Reviewerly/exHarmony huggingface dataset:
On the Efficacy of Deep Image Denoising for Computer Vision Applications

Image denoising is a process of inverse reconstruction where the original image is reconstructed from its noisy observations. Several deep learning models have been developed for image denoising. Usually, the performance of image denoising is measured by metrics like structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), however in this paper, we take a more pragmatic approach. We design and conduct experiments to evaluate the performance of deep image denoising methods in terms of improving the performance of some popular computer vision (CV) algorithms after image denoising. In this paper, we have comparatively analyzed: fast and flexible denoising (FFDNet) convolution neural network (CNN), feed forward denoising CNN (DnCNN), and deep image prior (DIP)-based image denoising. CV algorithms experimented with are face detection, face recognition, and object detection. Standard and augmented datasets were used in our experiments. Various types and amounts of noise were added to raw images from standard datasets (BSDS500, LFW, FDDB, and WGSID). We may conclude from our findings that image denoising does not improve the performance of CV algorithms when applied to raw images of datasets. But image denoising is very effective in improving the performance of the CV methods when denoising is applied to noise corrupted images of the datasets. In our experiments, we found results where the improvements were up to 11.70% in terms of accuracy for the face detection experiment.
Introducing students to out-of-distribution detection with deep neural networks

In the past ten years, deep learning has transformed data science, engineering, and even art in countless ways. The key challenge for training engineering students in deep learning is to provide hands-on topics, in which students can apply their problem-solving skills. Out-of-distribution detection is a subtle problem vexing real-world applications, when a deep neural network is faced with an object of a previously unseen class. It provides a challenging playground for students to explore different approaches, applying mathematics and statistics, while ultimately gaining a deeper understanding of deep learning models. In this paper, we review several starting points to explore this application area, including experiments on CIFAR-IO datasets.
Cost Estimate Migration for Crystal Reports

Currently, we are experiencing great advances in various fields of technology such as hardware and software. These advances propose various challenges in software engineering in order to face the maintainability and support of current systems. The cases of software that become obsoleted by this accelerated process of evolution involves increasingly complex procedural challenges of migrations and updates. A clear example are reporting systems which are an essential support for companies. It is important to build solutions and tools that leverage efficient migration process. In this paper, we present a model-based approach to cover the migration scenario of reports from Crystal to Oracle Publisher. The approach relies on three pillars: i) a view with the results of a function point analysis that aims at facilitating planning task; ii) a structural view of the reports that allows developers to restructure them and see the migration progress status; and iii) the actual transformation from Crystal to Publisher. An evaluation of the approach on a real dataset shows their advantages in term of developers’ productivity and report performance improvements.
ADNet: Rethinking the Shrunk Polygon-Based Approach in Scene Text Detection

To localize text regions and separate close instances, the shrunk polygon is widely used in recent scene text detection methods. However, there exist two problems: 1) Existing methods fail to consider the aspect ratio sensitive problem when reconstructing the text instance from shrunk polygon. 2) Texts with extreme aspect ratios will lead to the fracture of shrunk polygons. To handle these two problems, in this paper, we propose a novel Adaptive Dilation Network (ADNet) to focus on the reconstruction process from shrunk polygon, which aims to provide a tight and complete text representation. Firstly, instead of using a fixed dilation factor, ADNet uses an aspect ratio-wise dilation factor to reconstruct the text region from shrunk polygon for each text instance. Such an instance-wise dilation factor considers the scale correlation between the original and shrunk polygon, and thus can guide an adaptive text region reconstruction for texts with large aspect ratio variance. Secondly, to deal with the fracture of detection results, a new Efficient Spatial Relationship Module (ESRM) is devised to capture long-range dependencies with low computation cost. ESRM uses a novel Weighted Pooling to reduce the resolution of feature maps without much information loss. Compared with the existing methods, ADNet further explores the potential of shrunk polygon-based approaches and obtains excellent detection results at an impressive speed. Extensive experiments on several datasets (Total-Text, CTW1500, MSRA-TD500 and ICDAR2015) verify the superiority of our method.
Fusion Subspace Clustering for Incomplete Data

This paper introduces fusion subspace clustering, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize the distance between the subspaces of all data, so that subspaces of the same cluster get fused together. Our method allows low, high, and even full-rank data; it directly accounts for noise, and its sample complexity approaches the information-theoretic limit. In addition, our approach provides a natural model selection clusterpath, and a direct completion method. We give convergence guarantees, analyze computational complexity, and show through extensive experiments on real and synthetic data that our approach performs comparably to the state-of-the-art with complete data, and dramatically better if data is missing.
Clustering-Enhanced Knowledge Graph Embedding

Knowledge graph embedding (KGE) is a task to transform the symbolic entities and relations in Knowledge Graphs (KGs) into low-dimensional vectors, which facilitates the use of KGs in downstream applications. However, most existing models ignore the semantic correlations among similar entities and relations. Indeed, we find there exist semantically similarities in entities or relations. To take advantage of these semantic correlations, we utilize the entity cluster and relation cluster information to enhance traditional KGE models. Particularly, we first use an unsupervised clustering algorithm to get the entity and relation clusters. Then we decompose the representation of the entities or relations into two parts, one is from the entities or relations themselves, and the other is from the cluster information. By this way, the entities or relations can make full use of both the common information of their similar entities or relations and the unique information from themselves. Extensive experiments on popular benchmarks demonstrate the effectiveness of the proposed models.
Video Based Sentiment Analysis

It is generally said, that our inner emotions and feelings are mirrored on our faces. For better communication, Face Expressions Recognition plays a vital role and can be used in the study of human behaviour and psychological characteristics. In the domain of Machine Learning, this concept of reading and understanding human expressions falls into the category of Cognitive Systems and the study of the thought process of humans by understanding the human behaviour and emotions using the Machine Learning Algorithms is known as Cognitive Science.The technology of recognition of facial expression is basically a Sentiment Analysis tool that can detect six universal expressions: “happiness”, “anger”, “sadness”, “surprise”, “fear” and “disgust”. In this paper, the Sentiment analysis of a prerecorded video has been done using the algorithm of Face Emotion Recognition (FER) to depict the intensity of an emotion. The goal is to identify the emotions present in the pre-recorded video (input data) in order to emulate the human thought process. The approach used in this paper to do the video sentiment analysis is Statistical Approach which uses the algorithms of Machine Learning and Deep Learning such as CNN to detect the sentiments precisely. The result is visible in the form of graphs and it depicts various sentiment intensities. We can see every emotion plotted against time in the graph. In addition, the output is a replication of the original video with a box around the detected face exhibiting real emotions within the video.
Visual Recommendation and Visual Search for Fashion E-Commerce

Recommender systems are historically one of the most successfull and widely known applications of AI, personalized suggestions are nowadays a valuable commercial application of such systems. Many papers have been published in this field, but it is not yet solved; these models still lack state of the art multi-modal capabilities, such as conversational or visual suggestions. In this contribution we present a novel Visual Recommendation module for fashion e-commerces capable of recommending items based on a concept of visual similarity, and a Visual Search module where users can upload a picture of some clothing and search for the most similar ones in a given e-commerce. In conclusion we discuss about the accessibility of these recommender systems for small and medium enterprises, briefly describing our idea of Recommendations-as-a-Service.
Image-Attention based User Entity Recognition of Device

In this study, we investigate a method for identifying the attribution of domain user based on web page information. In the process of using domains, users or network administrators usually use the method of querying the domain registry or open portal website when detecting the real organization of the domain. However, because many organizations rely on the services provided by cloud service providers and domain service providers, The user obtained by the traditional query method is often the registration user of the domain, not the user of the domain. Named entity recognition, as an important branch of natural language processing, usually only processes and recognizes information through text. However, the information provided by the webpage contains both text and images, and many images also contain the name of the user to which the domain belongs. In the research of this paper, using the text information in the website and the user information in the image, a multi-modal fusion method is proposed to identify the user to which the domain belongs.
Ground Confluence and Strong Commutation Modulo Alpha-Equivalence in Nominal Rewriting

Nominal rewriting was introduced as an extension of first-order term rewriting by a binding mechanism based on the nominal approach. A distinctive feature of nominal rewriting is that $$\alpha $$ -equivalence is not implicitly dealt with at the meta-level but explicitly dealt with at the object-level. In this paper, we introduce the notion of strong commutation modulo $$\alpha $$ -equivalence and give a sufficient condition for it. Using the condition, we present a new criterion for confluence modulo $$\alpha $$ -equivalence (on ground terms) of possibly non-terminating left-linear nominal rewriting systems.
Text Edges Guided Network for Historical Document Super Resolution

Super-resolution aims to increase the resolution and the clarity of the details in low-resolution images, and document images are no exception. Although significant improvements have been achieved in super-resolution for different domains, historical document images have not been addressed well. Most of the current works in the text domain deal with modern fonts and rely on extracting prior semantic information from a recognizer to super-resolve images. The absence of a reliable handwritten recognizer for Arabic documents, where historical documents have a complex structure and overlapping parts, makes these text-domain works inapplicable. This paper presents a Text-Attention-ed Super Resolution GAN (TASR-GAN) to address this problem. The model deals with historical Arabic documents and does not rely on prior semantic information. Since our input domain documents, text edges are essential for quality and readability; thus, we introduce a new loss function called text edge loss. This loss function provides more attention and weight to text edge information and guides through optimization to super-resolve images with accurate small regions' details and fine edges to improve image quality. Experiments on six Arabic manuscripts show that the proposed TASR achieves state-of-the-art performance in terms of PSNR/SSIM metrics and significantly improves the visual image quality, mainly the edges of small regions details, and eliminates artifacts noises. Also, a grid search experiment has been conducted to tune the best hyperparameters values for our text edge loss function.
TCU-Net: Transformer and Convolutional Neural Network-Based Advanced U-Net for Concealed Object Detection

Camouflaged object detection (COD) seeks to find concealed objects hidden in natural surroundings. COD is challenging since it has to distinguish intrinsic similarities between foreground objects and background surroundings, unlike salient object detection. Convolutional neural network (CNN)-based approaches are proposed to overcome this challenge. However, they have inherent limitations in modeling and extracting global contexts. Although Transformer-based approaches are proposed to tackle this problem, which can maintain the semantic features of input images, they have limitations in learning localized spatial features in the limited receptive field. Therefore, one of the main challenges is to conduct accurate and robust COD while maintaining global contexts without sacrificing low-level contexts. This study proposes a novel concealed object detection and segmentation method using Transformer and CNN-based advanced U-Net (TCU-Net). TCU-Net can extract globalized semantic features using the Swin Transformer-based encoder and localized spatial features using the attentive inception decoder. In particular, multi-dilated residual (MDR) blocks connecting the encoder and decoder generate refined multi-level features to improve discriminability. Finally, the attentive inception decoder generates the final camouflaged object mask by maintaining the localized spatial information. Instead of simple up-sampling of the feature map, the attentive inception decoder conducts cascaded deconvolution through inception and attention modules. A weighted hybrid loss function is used for optimizing the model, consisting of the binary cross entropy (BCE) and intersection over union (IoU) losses. We comprehensively compared the proposed TCU-Net with previous studies by analyzing different metrics based on four public datasets, such as CAMO, CHAMELEON, COD10K, and NC4K. An ablation study was also conducted to evaluate network architectures and loss functions to verify advantages of the proposed approach. Experimental analysis on public datasets proves that the proposed TCU-Net outperforms previous approaches.
PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators

Recently low-precision deep learning accelerators (DLAs) have become popular due to their advantages in chip area and energy consumption, yet the low-precision quantized models on these DLAs bring in severe accuracy degradation. One way to achieve both high accuracy and efficient inference is to deploy high-precision neural networks on low-precision DLAs, which is rarely studied. In this paper, we propose the PArallel Low-precision Quantization (PalQuant) method that approximates high-precision computations via learning parallel low-precision representations from scratch. In addition, we present a novel cyclic shuffle module to boost the cross-group information communication between parallel low-precision groups. Extensive experiments demonstrate that PalQuant has superior performance to state-of-the-art quantization methods in both accuracy and inference speed, e.g., for ResNet-18 network quantization, PalQuant can obtain 0.52% higher accuracy and 1.78 $$\times $$ speedup simultaneously over their 4-bit counter-part on a state-of-the-art 2-bit accelerator. Code is available at https://github.com/huqinghao/PalQuant .
Assessing expectations and potential of domain independent corporate learning chatbots

Advancing technological developments in Natural Language Processing have led to a new generation of conversional agents for various application domains. A special challenge in the area of education is to handle the different domains of knowledge in order to enable domain independent learning with chatbots. To enable a future development of such a system, we investigate the different expectations of users and their satisfaction when learning with a chatbot through a Wizard-of-Oz study. Subsequently, we identified and categorized intentions of learners. The study shows high acceptance and willingness to further interact with the system. Technical requirements can be derived from our results to enable a domain-independent chatbot for corporate e-learning in the future.
A Visible Watermarking Approach Likely to Steganography Using Nonlinear Approach

Digital watermarking is the course of action of set in information into digital content. Authenticity or integrity is one of the key features of watermarking. Perceptibility or existence of information on cover signal depends on nature of watermarking process. In this effort, a massive amount of message may embed in cooperation with nonlinear pixel and bit positions of image. Without embedding any specific position, data embedded to entire image stating from an arbitrary position chosen by the secret key. The number of effected pixels of carrier image is proportionally got higher with the level of message. Choice of pixel position entirely depends on the size of message. At decryption era, without inspect all of the pixels of cover image, data bits are composed from that special pixel and bit positions. With embedding of large amount of information, sometimes it produces some visible scrap to the carrier. Though it’s visible to us but it’s still secure to us, as it’s embedded through some key to some nonlinear pixel positions. This approach is highly convinced the visible watermarking approach as well as the steganography approach also.
A Type System with Subtyping for WebAssembly’s Stack Polymorphism

We propose a new type system for WebAssembly. It is a refinement of the type system from the language specification and is based on type qualifiers and subtyping. In the WebAssembly specification, a typable instruction sequence gets many different types, depending in particular on whether it contains instructions such as $$\boldsymbol{\textsf{br}}$$ (unconditional branch) that are stack-polymorphic in an unusual way. But one cannot single out a canonical type for a typable instruction sequence satisfactorily. We introduce qualifiers on code types to distinguish between the two flavors of stack polymorphism that occur in WebAssembly and a subtyping relation on such qualified types. Our type system gives every typable instruction sequence a canonical type that is principal. We show that the new type system is in a precise relationship to the type system given in the WebAssembly specification. In addition, we describe a typed functional-style big-step semantics based on this new type system underpinned by an indexed graded monad and prove that it prevents stack-manipulation related runtime errors. We have formalized our type system, inference algorithm, and semantics in Agda.
Searching for a Consensus Agreement for Multilateral Multi-Issue Automated Negotiation

In this paper, we propose a multilateral multi-issue automated negotiation model that aims to search for a consensus agreement. Multilateral multi-issue automated negotiation is more complicated compared with bilateral negotiations. However, most of the existing multilateral negotiations are still the form of bilateral negotiations. This method usually performs with low transaction efficiency and high cost and cannot meet the complicated scenario requirements, in which multiple agents seek a consensus agreement. Therefore, we propose a multilateral multi-issue automated negotiation model based on the multi-dimensional Pareto front. This method takes the pareto optimality of negotiation offers, which can effectively solve the multilateral multi-issue automated negotiation problems.
Feature Space Curvature Map: A Method To Homogenize Cluster Densities

The majority of density-based clustering algorithms can not perform properly when data expose very different density through the feature space. These algorithms implicitly presume that all clusters almost have the same density, therefore, they normally use global parameters. Consequently, they are often biased towards finding dense clusters in front of sparse ones. In this paper, we propose a parametric multilinear transformation method to homogenize cluster densities while preserving the topological structure of the dataset. The transformed clusters have approximately the same density while all inter-cluster regions become globally low-density. In our method, the feature space is locally bent by dense data point concentrations the same way as stars bend the space-time dimensions in Theory of Relativity. We present a new Gravitational Self-organization Map to model the feature space curvature by plugging the concepts of gravity and fabric of space into the Self-organization Map algorithm to mathematically describe the density structure of the data. To homogenize the cluster density, we introduce a novel mapping mechanism to project the data from a non-Euclidean curved space to a new Euclidean flat space. Specifically, this mechanism transfers the basis vectors instead of the feature vectors to guarantee the continuity of the mapping function and optimize the computation cost of the algorithm. As a result, our method can efficiently and explicitly homogenize the density of any dataset globally to then apply existing clustering algorithms without modification. Our experimental results over both real-world and synthetic datasets show that our approach outperforms the current statistical-based methods.
Brain MRI Noise Reduction Using Convolutional Autoencoder

The Magnetic resonance imaging (MRI) machine will add random artifacts like intensity inhomogeneity, Gaussian noise, and Rician noise, while the image acquisition process. Due to hardware imperfection (magnetic fields, etc.), body motion during scanning, thermal noise, weak signal intensity (which causes low signal-to-noise ratio), etc. noises are present in the image. It is very difficult to diagnose of brain disorder if the MR image is corrected by Rician noise. In the past two decades, various algorithms have been proposed with different noise reduction performances. Recently machine learning and deep learning architectures outperform most of the conventional denoising algorithms. The Convolutional Neural Network (CNN) based residual learning architectures are showing promising performance in MRI noise reduction. The proposed algorithm makes use of convolutional neural network-based rician noise reduction using Augmented Autoencoder architecture, which increases the denoising performance with boosted MR image sample size. In particular, batch normalization and residual learning are applied to enhance noise reduction performance. To validate the proposed research work, tested two sets of MRI data from the Kaggle database. The proposed augmented autoencoder produced promising results at high rician noise levels and showed better performance over state of art deep learning architectures. The performance is measured in terms of Mean square error, Peak Signal to Noise Ratio, and Structural Similarity Index Measurements.