Reviewerly/exHarmony huggingface dataset:
A comparison between clinical decision support system and clinicians in breast cancer

We are building a clinical decision support system (CSCO AI) for breast cancer patients to improve the efficiency of clinical decision-making. We aimed to assess cancer treatment regimens given by CSCO AI and different levels of clinicians.400 breast cancer patients were screened from the CSCO database. Clinicians with similar levels were randomly assigned one of the volumes (200 cases). CSCO AI was asked to assess all cases. Three reviewers were independently asked to evaluate the regimens from clinicians and CSCO AI. Regimens were masked before evaluation. The primary outcome was the proportion of high-level conformity (HLC).The overall concordance between clinicians and CSCO AI was 73.9% (3621/4900). It was 78.8% (2757/3500) in the early-stage, higher than that in the metastatic stage (61.7% [864/1400], p < 0.001). The concordance was 90.7% (635/700) and 56.4% (395/700) in adjuvant radiotherapy and second-line therapy respectively. HLC in CSCO AI was 95.8% (95%CI:94.0%-97.6%), significantly higher than that in clinicians (90.8%, 95%CI:89.8%-91.8%). Considering professions, the HLC of surgeons was 85.9%, lower than that of CSCO AI (OR = 0.25,95%CI: 0.16-0.41). The most significant difference in HLC was in first-line therapy (OR = 0.06, 95%CI:0.01-0.41). When clinicians were divided according to their levels, there was no statistical significance between CSCO AI and higher level clinicians.Decision from CSCO AI for breast cancer was superior than most clinicians did except in second-line therapy. The improvements in process outcomes suggest that CSCO AI can be widely used in clinical practice.
A Paradigm Shift: 5 Cents To 1 Calorie

Innovation is suggestive of ground breaking ideas that take over the markets. In actuality, 70% of the innovation is incremental in nature[1]. With the rapid shift in trade prospects, firms shouldn’t underestimate the potentiality of incremental innovation. To illustrate, according to Bain and Co, a whooping [whopping] $5 Trillion of global GDP has been contributed by modifications in existing consumer products[2]. Software development life cycles (SDLC) are catalysts in providing standardised frameworks that define business operations. It causes structured project planning along with a decrement in the project expenditures and encounter with risks. The study draws attention towards the SDLC models and their employment in The Coca Cola Company. The brand extends a miscellany of developments under the banner of carbonated drinks. Such differentiated products cater to the market heterogeneity that gratifies customer personalisations. A planned approach helps the corporate navigate through unfamiliar sphere of undertakings; from determining novel ingredients to fabricating new chains of the brand. Key Words: Incremental innovation, SDLC
Transcription Free Filler Word Detection with Neural Semi-CRFs

Non-linguistic filler words, such as "uh" or "um", are prevalent in spontaneous speech and serve as indicators for expressing hesitation or uncertainty. Previous works for detecting certain non-linguistic filler words are highly dependent on transcriptions from a well-established commercial automatic speech recognition (ASR) system. However, certain ASR systems are not universally accessible from many aspects, e.g., budget, target languages, and computational power. In this work, we investigate filler word detection system <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that does not depend on ASR systems. We show that, by using the structured state space sequence model (S4) and neural semi-Markov conditional random fields (semi-CRFs), we achieve an absolute F1 improvement of 6.4% (segment level) and 3.1% (event level) on the PodcastFillers dataset. We also conduct a qualitative analysis on the detected results to analyze the limitations of our proposed system.
Oct Image Blind Despeckling Based on Gradient Guided Filter with Speckle Statistical Prior

Optical coherence tomography (OCT) imaging technique has been widely used for ocular disease diagnosis. However, speckles occur in OCT images due to the property of coherent imaging, inevitably affecting the visual quality and clinical analysis. To alleviate this problem, we propose a novel gradient-guided speckle image filtering method (GGSF) with structure enhancement for directly removing speckles in OCT images. Specifically, the multiplicative characteristic of speckle noise is incorporated into the guided filtering processing for modeling raw OCT images. To avoid getting trapped in image distortions, we further employ gradient regularization to integrate the structure prior information into the guided speckle image filtering procedure. Additionally, we introduce the statistical property of speckle noise obeying a gamma distribution into the least square method solver for the resulting non-convex GGSF model. Experimental results on the AS-OCT dataset demonstrate the effectiveness of GGSF for OCT image despeckling compared with competitive methods. Furthermore, we validate the benefits of GGSF for subsequent clinical analysis with the CM-OCT dataset.
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing

Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently. However, compared with abstract-syntactic-tree-based SQL generation, seq2seq semantic parsers face much more challenges, including poor quality on schematical information prediction and poor semantic coherence between natural language queries and SQLs. This paper analyses the above difficulties and proposes a seq2seq-oriented decoding strategy called SR, which includes a new intermediate representation SSQL and a reranking method with score re-estimator to solve the above obstacles respectively. Experimental results demonstrate the effectiveness of our proposed techniques and T5-SR-3b achieves new state-of-the-art results on the Spider dataset.
Hierarchical Hypergraph Recurrent Attention Network for Temporal Knowledge Graph Reasoning

Temporal knowledge graph (TKG) serves as an essential tool in modeling complex event relations among real-world entities. A temporal knowledge graph can be viewed as a collection of knowledge graph snapshots ordered by time. Reasoning over such graphs remains nontrivial as temporal causal dependencies between events are hard to capture. Current TKG reasoning methods only model pair-wise relations, which are limited in capturing higher-order dependencies between entities that are beyond dyadic connections. In this work, we aim to capture higher-order interactions of entities for TKG reasoning. To achieve this goal, we develop a Hierarchical Hypergraph Recurrent Attention Network on the type-induced entity hypergraph with multiple hierarchies to model the evolutionary pattern under different semantic granularities. The experimental analysis on benchmark datasets demonstrates the proposed model's superiority and elucidates the rationality of the hierarchical hypergraph modeling.
FCIR: Rethink Aerial Image Super Resolution with Fourier Analysis

Recent years, deep-learning-based methods achieve remarkable improvements on the super-resolution (SR) task. However, recovering high-quality (HQ) texture from the low-quality (LQ) aerial image is still challenging due to the limited contextual modeling ability of current deep-learning methods as well as the sharp artificial texture of aerial images. In this paper, we rethink aerial image super resolution (AISR) task with the perspective of Fourier analysis. Firstly, we build the Fourier Global Convolution (FGC) inspired by the convolution theorem of the Fourier Transform to extract the shadow features. Then, following the Gabor Transform, a carefully designed oriented Texture Contextual Block (OTCB) is proposed to enhance the oriented texture representation. By stacking FGC and OTCB, we propose a simple but effective straight-forward network named Fourier Consistency Image Reconstruction Model (FCIR) to restore HQ aerial image. Moreover, we design a gradient consistency loss (GC Loss) to enhance the quality of reconstructed high-frequency details. Compared with very recent state-of-the-art super-resolution methods, experimental results demonstrate promising SR performance boosts from FCIR on 3 typical aerial image datasets.
M<sup>2</sup>TSR: Multi-Range and Mix-Grained Transformer for Single Image Super-Resolution

Recently, Transformers have shown impressive performance in image super-resolution (SR), due to exploiting strong representation ability of multi-head self-attention (MSA). However, existing methods typically calculate MSA in a single range and granularity, preventing the model from capturing sufficient relationships between pixels, thus leading to inferior representation ability. To address this issue, we propose Multi-range and Mix-grained Transformer (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TSR) for accurate image SR. In particular, we develop Multi-range and Mix-grained Transformer Block (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TB) that construct diverse MSA to extract distinct relationships under various ranges and granularities. The short-range MSA <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</inf> focus on extracting local relationships. Then, the MSA <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> expands ranges to capture the complex relationships inherent in long-range pixels. Meanwhile, fine and coarse-grained features are employed to model distinct relationships at various granularities. Extensive experiments demonstrate the superiority of M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TSR over the SOTA methods. The lightweight variant, M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TSRS, also achieves a better trade-off between performance and computation cost against advanced lightweight methods.
Single-Shot Domain Adaptation via Target-Aware Generative Augmentations

The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural networks. While several test-time adaptation techniques have emerged, they typically rely on synthetic data augmentations in cases of limited target data availability. In this paper, we consider the challenging setting of single-shot adaptation and explore the design of augmentation strategies. We argue that augmentations utilized by existing methods are insufficient to handle large distribution shifts, and hence propose a new approach SiSTA (Single-Shot Target Augmentations), which first fine-tunes a generative model from the source domain using a single-shot target, and then employs novel sampling strategies for curating synthetic target data. Using experiments with a state-of-the-art domain adaptation method, we find that SiSTA produces improvements as high as 20% over existing baselines under challenging shifts in face attribute detection, and that it performs competitively to oracle models obtained by training on a larger target dataset. Our codes can be accessed at github.com/kowshikthopalli/SISTA.
Enhancing Robustness and Imperceptibility of Blind Watermarking with Improved Message Processor

The current state-of-the-art(SOTA) blind watermark embedding method MBRS based on deep learning is less robust to Crop, and additional diffusion layers need to be added for optimization. However, the diffusion layer will make the model less robust to noise other than Crop. Therefore, MBRS which needs to add or delete components is not a practical watermarking framework. Not only that, MBRS is easy to generate chessboard artifacts, resulting in the generated watermark being easy to be detected by the human eye. Therefore, we construct a more generalized watermarking framework and propose an improved blind watermarking method. The method addresses the shortcomings of MBRS by using an improved message processor with sub-pixel convolution layers and low-frequency features and incorporating double discriminators to improve the performance of the network. Extensive experiments show that our method demonstrates superior results compared to the SOTA method.
Clustering-Based Supervised Contrastive Learning for Identifying Risk Items on Heterogeneous Graph

Risk item identification is vital for protecting the health of ecommerce trades. Existing solutions prefer to model structure information besides item attributes and optimize parameters in cross-entropy (CE) manners. However, the few labeled and imbalanced supervision in real-world scenarios usually results in poor generalization of CE optimization. More seriously, the pattern-level difference of risk items is often neglected in binary supervised learning, leading to limited performance. In this paper, we propose a novel Clustering-based Supervised Contrastive Learning (CSCL) to address the two challenges. CSCL first devises a contrastive heterogeneous graph neural network that fully exploits multiple risk relations in contrastive learning, keeping generalization performance. It then designs a clustering-based reweighted sampling strategy to search informative positive and negative training instances for effective pattern-level optimization. We test the performance on Xianyu Platform, and experimental results demonstrate that CSCL outperforms all baselines.
Progressive Perception Learning for Distribution Modulation in Siamese Tracking

We explore an innovative view on distribution modulation to boost Siamese trackers. Specially, we observed two cases of possible distribution inconsistency in Siamese tracking: 1) Two branches with different sizes may be in different distribution ranges after a shared backbone (including BN layers). 2) The background data may affect the total feature distribution of the search branch. To address these issues, we proposed a plug-and-play component named Progressive Perception Learning Module (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> LM) to modulate the distribution using three feature normalization blocks successively, i.e., Self-Aware Block (SAB), Target-Aware Block (TAB), and Region-Aware Block (RAB). SAB regulates the distribution of each branch independently for the first issue. TAB uses the target information to guide the distribution adjustments of the two branches. RAB divides the search image into foreground and background with a region mask and normalizes them separately to filter the background distractors for robust tracking. TAB and RAB synergistically alleviate the distribution shifts caused by environmental variance. Experiments on OTB100, UAV123, LaSOT, and GOT-10k verify the compelling effects of our module.
Adaptive Gaussian Nested Filter for Parameter Estimation and State Tracking in Dynamical Systems

We introduce the adaptive Gaussian nested filter (AGNesF), the first nested method that adapts the number of samples to estimate both the static parameters and the dynamical variables of a state-space model. The proposed method is based on the nested Gaussian filter (NGF), that combines two layers of inference, one inside the other, to compute the joint posterior probability distribution of the static parameters and the state variables. We propose two novel rules to reduce computational complexity without compromising the performance. One enables the bottom layer techniques to run recursively, while the other reduces automatically the number of samples in the parameter space when they are redundant. We describe a specific implementation of the new scheme that uses a quadrature Kalman filter (QKF) in the parameter layer, and we study its performance in a stochastic Lorenz 63 model.
Seperation of Phishing Emails Using Probabilistic Classifiers

The activity of phishing has been carried out in more area nowadays and it is growing with advancements. Till now there are few methods to overcome the phishing but those are not up to the level of expectation. Phishing attempts have grown 65% in the last year and it is still growing rapidly. So that cost of the damage caused by it for mid-size companies is $1.6 million. Our work is to detect malicious emails and do the codification by using probabilistic classification. Victims trust the emails so the phishers can easily hack the information. In this work, a phishing detection method is proposed by using Naive bayes algorithm, SVM, KNN and RF. It involves collecting the datasets, pre-process and filter the data using string to word vector filter. Then classification of email data as spam or not spam and finally analysis about the performance accuracy.
Developing an Automatic Evaluation of Exertion Using a Smart Phone

Human activity recognition (HAR) is an important task in the field of wearable technology and the Internet of Things (IoT). This abstract presents a study on using Edge Impulse and an accelerometer sensor to recognize human activities using an auto-encoder (EON) tuner. The accelerometer sensor data is collected from a wearable device worn on the wrist. The data is then preprocessed and used to train an EON tuner model for recognizing different activities such as sitting, running, and walking. The results of the study show that the EON tuner model can achieve high accuracy in recognizing different human activities. The use of Edge Impulse and the accelerometer sensor in combination with the EON tuner provides a simple and efficient method for recognizing human activities in real-world scenarios. This research highlights the potential of using Edge Impulse and EON tuner for developing low-cost and low-power human activity recognition systems for IoT applications.
An Augmented Gaussian Sum Filter through a mixture Decomposition

Bayesian filtering is an approach to the inference problem for state-space models (SSM), arising in many disciplines of science and engineering. The well known Gaussian filters tackle the problem using Gaussian approximations of the distributions of the hidden state in the model. Gaussian filters however are unable to track multimodal distributions that commonly arise in complex dynamical systems. The Gaussian sum filter (GSF) addresses this problem by using Gaussian mixture approximations of the filtering distribution. The GSF however has important limitations, requiring small width of the component covariances for good performance. Moreover, in many SSMs the estimates provided by the GSF blow up, due to covariance inflation. In this paper, we propose a way of controlling the covariances of the underlying Gaussian mixture. Our approach relies on a well known Gaussian identity, which helps us break down each component of the GSF, the parent, into several children components of smaller width. These smaller components are propagated through the nonlinearities by local linearization, which results in a smaller error than in the standard GSF. To reduce the resulting mixture we use resampling. We refer to our novel approach as augmented Gaussian sum filter (AGSF). We demonstrate the advantages of our approach using a toy example, for which the extended Kalman filter (EKF) and GSF perform poorly due to covariance inflation.
DecomFormer: Decompose Self-Attention Via Fourier Transform for VHR Aerial Image Scene Classification

Very high-resolution (VHR) aerial image scene classification is an essential task for aerial image understanding. Although transformer-based models have demonstrated strong ability in natural image classification, transformer-based methods on VHR aerial image tasks are still lack of concern because the complexity of self-attention in the transformer grows quadratically with the image resolution. To address this issue, we decompose the self-attention via Fourier Transform and propose a novel Fourier self-attention (FSA) mechanism. Based on FSA, we design a highly efficient network named DecomFormer, which learns contextual relationships in the real part and imaginary part of the Fourier field, respectively. Theoretically, the DecomFormer reduces the complexity of the naive self-attention mechanism from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(nlog(n)). Universal experiments on public VHR aerial image classification benchmarks demonstrated the DecomFormer’s efficiency, especially on images with very high-resolution.
Poster Abstract: SenseEMS - Towards A Hand Activity Recognition and Monitoring System for Emergency Medical Services

Emergency Medical Services (EMS) providers use their hands extensively for the rescue operation and providing care to the patients in an EMS scene. Using smartwatch based sensor data, i.e., accelerometer, gyroscope, and magnetometer, we are developing SenseEMS, a system for hand operated EMS intervention detection and real-time monitoring. SenseEMS will use a hybrid deep neural network with appropriate real-time algorithms on the sensor data to detect multiple hand operated activities, i.e. CPR compressions, attaching defibrillation pads and breathing bags, and to provide quality assessment on different metrics of the activity, i.e., the rate and depth of CPR compressions. Our initial results for this ongoing research show promising accuracy. Preliminary survey with 31 anonymous EMS responders suggests that this automated system will be highly beneficial for real-scene application and EMS training.