first work to completely implement Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> on the GPU platforms, including finite field operations, point arithmetic, and scalar multiplication. Relying only on 36-W power consumption, our scalar multiplication performance reaches 1717 kops/s with the latency of 2.38 ms. In terms of the energy-efficiency ratio, EG-Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> has significant advantages over other platforms such as advanced RISC machines (ARM) CPU, Intel CPU, field programmable gate array (FPGA), and desktop GPUs. The throughput of EG-Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> is 1.75 times that of the fastest elliptic curve cryptography implementation based on the same platform and even exceeds the performance of Intel top server CPU E5-2699v3 (18-core). Based on the embedded GPU Xavier, EG-Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> can act as a cryptographic edge computing module or even a cloud cryptographic accelerator, providing more efficient elliptic curve cryptographic services for IIoT.
Underwater Image Enhancement Based on Feature Fusion Generative Adversaral Networks

Aiming at the problems of low contrast and color distortion of underwater images, an underwater image enhancement algorithm based on feature fusion generative adversarial networks is proposed. Firstly, color correction algorithm is applied to the underwater degraded image, and then the feature of color corrected image is extracted by convolution neural network. Secondly, the feature of underwater degraded image can be extracted by the feature extraction network which is based on U-Net, and then fuse it with the feature of color correction image. Finally, the convolution neural network is used to reconstruct the fusion feature to the enhanced image. The experimental results on Underwater-ImageNet dataset show that the algorithm can effectively improve the contrast and clarity of underwater degraded image, and the enhanced image color is more realistic. Compared with other algorithms on the Underwater-ImageNet dataset, the underwater image quality measure (UIQE) is increased by 0.308, the natural image quality evaluator (NIQE) is reduced by 0.638. The contrast and sharpness of the enhanced underwater image are improved and the colors are more realistic.
Convolutional Neural Network-BO Based Feature Extraction and Multi-Layer Neural Network-SR Based Classification for Facial Expression Recognition

Facial expression recognition has been more essential in artificial machine intelligence systems in recent years. Recognizing facial expressions automatically has constantly been considered as a challenging task since people significantly vary the way of exhibiting their facial expressions. Numerous researchers established diverse approaches to analyze the facial expressions automatically but there arise few imprecision issues during facial recognition. To address such shortcomings, our proposed approach recognizes the facial expressions of humans in an effective manner. The suggested method is divided into three stages: pre-processing, feature extraction, and classification. The inputs are pre-processed at the initial stage and CNN-BO algorithm is used to extract the best feature in the feature extraction step. Then the extracted feature is provided to the classification stage where MNN-SR algorithm is employed in classifying the face expression as joyful, miserable, normal, annoyance, astonished and frightened. Also, the parameters are tuned effectively to obtain high recognition accuracy. In addition to this, the performances of the proposed approach are computed by employing three various datasets namely; CMU/VASC, Caltech faces 1999, JAFFE and XM 2 VTS. The performance of the proposed system is calculated and comparative analysis is made with few other existing approaches and its concluded that the proposed method provides superior performance with optimal recognition rate.
UX Definitions: The Missing Components and the Need for the Context and Time of Use

There are several definitions for user experience (UX). However, this study unveiled that there are missing components in those definitions. An analysis of user experience definitions shows that the definitions had the elements of time of use and context of use absent in them. These missing components limit and obscure the understanding of user experience. Nevertheless, the inclusion of these components improves the specificity, robustness, uniqueness, and meaningfulness of UX and will provide illumination for the conceptualization, design, evaluation, and modeling of UX. As UX is understood the way it is defined, an all-encompassing definition that comprises the components of time and context of use will therefore be worthwhile. This study reviewed extant related literature and unraveled these gaps and suggested their inclusion in user experience definitions.
Exploit Domain Knowledge: Smarter Abductive Learning and Its Application to Math Word Problems

Perception and reasoning are two representative abilities of intelligence, and Abductive Learning (ABL) is targeted at unifying the two abilities in a mutually beneficial way, where the neural model learns to perceive the primitive logic facts from raw data, and logical reasoning can exploit domain knowledge to correct the wrongly perceived facts for training the neural model. However, there are three flaws in ABL: (1) While logical reasoning corrects the wrongly perceived facts, the corrected facts may contain redundancy. (2) Some complex facts are hard to correct, therefore no supervision can be provided by the corresponding samples in dataset. (3) The correction of logical reasoning induces a large number of spurious facts, which degrades the performance of the model. In this paper, we focus on these issues and argue that the lack of overall exploitation of domain knowledge is the root cause. We propose a Smarter Abductive Learning (S-ABL) framework. In this framework, we first sum up some extra knowledge and propose three mechanisms via extra knowledge, namely simplification, filtration, augmentation. We exploit the extra knowledge to simplify, filter and augment corrected facts to improve the quality and quantity of the corrected facts. The effectiveness of our framework has been verified in the math word problem. We experiment on dataset Math23k and MAWPS. In weak supervision, our method achieves 64.8% and 52.9% accuracy, outperforming the existing baseline by 5.4% points and 7.3% points on dataset Math23k and MAWPS respectively.
Cooperation of Boundary Attention and Negative Matrix L1 Regularization Loss Function for Polyp Segmentation

Most colorectal cancers are caused by colorectal adenomatous polyps, early screening for colonic polyps is of great clinical importance. However, polyps have diverse appearance, such as shape and size; polyps are difficult to distinguish from mucosa. To address these problems, this paper proposes a boundary attention and negative matrix L1 regularization loss function synergistic segmentation method. Firstly, a branch of boundary attention is added to the last detail finding branch of PraNet [16]. Secondly, the inner polyp region of the boundary is complemented using negative matrix L1 regularization iterations. The synergy of the two terms can refine object areas, which will improve the segmentation accuracy. We conducted extensive experimental evaluations on four publicly available datasets, results show that our method is superior to other models. Especially on CVC-ClinicDB dataset, compared with PraNet, Dice is improved by 4.3% and IoU is improved by 5.9%.
Alignment Techniques in Domain-Specific Models

During the requirements specification stage, conceptual descriptions of the Domain Universe (DU) are used through several representations such as glossaries, ontologies and other models. However, a complete representation of a domain is difficult to achieve. Different ways a domain can be conceptualized result in a heterogeneous representation, which often makes consistent description challenging. This work aims to identify techniques used in different projects for domain alignment and their behaviour with a lexical model. In the applied methodology, a search for the domain alignment was carried out in various scientific databases such as IEEE Explore, ACM and Springer. The obtained result showed that tools used to align domain specifications are concentrated in ontologies and glossaries, using techniques with similarities in the semantic, lexical, structural and syntactic fields. When applying semantic and lexical level measures in glossaries called Extended Language Lexicon (LEL) it was obtained the degree of similarity between the different glossaries, at the semantic level the one with the best precision was the Cosine Similarity method, while at the lexical level, the one with the best precision was Jaro Winkler. It is concluded that the application of the techniques independently does not allow the identification of similarities, omissions or correspondences between the domains, so it is necessary to develop a method that contemplates heuristics and integrates the different lexical and semantic measures as a support to improve the completeness and consistency of the domain.
Automatic Detection and Correction of Random Telegraph Signal Artifacts in Earth Observation Images

Satellite optical and infrared images can be degraded by a piecewise-constant random artefact called Random Telegraph Signal (RTS), which is caused by unstable semiconductor defects in the photodetector. In this letter, we aim at proposing new techniques to detect and correct such artefacts in the specific context of push-broom detectors, where some columns exhibit RTS that superimposes with the landscape. Our detector is based on a nonparametric statistical test that compares the distribution of each column to its neighbors. Concerning the RTS correction, we first propose a signal processing method that estimates the levels and jumps of the RTS. Then we propose another method inspired by variational image destriping, which directly estimates the RTS through the use of total variation. Experiments -on Pléiades images with synthetic RTS and on a real SPOT-5 image- show the effectiveness of the proposed techniques.