Reviewerly/exHarmony huggingface dataset:
Lattice Network for Lightweight Image Restoration

Deep learning has made unprecedented progress in image restoration (IR), where residual block (RB) is popularly used and has a significant effect on promising performance. However, the massive stacked RBs bring about burdensome memory and computation cost. To tackle this issue, we aim to design an economical structure for adaptively connecting pair-wise RBs, thereby enhancing the model representation. Inspired by the topological structure of lattice filter in signal processing theory, we elaborately propose the lattice block (LB), where couple butterfly-style topological structures are utilized to bridge pair-wise RBs. Specifically, each candidate structure of LB relies on the combination coefficients learned through adaptive channel reweighting. As a basic mapping block, LB can be plugged into various IR models, such as image super-resolution, image denoising, image deraining, etc. It can avail the construction of lightweight IR models accompanying half parameter amount reduced, while keeping the considerable reconstruction accuracy compared with RBs. Moreover, a novel contrastive loss is exploited as a regularization constraint, which can further enhance the model representation without increasing the inference expenses. Experiments on several IR tasks illustrate that our method can achieve more favorable performance than other state-of-the-art models with lower storage and computation.
Blockchain for AI-Enabled Industrial IoT with 5G Network

The world is advancing to a new digitalized world that has one of the most powerful technologies that ever existed in human history. These technologies are making humans create such objects that were only told in the fairy tales in the past. This research proposes a model that integrates the latest and one of the most powerful technologies of the decade. This study integrated the 5G network with the IIoT (Industrial Internet of Things) which is based on artificial intelligence to develop an intelligent machine capable of mimicking humans. Such a system is so powerful yet so vulnerable to problems like hacking, cyber-attacks and so on. This problem is solved with the blockchain. The research adds blockchain to the model to make the model more secure and efficient as it provides a decentralized system to ensure transparency. Previous researches have covered the IoT with blockchain but this research is the advanced version which includes the industrial IoT using Artificial intelligence to make intelligent Internet of Things.
FiFoNet: Fine-Grained Target Focusing Network for Object Detection in UAV Images

Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.
Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis

Practical demands and academic challenges have both contributed to making sentiment analysis a thriving area of research. Given that a great deal of sentiment analysis work is performed on social media communications, where text frequently ignores the rules of grammar and spelling, pre-processing techniques are required to clean the data. Pre-processing is also required to normalise the text before undertaking the analysis, as social media is inundated with abbreviations, emoticons, emojis, truncated sentences, and slang. While pre-processing has been widely discussed in the literature, and it is considered indispensable, recommendations for best practice have not been conclusive. Thus, we have reviewed the available research on the subject and evaluated various combinations of pre-processing components quantitatively. We have focused on the case of Twitter sentiment analysis, as Twitter has proved to be an important source of publicly accessible data. We have also assessed the effectiveness of different combinations of pre-processing components for the overall accuracy of a couple of off-the-shelf tools and one algorithm implemented by us. Our results confirm that the order of the pre-processing components matters and significantly improves the performance of naïve Bayes classifiers. We also confirm that lemmatisation is useful for enhancing the performance of an index, but it does not notably improve the quality of sentiment analysis.
A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora

Clinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is very challenging due to the complex and unstructured texts of such documents. Named entity recognition (NER) is a fundamental and necessary step to process and standardize the unstructured text in clinical trials using Natural Language Processing (NLP) techniques.In this study we fine-tuned pre-trained language models to support the NER task on clinical trial eligibility criteria. We systematically investigated four pre-trained contextual embedding models for the biomedical domain (i.e., BioBERT, BlueBERT, PubMedBERT, and SciBERT) and two models for the open domains (BERT and SpanBERT), for NER tasks using three existing clinical trial eligibility criteria corpora. In addition, we also investigated the feasibility of data augmentation approaches and evaluated their performance.Our evaluation results using tenfold cross-validation show that domain-specific transformer models achieved better performance than the general transformer models, with the best performance obtained by the PubMedBERT model (F1-scores of 0.715, 0.836, and 0.622 for the three corpora respectively). The data augmentation results show that it is feasible to leverage additional corpora to improve NER performance.Findings from this study not only demonstrate the importance of contextual embeddings trained from domain-specific corpora, but also shed lights on the benefits of leveraging multiple data sources for the challenging NER task in clinical trial eligibility criteria text.
Underwater Sea Cucumber Identification Based on Improved YOLOv5

In order to develop an underwater sea cucumber collecting robot, it is necessary to use the machine vision method to realize sea cucumber recognition and location. An identification and location method of underwater sea cucumber based on improved You Only Look Once version 5 (YOLOv5) is proposed. Due to the low contrast between sea cucumbers and the underwater environment, the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm was introduced to process the images to enhance the contrast. In order to improve the recognition precision and efficiency, the Convolutional Block Attention Module (CBAM) is added. In order to make small target recognition more precise, the Detect layer was added to the Head network of YOLOv5s. The improved YOLOv5s model and YOLOv5s, YOLOv4, and Faster-RCNN identified the same image set; the experimental results show improved YOLOv5 recognition precision level and confidence level, especially for small target recognition, which is excellent and better than other models. Compared to the other three models, the improved YOLOv5s has higher precision and detection time. Compared with the YOLOv5s, the precision and recall rate of the improved YOLOv5s model are improved by 9% and 11.5%, respectively.
Equilibrium mass-dependent isotope fractionation of antimony between stibnite and Sb secondary minerals: A first-principles study

Antimony (Sb) isotopes are gaining increasing interest for their potential as geochemical tracers in geological, environmental and archaeological studies. However, little is known about the parameters controlling Sb isotope fractionation, which is essential to interpret variations of isotopic signature in natural systems. In this study, equilibrium mass-dependent isotope fractionation factors (β-factor) were calculated between different Sb-bearing minerals commonly found in mining environments including primary Sb sulphide (stibnite Sb2S3) and its oxidation products (valentinite Sb2O3, senarmontite Sb2O3, cervantite Sb2O4) and synthetic antimony pentoxide Sb2O5. First-principles calculations within the Density Functional Theory (DFT) were performed with different functionals to test the robustness of the method. Among the studied minerals, stibnite has the lowest β-factor (ln(β) = 0.71 ‰ at 22°C), then β-factors progressively increase from valentinite (ln(β) = 1.64 ‰ at 22°C), to senarmontite (ln(β) = 1.80 ‰ at 22°C), cervantite (ln(β) = 2.20 ‰ at 22°C) and antimony pentoxide (ln(β) = 3.03 ‰ at 22°C). The factors that most fractionate Sb isotopes are found to be i) the change of Sb oxidation state (Sb isotope ratio in Sb(V)-bearing minerals is higher than in Sb(III)-bearing minerals), ii) the change of first neighbour of Sb (Sb isotope ratio in SbO bonds is higher than in SbS bonds) and iii) distortion of the atomic SbO polyhedrons. The negligible differences in the β-factors obtained with different functionals showed the robustness of the approach for the calculation of β-factors, despite differences in the calculated mineral lattice and Raman frequencies. The results of this study provide a theoretical basis to interpret natural Sb isotope variations. The results suggest that a significant enrichment in the heavy isotope could occur during oxidative dissolution of stibnite and subsequent precipitation of Sb(III) and Sb(V) oxides in sulphide environments. More generally, this work strongly supports that Sb isotopes may be a useful tracer of Sb transformation processes in nature.
How Scrum adds value to achieving software quality?

Scrum remains the most popular agile software development method implementation for a variety of reasons; one important motive is to improve software quality. Yet many organizations fail to achieve quality improvements through the use of Scrum, and existing research sheds little light on the value-add of Scrum for software quality. More specifically, (1) how notions of software quality among Scrum practitioners relate to established quality perspectives, (2) how Scrum helps teams to achieve higher software quality and (3) why some teams fail to meet the objective of higher quality. We addressed these gaps through a two-phased qualitative study based on 39 interviews and two in-depth case studies. We find that Scrum practitioners emphasize established notions of external quality comprising of conformity to business needs and absence of defects, while they also value internal quality, especially sustainable software design. Our results show that Scrum helps teams achieve both dimensions of quality by promoting some social antecedents (collaboration, psychological safety, accountability, transparency) and process-induced advantages (iterative development, formal inspection, and adaptation). Our findings unveil how these factors contribute to achieving software quality and under what conditions their effects can fail to materialize. These conditions include inconsistent Scrum implementations, cultural constraints, team tensions, and inaccessibility of end-users. In addition, the complexity of the project aggravates the impact of these conditions. Taken together, these findings show that Scrum can complement established quality assurance and software engineering practices by promoting a social environment that is conducive to creating high-quality software. Based on our findings, we provide specific recommendations for how practitioners can create such an environment.
Multi-Learner Based Deep Meta-Learning for Few-Shot Medical Image Classification

Few-shot learning (FSL) is promising in the field of medical image analysis due to high cost of establishing high-quality medical datasets. Many FSL approaches have been proposed in natural image scenes. However, present FSL methods are rarely evaluated on medical images and the FSL technology applicable to medical scenarios need to be further developed. Meta-learning has supplied an optional framework to address the challenging FSL setting. In this paper, we propose a novel multi-learner based FSL method for multiple medical image classification tasks, combining meta-learning with transfer-learning and metric-learning. Our designed model is composed of three learners, including auto-encoder, metric-learner and task-learner. In transfer-learning, all the learners are trained on the base classes. In the ensuing meta-learning, we leverage multiple novel tasks to fine-tune the metric-learner and task-learner in order to fast adapt to unseen tasks. Moreover, to further boost the learning efficiency of our model, we devised real-time data augmentation and dynamic Gaussian disturbance soft label (GDSL) scheme as effective generalization strategies of few-shot classification tasks. We have conducted experiments for three-class few-shot classification tasks on three newly-built challenging medical benchmarks, BLOOD, PATH and CHEST. Extensive comparisons to related works validated that our method achieved top performance both on homogeneous medical datasets and cross-domain datasets.
Joint task offloading and resource optimization in NOMA-based vehicular edge computing: A game-theoretic DRL approach

Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular applications with stringent deadline requirements. This work presents a non-orthogonal multiple access (NOMA) based architecture in VEC, where heterogeneous edge nodes are cooperated for real-time task processing. We derive a vehicle-to-infrastructure (V2I) transmission model by considering both intra-edge and inter-edge interferences and formulate a cooperative resource optimization (CRO) problem by jointly optimizing the task offloading and resource allocation, aiming at maximizing the service ratio. Further, we decompose the CRO into two subproblems, namely, task offloading and resource allocation. In particular, the task offloading subproblem is modeled as an exact potential game (EPG), and a multi-agent distributed distributional deep deterministic policy gradient (MAD4PG) is proposed to achieve the Nash equilibrium. The resource allocation subproblem is divided into two independent convex optimization problems, and an optimal solution is proposed by using a gradient-based iterative method and KKT condition. Finally, we build the simulation model based on real-world vehicular trajectories and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solutions.
ACUX Recommender: A Mobile Recommendation System for Multi-Profile Cultural Visitors Based on Visiting Preferences Classification

In recent years, Recommendation Systems (RSs) have gained popularity in different scientific fields through the creation of (mostly mobile) applications that deliver personalized services. A mobile recommendation system (MRS) that classifies in situ visitors according to different visiting profiles could act as a mediator between their visiting preferences and cultural content. Drawing on the above, in this paper, we propose ACUX Recommender (ACUX-R), an MRS, for recommending personalized cultural POIs to visitors based on their visiting preferences. ACUX-R experimentally employs the ACUX typology for assigning profiles to cultural visitors. ACUX-R was evaluated through a user study and a questionnaire. The evaluation conducted showed that the proposed ACUX-R satisfies cultural visitors and is capable of capturing their nonverbal visiting preferences and needs.
Breast Cancer Dataset, Classification and Detection Using Deep Learning

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients’ treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.
Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan)

Background and objectives: Visual analysis of cell morphology has an important role in the diagnosis of hematological diseases. Morphological cell recognition is a challenge that requires experience and in-depth review by clinical pathologists. Within the new trend of introducing computer-aided diagnostic tools in laboratory medicine, models based on deep learning are being developed for the automatic identification of different types of cells in peripheral blood. In general, well-annotated large image sets are needed to train the models to reach a desired classification performance. This is especially relevant when it comes to discerning between cell images in which morphological differences are subtle and when it comes to low prevalent diseases with the consequent difficulty in collecting cell images. The objective of this work is to develop, train and validate SyntheticCellGAN (SCG), a new system for the automatic generation of artificial images of white blood cells, maintaining morphological characteristics very close to real cells found in practice in clinical laboratories. Methods: SCG is designed with two sequential generative adversarial networks. First, a Wasserstein structure is used to transform random noise vectors into low resolution images of basic mononuclear cells. Second, the concept of image-to-image translation is used to build specific models that transform the basic images into high-resolution final images with the realistic morphology of each cell type target: 1) the five groups of normal leukocytes (lymphocytes, monocytes, eosinophils, neutrophils and basophils); 2) atypical promyelocytes and hairy cells, which are two relevant cell types of complex morphology with low abundance in blood smears. Results: The images of the SCG system are evaluated with four experimental tests. In the first test we evaluated the generated images with quantitative metrics for GANs. In the second test, morphological verification of the artificial images is performed by expert clinical pathologists with 100% accuracy. In the third test, two classifiers based on convolutional neural networks (CNN) previously trained with images of real cells are used. Two sets of artificial images of the SCG system are classified with an accuracy of 95.36% and 94%, respectively. In the fourth test, three CNN classifiers are trained with artificial images of the SCG system. Real cells are identified with an accuracy ranging from 87.7% to 100%. Conclusions: The SCG system has proven effective in creating images of all normal leukocytes and two low-prevalence cell classes associated with diseases such as acute promyelocyte leukemia and hairy cell leukemia. Once trained, the system requires low computational cost and can help augment high-quality image datasets to improve automatic recognition model training for clinical laboratory practice.
Quantum speed limits on operator flows and correlation functions

Quantum speed limits (QSLs) identify fundamental time scales of physical processes by providing lower bounds on the rate of change of a quantum state or the expectation value of an observable. We introduce a generalization of QSL for unitary operator flows, which are ubiquitous in physics and relevant for applications in both the quantum and classical domains. We derive two types of QSLs and assess the existence of a crossover between them, that we illustrate with a qubit and a random matrix Hamiltonian, as canonical examples. We further apply our results to the time evolution of autocorrelation functions, obtaining computable constraints on the linear dynamical response of quantum systems out of equilibrium and the quantum Fisher information governing the precision in quantum parameter estimation.
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image

We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in-the-wild. It contains annotations of over 3.5K land-marks that accurately represent 3D head shape compared to the ground-truth scans. The data-driven model, DAD-3DNet, trained on our dataset, learns shape, expression, and pose parameters, and performs 3D reconstruction of a FLAME mesh. The model also incorporates a landmark prediction branch to take advantage of rich supervision and co-training of multiple related tasks. Experimentally, DAD-3DNet outperforms or is comparable to the state-of-the-art models in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D Face Shape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3D Land-marks Estimation on DAD-3DHeads dataset. Finally, diver-sity of DAD-3DHeads in camera angles, facial expressions, and occlusions enables a benchmark to study in-the-wild generalization and robustness to distribution shifts. The dataset webpage is https://p.farm/research/dad-3dheads.