Reviewerly/exHarmony huggingface dataset:
Diagnostic performance of combined use of automated breast volume scanning &amp; hand-held ultrasound for breast lesions

Breast cancer being one of the most common malignant tumours among women, diagnostic modalities for early detection of the same become of paramount importance. In this context, the hand-held ultrasound (HHUS) and automated breast volume scanner (ABVS) could provide valuable information for clinicians to diagnose breast diseases. This study aimed to compare and evaluate the diagnostic performance of combined use of HHUS and ABVS for the differentiation of benign and malignant breast lesions.A total of 361 female patients, who underwent both HHUS and ABVS examinations were included in this study. ABVS and HHUS images were interpreted using the American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS). The distributions of the BI-RADS categories and pathology results were shown as specific numbers. Kappa coefficients test (κ) was calculated to compare the diagnostic results amongst the ABVS, HHUS and ABVS combined with HHUS. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of the three diagnostic methods were calculated and their respective diagnostic performance was analyzed by receiver operator characteristic curve.Of a total of 431 lesions, 153 (35.5%) were malignant and 278 (64.5%) were benign. With respect to the pathology results, the value of κ was 0.713 (P<0.001) for HHUS, κ=0.765 (P<0.001) for ABVS and κ=0.815 (P<0.001) for HHUS+ABVS. The sensitivity, specificity, accuracy, PPV and NPV for HHUS combined with ABVS were 96.08 (147/153), 88.49 (246/278), 91.18 (393/431), 82.12 (147/179) and 97.62 per cent (246/252) respectively. For HHUS, these were 90.20 (138/153), 84.17 (234/278), 86.31 (372/431), 75.82 (138/182) and 93.98 per cent (234/249) respectively; and for ABVS these were 92.16 (141/153), 87.05 (242/278), 88.86 (383/431), 79.66 (141/177) and 95.28 per cent (242/254), respectively. There was no significant difference amongst these three methods, but the diagnostic performance of HHUS combined with ABVS was better than, or at least equal to, that of HHUS or ABVS alone.The results of this study suggest that ABVS is a promising and advantageous modality for breast cancer detection. Furthermore, the combination of HHUS and ABVS showed a more comparable diagnostic performance than HHUS or ABVS alone for distinguishing between benign and malignant breast lesions.
Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images

Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic. Motivated by this, several personalized enhancement methods have been investigated. However, the enhancement process based on user preferences in these techniques is invisible, i.e., a "black box". In this work, we propose an intelligible unsupervised personalized enhancer (iUP-Enhancer) for low-light images, which establishes the correlations between the low-light and the unpaired reference images with regard to three user-friendly attributions (brightness, chromaticity, and noise). The proposed iUP-Enhancer is trained with the guidance of these correlations and the corresponding unsupervised loss functions. Rather than a "black box" process, our iUP-Enhancer presents an intelligible enhancement process with the above attributions. Extensive experiments demonstrate that the proposed algorithm produces competitive qualitative and quantitative results while maintaining excellent flexibility and scalability. This can be validated by personalization with single/multiple references, cross-attribution references, or merely adjusting parameters.
Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot.
Active learning improves performance on symbolic regression tasks in StackGP

This paper introduces an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds the data point characterized by maximizing prediction uncertainty as measured by the model ensemble. Symbolic regression is re-run with the larger data set. This cycle continues until the system satisfies a termination criterion. The Feynman AI benchmark set of equations is used to examine the ability of the method to find appropriate models using as few data points as possible. The approach successfully rediscovered 72 of the 100 Feynman equations without the use of domain expertise or data translation.
An Improved Authentication &amp; Key Exchange Protocol Based on ECDH for WSNs

The widespread use of wireless sensor networks (WSNs) that are consisted of resource-constrained sensor nodes in communication with gateways in open-space environments and industries has highlighted the need for a secure yet fast communication protocol between users, gateways, and sensor nodes. Due to the properties of the network, elliptic-curve cryptography seems to be the most viable choice as it requires fewer resources than most other options. In this paper, we analyze the protocol suggested by Moghadam et al. which is based on ECDH (elliptic-curve Diffie-Hellman), and mention some of the flaws in their proposed authentication and key exchange protocol. Some attacks are also mentioned to further explain the shortcomings of their schema. Then a modified version of the protocol is proposed, analyzed, and checked against the same attacks as an informal security proof.
SDST-Accelerating GEMM-based Convolution through Smart Data Stream Transformation

The development of flexible Convolutional Neural Network (CNN) accelerators is critical for large-scale inference and training. Accelerators based on the General Matrix Multiplication (GEMM) kernel have gained popularity due to their ability to accelerate the most prevalent convolutional and fully connected layers in CNNs. However, the convolution inputs must be reshaped and packed into redundant matrices, which is performed by the im2col (image to column) algorithm. As the performance of the GEMM kernel improves, it increases latency and gradually becomes a bottleneck. To address this issue, we propose Smart Data Stream Transformation (SDST), a technique that eliminates explicit data transformation through data stream manipulation. SDST divides the input data into conflict-free streams based on the locality of data redundancy. Additionally, we design the continuity-friendly data layout to unify the transformations across data streams. Our design is evaluated by running the YoloV3-tiny model on an FPGA-based prototype system. Experimental results show that SDST improves the performance of convolutional acceleration by a factor of 1.12 to 5.69 compared to explicit im2col performed on the CPU.
You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine

Layout Analysis (the identification of zones and their classification) is the first step along line segmentation in Optical Character Recognition and similar tasks. The ability of identifying main body of text from marginal text or running titles makes the difference between extracting the work full text of a digitized book and noisy outputs. We show that most segmenters focus on pixel classification and that polygonization of this output has not been used as a target for the latest competition on historical document (ICDAR 2017 and onwards), despite being the focus in the early 2010s. We propose to shift, for efficiency, the task from a pixel classification-based polygonization to an object detection using isothetic rectangles. We compare the output of Kraken and YOLOv5 in terms of segmentation and show that the later severely outperforms the first on small datasets (1110 samples and below). We release two datasets for training and evaluation on historical documents as well as a new package, YALTAi, which injects YOLOv5 in the segmentation pipeline of Kraken 4.1.
A Blend of Analytical and Numerical Methods to Compute Orthogonal Image Moments over a Unit Disk

Accurate image moment computation is critical because they are used in a variety of fields, including image reconstruction and object recognition. Orthogonal polynomials are frequently used to compute moments due to their numerous intersecting and important theoretical properties. In polar coordinate systems, orthogonal polynomials such as the Polar Harmonic Fourier Transform (PHFT) and Polar Harmonic Transformation (PHT) are defined. However, the images are defined by a Cartesian coordinate system. To obtain image moments, a double integration over a unit circle over the product of the PHFT or PHT and the image function must be performed. The choice of double integration techniques and domain for the double integral has a significant effect on the precision of the computed moments. We have proposed a method for using the entire unit circle as an integration domain in this study, for image reconstruction. We used the PHFT and PHT to apply this technique to computing moments for the reconstruction. The proposed method outperforms other state-of-the-art methods, including Gaussian quadrature numerical integration method (GQM) and zeroth order approximation (ZOA) on variety of scenarios using three benchmarked image sets. We have demonstrated experimentally that this technique significantly improves the accuracy of image moments in higher order moments, as measured by reduced mean squared error (MSE). Additionally, the proposed method significantly improved the moment’s rotational (an improvement of 1-3% as compared to GQM and an improvement of 37-93% as compared to ZOA) and scaling invariance (an improvement of 0-500% as compared to GQM and an improvement of 3-7000% as compared to ZOA).
Automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging of rat brain

To construct an automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging (MEMRI) of rat brain with high accuracy, which could preserve the inherent voxel intensity and Regions of interest (ROI) morphological characteristics simultaneously.The transformation relationship from standardized space to individual space was obtained by firstly normalizing individual image to the Paxinos space and then inversely transformed. On the other hand, all the regions defined in the atlas image were separated and resaved as binary mask images. Then, transforming the mask images into individual space via the inverse transformations and reslicing using the 4th B-spline interpolation algorithm. The boundary of these transformed regions was further refined by image erosion and expansion operator, and finally combined together to generate the individual parcellations. Moreover, two groups of MEMRI images were used for evaluation. We found that the individual parcellations were satisfied, and the inherent image intensity was preserved. The statistical significance of case-control comparisons was further optimized.We have constructed a new automatic method for individual parcellation of rat brain MEMRI images, which could preserve the inherent voxel intensity and further be beneficial in case-control statistical analyses. This method could also be extended to other imaging modalities, even other experiments species. It would facilitate the accuracy and significance of ROI-based imaging analyses.
The Performance Analysis of a Location based Weather Identification Device

The Android platform is the primary leading operating system in the mobile phone market, followed by the iOS platform while the rest of the platforms have a negligible share in the mobile operating systems market. So, it’s safe to say that android is the fastest growing mobile operating systems platform and creating an android application will be increasingly profitable. And so, the proposed system is a weather forecasting android application. This application shows weather forecasts while travelling, weather forecasts for tourist spots, weather news, weather forecasts for cities, and weather facts. User analyzed several weather forecasting android applications, and they came up with features which do not exist in those applications, and those features have been implemented in this work. Thus, the developed android application is aimed to help tourists, travelers, and common people. This submission also sends weather alerts and weather notifications. This application has numerous benefits such as accurate weather forecasts, adaptability and so on.
Research on Embedded Animal Recognition System Based on YOLO

A lot of manpower and material resources are consumed in the classification and identification of wild animals. The detection cycle is long and the real-time performance is poor. In this paper, the YOLOv1 algorithm is applied to animal detection, and the training model is embedded into K210 chip to design a portable detection product. Experimental results show that this design can effectively realize animal classification, fast target detection and high detection accuracy. The design can meet the demand of real-time detection of wild animals. It can also track the activity range and habits of the population, which provides a good scientific tool for ecological civilization construction and animal diversity research.
Chinese Spam Detection Using a Hybrid BiGRU-CNN Network with Joint Textual and Phonetic Embedding

The proliferation of spam in China has a negative impact on internet users’ experiences online. Existing methods for detecting spam are primarily based on machine learning. However, it has been discovered that these methods are susceptible to adversarial textual spam that has frequently been imperceptibly modified by spammers. Spammers continually modify their strategies to circumvent spam detection systems. Text with Chinese homophonic substitution may be easily understood by users according to its context. Currently, spammers widely use homophonic substitution to break down spam identification systems on the internet. To address these issues, we propose a Bidirectional Gated Recurrent Unit (BiGRU)–Text Convolutional Neural Network (TextCNN) hybrid model with joint embedding for detecting Chinese spam. Our model effectively uses phonetic information and combines the advantages of parameter sharing from TextCNN with long-term memory from BiGRU. The experimental results on real-world datasets show that our model resists homophone noise to some extent and outperforms mainstream deep learning models. We also demonstrate the generality of joint textual and phonetic embedding, which is applicable to other deep learning networks in Chinese spam detection tasks.
Fast Shape Recognition via the Restraint Reduction of Bone Point Segment

In computer science, and especially in computer vision, the contour of an object is used to describe its features; thus, the shape descriptor plays an indispensable role in target detection and recognition. Further, Fourier is an important mathematical description method, and the Fourier transform of a shape contour has symmetry. This paper will demonstrate the symmetry of shape contour in the frequency domain. In recent years, increasing numbers of shape descriptors have come to the fore, but many descriptors ignore the details of shape. It is found that the most fundamental reason affecting the performance of shape descriptors is structural restraints, especially feature structure restraint. Therefore, in this paper, the restraint of feature structure that intrinsically deteriorates recognition performance is shown, and a fast shape recognition method via the Bone Point Segment (BPS) restraint reduction is proposed. An approach using the inner distance to find bone shapes and segment the shape contour by these bones is proposed. Then, Fourier transform is performed on each segment to form the shape feature. Finally, the restraints of the shape feature are reduced in order to build a more effective shape feature. What is commendable is that its discriminability and robustness is strong, the process is simple, and matching speed is fast. More importantly, the experiment results show that the shape descriptor has higher recognition accuracy and the matching speed runs up to more than 1000 times faster than the existing description methods like CBW and TCD. More importantly, it is worth noting that the recognition accuracy approaches 100% in the self-build dataset.
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning

With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important. Motivated by the tuning behaviors of human experts, it is intuitive to leverage auxiliary knowledge from past HPO tasks to accelerate the current HPO task. In this paper, we propose TransBO, a novel two-phase transfer learning framework for HPO, which can deal with the complementary nature among source tasks and dynamics during knowledge aggregation issues simultaneously. This framework extracts and aggregates source and target knowledge jointly and adaptively, where the weights can be learned in a principled manner. The extensive experiments, including static and dynamic transfer learning settings and neural architecture search, demonstrate the superiority of TransBO over the state-of-the-arts.
A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT

Ambiguities of natural language do not preclude us from using it and context helps in getting ideas across.They, nonetheless, pose a key challenge to the development of competent machines to understand natural language and use it as humans do.Contextuality is an unparalleled phenomenon in quantum mechanics, where different mathematical formalisms have been put forwards to understand and reason about it.In this paper, we construct a schema for anaphoric ambiguities that exhibits quantum-like contextuality.We use a recently developed criterion of sheaf-theoretic contextuality that is applicable to signalling models.We then take advantage of the neural word embedding engine BERT to instantiate the schema to natural language examples and extract probability distributions for the instances.As a result, plenty of sheaf-contextual examples were discovered in the natural language corpora BERT utilises.Our hope is that these examples will pave the way for future research and for finding ways to extend applications of quantum computing to natural language processing.
Approximated Doubly Robust Search Relevance Estimation

Extracting query-document relevance from the sparse, biased clickthrough log is among the most fundamental tasks in the web search system. Prior art mainly learns a relevance judgment model with semantic features of the query and document and ignores directly counterfactual relevance evaluation from the clicking log. Though the learned semantic matching models can provide relevance signals for tail queries as long as the semantic feature is available. However, such a paradigm lacks the capability to introspectively adjust the biased relevance estimation whenever it conflicts with massive implicit user feedback. The counterfactual evaluation methods, on the contrary, ensure unbiased relevance estimation with sufficient click information. However, they suffer from the sparse or even missing clicks caused by the long-tailed query distribution.
Deterministic Entanglement of Large-Scale Hermite-Gaussian Modes

Continuous-variable (CV) entanglement constitutes a powerful approach to deterministic quantum information protocols. Improving the scale of entanglement is of vital importance for enhancing the information capacity of quantum information protocols. Here we demonstrate the deterministic generation of CV entanglement of large-scale Hermite-Gaussian (HG) modes from the four-wave mixing (FWM) process in an atomic vapor. In our experiment, 56 pairs of entangled HG modes are deterministically and simultaneously generated from the FWM process, which increases the number of entangled spatial mode pairs in the CV system. Meanwhile, we experimentally verify the existence of CV entanglement between the evolution process from HG mode to Laguerre-Gaussian mode. Our results shed light on constructing high-capacity CV quantum information protocols and provide a promising platform to study CV entanglement.
Analysis of caesarean section using Robson’s ten group classification system - a way of monitoring obstetric practice

Aim:The aim of the present study was to calculate the overall cesarean section rate, to identify groups of women (distributed according to Robson's Ten Group classification system) that contributed most to the overall cesarean section rate and to analyze cesarean section rates within groups in our institute.Methods: A cross-sectional study was conducted for a period of 1year from January 2019 to December 2019.All pregnant women with gestational age of more than 28 weeks, delivered during the study period were classified according to Robson's ten-group classification system.Cesarean section rate, group size, group cesarean section rate and absolute and relative contribution of each group to caesarian section (CS) rate were calculated and analysis was done.Results: The overall cesarean section rate was 42.39%.Group 3 included multiparous women (excluding previous cesarean section), with single pregnancy cephalic, at term in spontaneous labour, was the largest group (group size 29.87%).The second largest group was group 1 included nulliparous women with single pregnancy cephalic at term (group size 23.49%), with a CS rate of 34.51%.Group 5, which consists of multiparous women, with at least one previous section and single pregnancy in a cephalic presentation at term, was the largest contributor to overall caesarean section rate (38.69%).Conclusion: Women with previous cesarean section constitute the most important determinant of overall cesarean section rates.Decreasing the primary caesarean section rates is the key to reducing overall caesarean section rates.