Reviewerly/exHarmony huggingface dataset:
Manifold Modeling in Embedded Space: An Interpretable Alternative to Deep Image Prior

Deep image prior (DIP), which uses a deep convolutional network (ConvNet) structure as an image prior, has attracted wide attention in computer vision and machine learning. DIP empirically shows the effectiveness of the ConvNet structures for various image restoration applications. However, why the DIP works so well is still unknown. In addition, the reason why the convolution operation is useful in image reconstruction, or image enhancement is not very clear. This study tackles this ambiguity of ConvNet/DIP by proposing an interpretable approach that divides the convolution into "delay embedding" and "transformation" (i.e., encoder-decoder). Our approach is a simple, but essential, image/tensor modeling method that is closely related to self-similarity. The proposed method is called manifold modeling in embedded space (MMES) since it is implemented using a denoising autoencoder in combination with a multiway delay-embedding transform. In spite of its simplicity, MMES can obtain quite similar results to DIP on image/tensor completion, super-resolution, deconvolution, and denoising. In addition, MMES is proven to be competitive with DIP, as shown in our experiments. These results can also facilitate interpretation/characterization of DIP from the perspective of a "low-dimensional patch-manifold prior."
Concept of a Voice-Enabled Digital Assistant for Predictive Maintenance in Manufacturing

Voice-enabled assistants, such as Alexa and Google Assistant, are among the fastest-growing information technology applications. Their technological foundation matured over the last years and reached a point where new application areas in challenging business environments become a certainty. Maintenance in manufacturing is one of these areas. This paper presents expectations, requirements, and a concept for a voice-enabled digital intelligent assistant that supports maintenance activities. We identified process monitoring, task execution, reporting, problem-solving, and maintenance planning as the key functional modules for an assistant. Realizing them depends on basic, utility, and maintenance functions. Our discussion states that all fundamental technologies and tools to realize an assistant for maintenance exist, but they have constraints. For instance, Speech-to-Text mechanisms lack transparent and performant solutions, and natural language understanding must rely on small datasets, which is challenging. We argue that continuous improvement and systematic evaluation of an assistant prototype is important to create high-quality training data. Trial-and-error is common because some technologies still mature, and conversation designers lack design patterns for the maintenance domain. Challenges for system adoption include providing an outstanding user experience, handling factory-specific jargon, and the limited availability of easy-to-use data exchange interfaces for machines and business applications. We conclude that further efforts on interoperability, technology stack management, AI-focused change management, and education programs are necessary. Furthermore, the accountability of AI systems is a cost factor for the assistant’s service providers and the client companies in manufacturing – AI insurance services, human-in-the-loop functions, user training, and professional education are actions to address this issue.
Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement

These days, digital images are one of the most profound methods used to represent information. Still, various images are obtained with a low-light effect due to numerous unavoidable reasons. It may be problematic for humans and computer-related applications to perceive and extract valuable information from such images properly. Hence, the observed quality of low-light images should be ameliorated for improved analysis, understanding, and interpretation. Currently, the enhancement of low-light images is a challenging task since various factors, including brightness, contrast, and colors should be considered effectively to produce results with adequate quality. Therefore, a retinex-based multiphase algorithm is developed in this study, in that it computes the illumination image somewhat similar to the single-scale retinex algorithm, takes the logs of both the original and the illumination images, subtract them using a modified approach, the result is then processed by a gamma-corrected sigmoid function and further processed by a normalization function to produce to the final result. The proposed algorithm is tested using natural low-light images, evaluated using specialized metrics, and compared with eight different sophisticated methods. The attained experiential outcomes revealed that the proposed algorithm has delivered the best performances concerning processing speed, perceived quality, and evaluation metrics.
Stochastic Conditional Gradient++: (Non)Convex Minimization and Continuous Submodular Maximization

In this paper, we consider the general nonoblivious stochastic optimization where the underlying stochasticity may change during the optimization procedure and depends on the point at which the function is evaluated. We develop Stochastic Frank--Wolfe++ (SFW++), an efficient variant of the conditional gradient method for minimizing a smooth nonconvex function subject to a convex body constraint. We show that SFW++ converges to an $\epsilon$-first order stationary point by using $O(1/\epsilon^3)$ stochastic gradients. Once further structures are present, SFW++'s theoretical guarantees, in terms of the convergence rate and quality of its solution, improve. In particular, for minimizing a convex function, SFW++ achieves an $\epsilon$-approximate optimum while using $O(1/\epsilon^2)$ stochastic gradients. It is known that this rate is optimal in terms of stochastic gradient evaluations. Similarly, for maximizing a monotone continuous DR-submodular function, a slightly different form of SFW++, called Stochastic Continuous Greedy++ (SCG++), achieves a tight $[(1-1/e){\text{OPT}} -\epsilon]$ solution while using $O(1/\epsilon^2)$ stochastic gradients. Through an information theoretic argument, we also prove that SCG++'s convergence rate is optimal. Finally, for maximizing a nonmonotone continuous DR-submodular function, we can achieve a $[(1/e){\text{OPT}} -\epsilon]$ solution by using $O(1/\epsilon^2)$ stochastic gradients. We should highlight that our results and our novel variance reduction technique trivially extend to the standard and easier oblivious stochastic optimization settings for (non)convex and continuous submodular settings.
Curriculum-Based Deep Reinforcement Learning for Quantum Control

Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel DRL approach by constructing a curriculum consisting of a set of intermediate tasks defined by fidelity thresholds, where the tasks among a curriculum can be statically determined before the learning process or dynamically generated during the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based DRL (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical comparison with the traditional methods [gradient method (GD), genetic algorithm (GA), and several other DRL methods] demonstrates that CDRL exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with few control pulses.
Designing research to inform sustainability and scalability of digital technology innovations

Considerably more research needs to be done to understand how successful technological innovations and change processes are sustained and scaled to new learning contexts. Without a better understanding of successful technological innovation, the wider field and education community are unable to benefit and build capacity. A model is presented that explores how research can better inform sustainability and scalability in technological innovations. It is represented as four loops: Organization, Innovation, Research and New Contexts. Three international case studies of technological innovation are analyzed to demonstrate use of the framework and model. Results show that research can be designed to support sustainability and scalability, but that this needs to be balanced with other factors to support a successful technological innovation. Implications for supporting technological innovations are explored.
C3Net: Cross-Modal Feature Recalibrated, Cross-Scale Semantic Aggregated and Compact Network for Semantic Segmentation of Multi-Modal High-Resolution Aerial Images

Semantic segmentation of multi-modal remote sensing images is an important branch of remote sensing image interpretation. Multi-modal data has been proven to provide rich complementary information to deal with complex scenes. In recent years, semantic segmentation based on deep learning methods has made remarkable achievements. It is common to simply concatenate multi-modal data or use parallel branches to extract multi-modal features separately. However, most existing works ignore the effects of noise and redundant features from different modalities, which may not lead to satisfactory results. On the one hand, existing networks do not learn the complementary information of different modalities and suppress the mutual interference between different modalities, which may lead to a decrease in segmentation accuracy. On the other hand, the introduction of multi-modal data greatly increases the running time of the pixel-level dense prediction. In this work, we propose an efficient C3Net that strikes a balance between speed and accuracy. More specifically, C3Net contains several backbones for extracting features of different modalities. Then, a plug-and-play module is designed to effectively recalibrate and aggregate multi-modal features. In order to reduce the number of model parameters while remaining the model performance, we redesign the semantic contextual extraction module based on the lightweight convolutional groups. Besides, a multi-level knowledge distillation strategy is proposed to improve the performance of the compact model. Experiments on ISPRS Vaihingen dataset demonstrate the superior performance of C3Net with 15× fewer FLOPs than the state-of-the-art baseline network while providing comparable overall accuracy.
ANT-Man: Towards Agile Power Management in the Microservice Era

The emerging trend of decomposing cloud applications into microservices has raised new questions about managing the performance/power trade-off of a datacenter at microsecondscale. We introduce ANT-Man, an Auto, Native and Transparent power Management framework that can exploit fine-grained microservice variability for system efficiency. To achieve this, ANT-Man abstracts away two major sources of latency overhead in traditional hierarchical power management frameworks. First, ANT-Man proposes an auto power budgeting scheme for reducing the power coordination latency at the datacenter level. It can proactively determine the power budget tailored to each individual microservice. Second, ANT-Man proposes a native and transparent power control scheme to overcome the power configuration latency for each microservice. It enables super-fast power budget enforcement with nanosecond-scale performance scaling. Extensive experiments on our prototyped system show that ANT-Man could slash power consumption by $ 7.8\sim 43.5\%$ and in the meantime reduce the $95^{\text{th}}$ tail latency by $ 9.7\sim 12.5\%$ compared to existing techniques.
Image Caption Generator

In the modern era, image captioning has become one of the most widely required tools. Moreover, there are inbuilt applications that generate and provide a caption for a certain image, all these things are done with the help of deep neural network models. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. It generates syntactically and semantically correct sentences. In this paper, we present a deep learning model to describe images and generate captions using computer vision and machine translation. This paper aims to detect different objects found in an image, recognize the relationships between those objects and generate captions. The dataset used is Flickr8k and the programming language used was Python3, and an ML technique called Transfer Learning will be implemented with the help of the Xception model, to demonstrate the proposed experiment. This paper will also elaborate on the functions and structure of the various Neural networks involved. Generating image captions is an important aspect of Computer Vision and Natural language processing. Image caption generators can find applications in Image segmentation as used by Facebook and Google Photos, and even more so, its use can be extended to video frames. They will easily automate the job of a person who has to interpret images. Not to mention it has immense scope in helping visually impaired people.
Guided-Pix2Pix: End-to-End Inference and Refinement Network for Image Dehazing

Haze removal is still an essential prerequisite for image processing and computer vision tasks, and joint inference and refinement of transmission maps remain challenging in the physical scattering model-based haze removal methods. In this article, we propose an end-to-end learnable dehazing network, which is referred to as Guided-Pix2Pix, to jointly estimate and refine the transmission map and further dehaze images by the physical scattering equation. Instead of a two-stage model of predicting and postprocessing the transmission, Guided-Pix2Pix concatenates the trainable Pix2Pix backbone and differentiable guided filter as an embedded layer, which enables generating refined transmission maps in one feed-forward step, and then it substitutes these potential refinements into the physical scattering equation to restore dehazed images. To verify that our Guided-Pix2Pix can be embedded in both training and inference, we demonstrate that the guided filter layer is differentiable and capable of propagating both features forward and gradients backward. Furthermore, explicit derivatives with respect to the input of the guided filter are given, and the relationship between our derivation and that in the guided filter is also explored. Experiments show that our network is effective and robust in image dehazing, can alleviate the halo artifacts along edges, and has great generalization capability.
An Approach for Shadow Detection in Aerial Images Based on Multi-Channel Statistics

Shadows are likely to cause flaws in color interpretation, loss of image information or deformation of objects; this is a relevant issue owing to the fact that with the development of unmanned aerial vehicles and satellite devices, object detection by aerial images has become an essential aim to research works.The main contribution of this article is the development of an algorithm that processes shadow detection by detaching the channels red, green, blue and an additional low saturation channel on individual masks; having created those masks, the task of shadow detection is accomplished by conducting a pixel-by-pixel basis with the aid of a decision tree and statistical descriptors extracted out of the image, where the low saturation channel information leads to improvements on shadow detection accuracy in regions that contain asphalt and concrete.Thus, experimental results show the good behavior of shadow detection algorithm in terms of accuracy, over-segmentation, and under-segmentation, in which the accuracy of obtained shadow detection approaches a 94%.
Application of Cognitive Modelling for Operation Improvement of Retail Chain Management System

The purpose of the study is to implement cognitive modelling of the retail chain management system to ensure the effectiveness of management decisions in order to implement structural changes in its activities. It is determined that the organization of retail chain management is more effective due to provided principles of a systematic approach which are applied. Therefore, using the concept of Balanced Scorecard (BSC) in this study, which reflects the interaction and interdependence of structural units, which is feasible, we propose it to further search for development scenarios for the retail chain management. The article presents a sequence of cognitive modelling, which consists of seven main stages. Drogerie - format management system of retail chains has been chosen since it has recently been standing out by its popularity with consumers among other formats, as an object for the study. The results of this study can be useful for enterprises and organizations with complex systems that are characterized by hierarchical, network relationships between subsystems or where random processes take place.
Smart attendance using deep learning and computer vision

Attendance is an essential part of daily classroom evaluation. Traditional classroom follows a manual attendance marking system, i.e., calling a student's names or by forwarding an attendance sheet. This process is both time-consuming and error-prone, i.e., student proxy, etc. Hence a face recognition based smart classroom attendance management system using computer vision and deep learning implemented on a Raspberry Pi has been proposed. It has been proposed to mount a camera at the top of the blackboard so that the students are visible while they are sitting down. A face detection algorithm followed by face recognition has been used to mark the attendance of the detected student.
SiameseQAT: A Semantic Context-Based Duplicate Bug Report Detection Using Replicated Cluster Information

In large-scale software development environments, defect reports are maintained through bug tracking systems (BTS) and analyzed by domain experts.Different users may create bug reports in a nonstandard manner and may report a particular problem using a particular set of words due to stylistic choices and writing patterns.Therefore, the same defect can be reported with very different descriptions, generating non-trivial duplicates.To avoid redundant work for the development team, an expert needs to look at all new reports while trying to label possible duplicates.However, this approach is neither trivial nor scalable and directly impacts bug fix correction time.Recent efforts to find duplicate bug reports tend to focus on deep neural approaches that consider hybrid representations of bug reports, using both structured and unstructured information.Unfortunately, these approaches ignore that a single bug can have multiple previously identified duplicates and, therefore, multiple textual descriptions, titles, and categorical information.In this work, we propose SiameseQAT, a duplicate bug report detection method that considers information on individual bugs as well as information extracted from bug clusters.The SiameseQAT combines context and semantic learning on structured and unstructured features and corpus topic extraction-based features, with a novel loss function called Quintet Loss, which considers the centroid of duplicate clusters and their contextual information.We validated our approach on the well-known open-source software repositories Eclipse, NetBeans, and Open Office, comprised of more than 500 thousand bug reports.We evaluated both the retrieval and classification of duplicates, reporting a Recall@25 mean of 85% for retrieval and 84% AUROC for classification tasks, results that were significantly superior to previous works.
A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease Evolution

Kidney Disease (KD) may hide complex causes and is associated with a tremendous socio-economic impact. Timely identification and management from the first level of medical care represent the most effective strategy to address the growing global burden sustainably. Clinical practice guidelines suggest utilizing estimated Glomerular Filtration Rate (eGFR) for routine evaluation within a screening purpose. Accordingly, the analysis of Electronic Health Records (EHRs) using Machine Learning techniques offers great opportunities to monitor and predict the eGFR trend over time. This paper aims to propose a novel Semi-Supervised Multi-Task Learning (SS-MTL) approach for predicting short-term KD evolution on multiple General Practitioners' EHR data. We demonstrated that the SS-MTL approach can (i) capture the eGFR temporal evolution by imposing a temporal relatedness between consecutive time windows and (ii) exploit useful information from unlabeled patients when labeled patients are less numerous with a gain of up to 4.1% in terms of Recall. This situation reflects the real-case scenario, where available labeled samples are limited, but those unlabeled much more abundant. The SS-MTL approach, also given the high level of interpretability, might be the ideal candidate in general practice to get integrated within a decision support system for KD screening purposes.
Applied Research of Knowledge in the Field of Artificial Intelligence in the Intelligent Retrieval of Teaching Resources

In the development process of education informatization, digital teaching resources continue to grow, and how to manage and organize massive teaching resources has become a key issue for teaching staff. An efficient and accurate search system is an important part of the teaching resource service system. The use of intelligent search engines can search teaching resources comprehensively and efficiently, and the artificial intelligence search engine provides a reliable and convenient solution for the design and development of intelligent search systems. The article analyzes the design principles and technical standards of the intelligent search system, expounds the system’s functional architecture and database design, and introduces the realization process and principles of the search system. By analyzing the characteristics of basic education resources and existing automatic abstracting methods, this paper proposes to integrate the calculated feature word weights in the field of basic education into the algorithm for calculating the weights of abstract sentences and simultaneously examine the sentence position, sentence length, and other texts. There is an automatic summarization algorithm for surface statistics. This article also introduces the search design ideas and implementation steps based on artificial intelligence, makes a scientific evaluation and summary of the actual situation of the automatic abstract system running in the basic education resource search engine, and looks forward to the next improvement work.
Digitality: A reality check

The condition of digitality and the associated concept of digitalism are increasingly important today.Our ever-more digital societal context means that many are experiencing a digital life where reality and digitality blur in their distinction in many people's digital lives.This paper considers these aspects with respect to digital culture, especially in the context of heritage and museums.In particular, we look at developments since a previous 2014 paper by the authors and speculate on possible future developments.We focus especially on the reality check provided by the Covid-19 pandemic, that has accelerated societal digitality.Digitalism.Digitality.Digitalisation.