Reviewerly/exHarmony huggingface dataset:
Identifying Confounders Using Bayesian Networks and Estimating Treatment Effect in Prostate Cancer With Observational Data

Randomized controlled trials are considered the golden standard for estimating treatment effect but are costly to perform and not always possible. Observational data, although readily available, is sensitive to biases such as confounding by indication. Structure learning algorithms for Bayesian Networks (BNs) can be used to discover the underlying model from data. This enables identification of confounders through graph analysis, although the model might contain noncausal edges. We propose using a blacklist to aid structure learning in finding causal relationships. This is illustrated by an analysis into the effect of active treatment (v observation) in localized prostate cancer.In total, 4,121 prostate cancer records were obtained from the Netherlands Cancer Registry. Subsequently, we developed a (causal) BN using structure learning while precluding noncausal relations. Additionally, we created several Cox proportional hazards models, each correcting for a different set of potential confounders (including propensity scores). Model predictions for overall survival were compared with expected survival on the basis of the general population using data from Statistics Netherlands (Centraal Bureau voor de Statistiek).Structure learning precluding noncausal relations resulted in a causal graph but did not identify significant edges toward treatment; they were added manually. Graph analysis identified year of diagnosis and age as confounders. The BN predicted a treatment effect of 1 percentage point at 10 years. Chi-squared analysis found significant associations between year of diagnosis, age, stage, and treatment. Propensity score correction was successful. Adjusted Cox models predicted significant treatment effect around 3 percentage points at 10 years.A blacklist in conjunction with structure learning can result in a causal BN that can be used for confounder identification. Treatment effect found here is close to the 5 percentage point found in the literature.
Performance and stability Comparison of React and Flutter: Cross-platform Application Development

Application development is a very important aspect when it comes to business. From small-scale companies to Fortune Companies everyone has an application through which they are running their business whether it is a web-based application or Mobile Application. Now the Mobile market is majorly captured by two Operating systems Android and iOS. Initially, to develop an application for a single project for both Systems separately, companies need to hire separate developers. But ReactJs is a Cross-Platform Application development library and is used to create a frontend for applications. It is majorly used by Facebook, Instagram, Pinterest, etc. It is JavaScript Library. Now Google has also launched its pure cross-platform framework called Flutter. Flutter uses Dart programming language to run. An open-source framework created by Google in 2017 to create applications from a single codebase. This research paper presents the study and comparison of the two most famous cross-platform application development technologies. It starts by discussing the basic functions of the application development methodologies. Followed by, it contains a comparison of the performance of the applications developed by these technologies. After that Stability of applications made by the flutter and React Native is checked on different parameters. There is an implementation of an application using Flutter and React Native which will be used for performance analysis between two applications running Android and Web platforms
Contrastive Accelerometer–Gyroscope Embedding Model for Human Activity Recognition

Recently, the widespread use of smart devices has invoked more interest in sensor-based applications. Human activity recognition (HAR) with body-worn sensors is an underlying task that aims to recognize a person’s physical activity from on-body sensor readings. In this article, we address the HAR problem that utilizes accelerometers and gyroscopes. While deep-learning-based feature extraction methods are advancing, recent HAR systems with multimodal sensors mostly use data-level fusion, aggregating different sensor signals into one multichannel input. However, they neglect the fact that different sensors capture different physical properties and produce distinct patterns. In this article, we propose a two-stream convolutional neural network (CNN) model that processes the accelerometer and gyroscope signals separately. The modality-specific features are fused in feature-level and jointly used for the recognition task. Furthermore, we introduce a self-supervised learning (SSL) task that pairs the accelerometer and the gyroscope embeddings acquired from the same activity instance. This auxiliary objective allows the feature extractors of our model to communicate during training and exploit complementary information, achieving better representations for HAR. We name our end-to-end multimodal HAR system the Contrastive Accelerometer–Gyroscope Embedding (CAGE) model. CAGE outperforms preceding HAR models in four publicly available benchmarks. The code is available at github.com/quotation2520/CAGE4HAR.
An efficient reversible data hiding using SVD over a novel weighted iterative anisotropic total variation based denoised medical images

Computed tomography (CT) advancement and extensive usage have raised the public's worry regarding the patient's associated radiation dose. Reducing the radiation dose may lead to more noise and artifacts, which may harm the reputation of radiologists. The instability of low-dose CT reconstruction necessitates better image reconstruction, which increases the diagnostic performance. More modern low-dose CT tests have demonstrated outstanding results. Many times these low-dose denoised medical images with medical related information are also required to transmit over a network. Hence in this article, firstly is a novel denoising method is proposed to improve the quality of low-dose CT images that is based on the total variation method by utilizing whale optimization algorithm (WHA). WHA method is important for getting the best possible weighted function. Reduction of noise occurs by the comparison of a given output to the ground truth, although total variation tends to statistically migrate the data noise distribution from strong to weak. Following denoising, a reversible watermarking approach based on SVD and multi-local extrema (MLE) approaches is provided. Individual results of denoising and watermarking are excellent in terms of visual and performance metrics, according to comparative experimental investigation. Also it was also analyzed that if the watermark is embedded over the denoised CT images then the results of watermarking methods are impressive. So, resultant image offers us the chance to use our visual perception abilities to allow us to cut noise and keep vital and secure information.
A Human-Centered Approach to Developer Productivity

WE LEAD A mixed-methods research team at Google that seeks to understand what makes engineers productive and happy.We explore the impact of different engineering tools, infrastructure, processes, and best practices on engineering productivity.
Optimized Doctor Recommendation System using Supervised Machine Learning

In the past decade, we have seen many patients and healthcare problems. Due to this, patients find difficulty choosing doctors according to their disease. Several Machine Learning (ML) based techniques already exist to predict doctors based on patient’s health conditions. However, it is essential to accurately recommend doctors to patients with low errors based on patients’ health conditions. Therefore, we propose a method that assigns quantitative importance (weight) to each feature using an ML technique. Moreover, we offer a framework to recommend doctors based on the similarity score and doctor’s skill score, which utilizes weight prediction to enhance operational efficiency. Additionally, on real-world datasets, the effectiveness of the proposed framework is demonstrated empirically by lowering the average loss by roughly 34% and 3% as compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), respectively. The outcome demonstrates that the algorithm can efficiently recommend doctors to patients compared to state-of-the-art techniques. This analysis technique aid patients in opting for the right doctor.
Is psychological science progressing? Explained variance in PsycINFO articles during the period 1956 to 2022

We aimed to numerically assess the progress of modern psychological science. Average explained variance in 1565 included articles was 42.8 percent, and this was constant during 1956 to 2022. We explored whether this could be explained by a combination of methodological conventions with the semantic properties of the involved variables. Using latent semantic analysis (LSA) on a random sample of 50 studies from the 1,565, we were able to replicate the possible semantic factor structures of 205 constructs reported in the corresponding articles. We argue that the methodological conventions pertaining to factor structures will lock the possible explained variance within mathematical constraints that will make most statistics cluster around 40 percent explained variance. Hypotheses with close to 100 percent semantic truth value will never be part of any assumed empirical study. Nor will hypotheses approaching zero truth value. Hypotheses with around 40 percent truth value will probably be experienced as empirical and plausible and, consequently, as good candidates for psychological research. Therefore, to the extent that the findings were indeed produced by semantic structures, they could have been known without collecting data. Finally, we try to explain why psychology had to abandon an individual, causal method and switch to studying whether associations among variables at the group level differ from chance. Psychological processes take place in indefinitely complex and irreversibly changing contexts. The prevalent research paradigm seems bound to producing theoretical statements that explain each other to around 40%. Any theoretical progress would need to address and transcend this barrier.
On the performance of non‐profiled side channel attacks based on deep learning techniques

Abstract In modern embedded systems, security issues including side‐channel attacks (SCAs) are becoming of paramount importance since the embedded devices are ubiquitous in many categories of consumer electronics. Recently, deep learning (DL) has been introduced as a new promising approach for profiled and non‐profiled SCAs. This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non‐profiled attacks on the AES‐128 encryption implementation. Especially, the proposed network is fine‐tuned with different number of hidden layers, labelling techniques and activation functions. Along with the designed models, a dataset reconstruction and labelling technique for the proposed model has also been performed for solving the high dimension data and imbalanced dataset problem. As a result, the DL based SCA with our reconstructed dataset for different targets of ASCAD, RISC‐V microcontroller, and ChipWhisperer boards has achieved a higher performance of non‐profiled attacks. Specifically, necessary investigations to evaluate the efficiency of the proposed techniques against different SCA countermeasures, such as masking and hiding, have been performed. In addition, the effect of the activation function on the proposed DL models was investigated. The experimental results have clarified that the exponential linear unit function is better than the rectified linear unit in fighting against noise generation‐based hiding countermeasure.
Qade: solving differential equations on quantum annealers

Abstract We present a general method, called Qade, for solving differential equations using a quantum annealer. One of the main advantages of this method is its flexibility and reliability. On current devices, Qade can solve systems of coupled partial differential equations that depend linearly on the solution and its derivatives, with non-linear variable coefficients and arbitrary inhomogeneous terms. We test this through several examples that we implement in state-of-the-art quantum annealers. The examples include a partial differential equation and a system of coupled equations. This is the first time that equations of these types have been solved in such devices. We find that the solution can be obtained accurately for problems requiring a small enough function basis. We provide a Python package implementing the method at gitlab.com/jccriado/qade .
An Energy-Efficient Transformer Processor Exploiting Dynamic Weak Relevances in Global Attention

Transformer-based models achieve tremendous success in many artificial intelligence (AI) tasks, outperforming conventional convolution neural networks (CNNs) from natural language processing (NLP) to computer vision (CV). Their success relies on the self-attention mechanism that provides a global rather than local receptive field as CNNs. Despite its superiority, the global–level self-attention consumes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 100\times $ </tex-math></inline-formula> more operations than CNNs and cannot be effectively handled by the existing CNN processor due to the distinct operations. It inspires an urgent requirement to design a dedicated Transformer processor. However, global self-attention involves massive naturally existent weakly related tokens (WR-Tokens) due to the redundant contents in human languages or images. These WR-Tokens generate zero and near-zero attention results that introduce energy consumption bottleneck, redundant computations, and hardware under-utilization issues, making it challenging to achieve energy-efficient self-attention computing. This article proposes a Transformer processor effectively handling the WR-Tokens to solve these challenges. First, a big-exact-small-approximate processing element (PE) reduces multiply-and-accumulate (MAC) energy for WR-Tokens by adaptively computing the small values approximately while computing the large values exactly. Second, a bidirectional asymptotical speculation unit captures and removes redundant computations of zero attention outputs by exploiting the local property of self-attention. Third, an out-of-order PE-line computing scheduler improves hardware utilization for near-zero values by reordering the operands to dovetail two operations into one multiplication. Fabricated in a 28-nm CMOS technology, the proposed processor occupies an area of 6.82 mm2. When evaluated with a 90% of approximate computing for the generative pre-trained transformer 2 (GPT-2) model, the peak energy efficiency is 27.56 TOPS/W under 0.56 V at 50 MHz, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17.66\times $ </tex-math></inline-formula> higher than A100 graphics processing unit (GPU). Compared with the state-of-the-art Transformer processor, it reduces energy by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.57\times $ </tex-math></inline-formula> and offers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.73\times $ </tex-math></inline-formula> speedup.
A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model

Autonomous driving technology, especially motion planning and the trajectory tracking method, is the foundation of an intelligent interconnected vehicle, which needs to be improved urgently. Currently, research on path planning methods has improved, but few of the current studies consider the vehicle’s nonlinear characteristics in the reference model, due to the heavy computational effort. At present, most of the algorithms are designed by a linear vehicle model in order to achieve the real-time performance at the cost of lost accuracy. To achieve a better performance, the dynamics and kinematics characteristics of the vehicle must be simulated, and real-time computing ensured at the same time. In this article, a Takagi–Sugeno fuzzy-model-based closed-loop rapidly exploring random tree algorithm with on-line re-planning process is applied to build the motion planner, which effectively improves the vehicle performance of dynamic obstacle avoidance, and plans the local obstacle avoidance path in line with the dynamic characteristics of the vehicle. A nonlinear vehicle model is integrated into the motion planner design directly. For fast local path planning mission, the Takagi–Sugeno fuzzy modelling method is applied to the modeling process in the planner design, so that the vehicle state can be directly utilized into the path planner to create a feasible path in real-time. The performance of the planner was evaluated by numerical simulation. The results demonstrate that the proposed motion planner can effectively generate a reference trajectory that guarantees driving efficiency with a lower re-planning rate.
Critical Success Factors Evaluation for Blockchain’s Adoption and Implementing

Blockchain has completely changed how business is performed today, thus making it one of the most disruptive technologies in recent times. However, it is a challenging task to adopt and implement blockchain technologies in different services and industries. Therefore, this study introduces a framework for investigating critical factors influencing the successful adoption of blockchain technologies in different applications and prioritizes them using the hierarchical Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. First, it provides fourteen critical success factors with the help of the extant literature and further classifies them into three categories: technological, organizational, and environmental. In addition, a set of sixteen key performance indicators (KPI) of successful blockchain adoption is introduced and classified into five categories: overall performance, system robustness, data robustness, accessibility, and overall cost. Then, the fourteen success factors are ranked based on their degree of prominence and relationships. It is concluded that environmental factors are the most critical factors for successful blockchain adoption, and law and policies and competitive pressure are the top two factors needed for blockchain adoption. In the technological context, only blockchain scalability is ranked among the top significant factors for blockchain adoption. On the other hand, adequate resources, top management support, and financial constraints are highly ranked in the organizational context.
Cross-Image Dependency Modeling for Breast Ultrasound Segmentation

We present a novel deep network (namely BUSSeg) equipped with both within- and cross-image long-range dependency modeling for automated lesions segmentation from breast ultrasound images, which is a quite daunting task due to (1) the large variation of breast lesions, (2) the ambiguous lesion boundaries, and (3) the existence of speckle noise and artifacts in ultrasound images. Our work is motivated by the fact that most existing methods only focus on modeling the within-image dependencies while neglecting the cross-image dependencies, which are essential for this task under limited training data and noise. We first propose a novel cross-image dependency module (CDM) with a cross-image contextual modeling scheme and a cross-image dependency loss (CDL) to capture more consistent feature expression and alleviate noise interference. Compared with existing cross-image methods, the proposed CDM has two merits. First, we utilize more complete spatial features instead of commonly used discrete pixel vectors to capture the semantic dependencies between images, mitigating the negative effects of speckle noise and making the acquired features more representative. Second, the proposed CDM includes both intra- and inter-class contextual modeling rather than just extracting homogeneous contextual dependencies. Furthermore, we develop a parallel bi-encoder architecture (PBA) to tame a Transformer and a convolutional neural network to enhance BUSSeg's capability in capturing within-image long-range dependencies and hence offer richer features for CDM. We conducted extensive experiments on two representative public breast ultrasound datasets, and the results demonstrate that the proposed BUSSeg consistently outperforms state-of-the-art approaches in most metrics.
Channel-Resilient Deep-Learning-Driven Device Fingerprinting Through Multiple Data Streams

Enabling accurate and automated identification of wireless devices is critical for allowing network access monitoring and ensuring data authentication for large-scale IoT networks. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitters’ inevitable hardware impairments that occur during manufacturing. Although deep learning is proven efficient in classifying devices based on hardware impairments, the performance of deep learning models suffers greatly from variations of the wireless channel conditions, across time and space. To the best of our knowledge, we are the first to propose leveraging MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification framework. We begin by showing that for AWGN channels, combining multiple received signals improves the testing accuracy by up to 30%. We then show that for more realistic Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to 50% when the models are trained and tested over the same channel, and by up to 69% when the models are tested on a channel that is different from that used for training.
Uncertainty-aware semi-supervised few shot segmentation

Few shot segmentation (FSS) aims to learn pixel-level classification of a target object in a query image using only a few annotated support samples. This is challenging as it requires modeling appearance variations of target objects and the diverse visual cues between query and support images with limited information. To address this problem, we propose a semi-supervised FSS strategy that leverages additional prototypes from unlabeled images with uncertainty guided pseudo label refinement. To obtain reliable prototypes from unlabeled images, we meta-train a neural network to jointly predict segmentation and estimate the uncertainty of predictions. We employ the uncertainty estimates to exclude predictions with high degrees of uncertainty for pseudo label construction to obtain additional prototypes from the refined pseudo labels. During inference, query segmentation is predicted using prototypes from both support and unlabeled images including low-level features of the query images. Our approach can easily supplement existing approaches without the requirement of additional training when employing unlabeled samples. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our model can effectively remove unreliable predictions to refine pseudo labels and significantly improve upon baseline performance.