Reviewerly/exHarmony huggingface dataset:
Deep Learning Toolkit-Accelerated Analytical Co-Optimization of CNN Hardware and Dataflow

The continuous growth of CNN complexity not only intensifies the need for hardware acceleration but also presents a huge challenge. That is, the solution space for CNN hardware design and dataflow mapping becomes enormously large besides the fact that it is discrete and lacks a well behaved structure. Most previous works either are stochastic metaheuristics, such as genetic algorithm, which are typically very slow for solving large problems, or rely on expensive sampling, e.g., Gumbel Softmax-based differentiable optimization and Bayesian optimization. We propose an analytical model for evaluating power and performance of CNN hardware design and dataflow solutions. Based on this model, we introduce a co-optimization method consisting of nonlinear programming and parallel local search. A key innovation in this model is its matrix form, which enables the use of deep learning toolkit for highly efficient computations of power/performance values and gradients in the optimization. In handling power-performance tradeoff, our method can lead to better solutions than minimizing a weighted sum of power and latency. The average relative error of our model compared with Timeloop is as small as 1%. Compared to state-of-the-art methods, our approach achieves solutions with up to 1.7 × shorter inference latency, 37.5% less power consumption, and 3 × less area on ResNet 18. Moreover, it provides a 6.2 × speedup of optimization runtime.
Exploring how Temporal Framing Affects Trust with Time-series Visualizations

Trust is one of the most relevant factors when users build knowledge from visualization to predict whether they will use the represented information. In particular, trust perception is the user’s subjective evaluation of the quality and reliability of the visualized information. However, research leveraging information visualization techniques to study trust perception is limited. This work studies whether varying the temporal framing of line charts affects trust perception in an uncertain scenario. Our results suggest that granularity may be relevant for time-based visualization design. In particular, individuals trust more in a line chart with a higher number of data points and interact more with a line chart in which they trust less. These findings contribute to the state-of-the-art research in visual analytic systems by empowering designers to understand how trust perception in a health emergency scenario varies for line charts with different temporal frames.
Multisensor Estimation Fusion on Statistical Manifold

In the paper, we characterize local estimates from multiple distributed sensors as posterior probability densities, which are assumed to belong to a common parametric family. Adopting the information-geometric viewpoint, we consider such family as a Riemannian manifold endowed with the Fisher metric, and then formulate the fused density as an informative barycenter through minimizing the sum of its geodesic distances to all local posterior densities. Under the assumption of multivariate elliptical distribution (MED), two fusion methods are developed by using the minimal Manhattan distance instead of the geodesic distance on the manifold of MEDs, which both have the same mean estimation fusion, but different covariance estimation fusions. One obtains the fused covariance estimate by a robust fixed point iterative algorithm with theoretical convergence, and the other provides an explicit expression for the fused covariance estimate. At different heavy-tailed levels, the fusion results of two local estimates for a static target display that the two methods achieve a better approximate of the informative barycenter than some existing fusion methods. An application to distributed estimation fusion for dynamic systems with heavy-tailed process and observation noises is provided to demonstrate the performance of the two proposed fusion algorithms.
A particle swarm optimization algorithm based on an improved deb criterion for constrained optimization problems

To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the information of 'excellent' infeasible solutions. The algorithm uses this information to escape from the local best solution and quickly converge to the global best solution. Additionally, to further improve the global search ability of the algorithm, the DE strategy is used to optimize the personal best position of the particle, which speeds up the convergence speed of the algorithm. The performance of our method was tested on 24 benchmark problems from IEEE CEC2006 and three real-world constraint optimization problems from CEC2020. The simulation results show that the CPSO algorithm is effective.
Algorithmic Dependent-Type Theory of Situated Information and Context Assessments

The paper introduces an enriched formal language of information that establishes propositions dependent on situations and propositional types. The language supports structured data of situated information, which can be partial, parametric, and underspecified. Information can be associated with quantitative evaluations depending on situations. The formal terms can integrate propositional types of situated information with statistical and other quantitative evaluations. Structured content integrated with quantitative data facilitates development of new techniques for amalgamating logic representation of situated, propositional content with numerical data. Numerical data can be provided by techniques from mathematical statistics and probability.
Cost-Sensitive Learning and Threshold-Moving Approach to Improve Industrial Lots Release Process on Imbalanced Datasets

With Industry 4.0, companies must manage massive and generally imbalanced datasets. In an automotive company, the lots release decision process must cope with this problem by combining data from different sources to determine if a selected group of products can be released to the customers. This work focuses on this process and aims to classify the occurrence of customer complaints with a conception, tune and evaluation of five ML algorithms, namely XGBoost (XGB), LightGBM (LGBM), CatBoost (CatB), Random Forest(RF) and a Decision Tree (DT), based on an imbalanced dataset of automatic production tests. We used a non-sampling approach to deal with the problem of imbalanced datasets by analyzing two different methods, cost-sensitive learning and threshold-moving. Regarding the obtained results, both methods showed an effective impact on boosting algorithms, whereas RF only showed improvements with threshold-moving. Also, considering both approaches, the best overall results were achieved by the threshold-moving method, where RF obtained the best outcome with a F1-Score value of 76.2%.
Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach

The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1-5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics.
Improvement of terahertz images by adaptive discrete cosine transform (DCT)-based denoising

Due to certain hardware limitations the quality of terahertz images is often lower than desired, which makes it difficult to extract valuable information from them. The goal of this paper is to investigate possibilities to overcome some of these limitations by means of digital image processing. The research is held on a set of images obtained at different distances from the source of terahertz radiation at 0.1 THz frequency. It is shown that the noise in these images is mixed and has a significant level of spatial correlation. For image quality enhancement a fully automatic denoising method based on the use of a discrete cosine transform with a spatially adapted spectrum is proposed. It is shown that despite an initially low spatial resolution of terahertz images and intensive noise, it provides a good noise reduction with a good preservation of edges, which allows one to noticeably improve the quality of these images and make them more convenient for visual analysis carried out by a human operator.
End-to-End One-Shot Path-Planning Algorithm for an Autonomous Vehicle Based on a Convolutional Neural Network Considering Traversability Cost

Path planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively.
System for Semi-Automated Literature Review Based on Machine Learning

This paper presents the design and implementation of a system for semi-automating the literature review process based on machine learning. By using machine learning algorithms, the system determines whether scientific papers belong to the topic that is being explored as part of the review process. The system’s user interface allows the process of creating a literature review to be managed through a series of steps: selecting data sources, building queries and topic searches, displaying the scientific papers found, selecting papers that belong to the set of desired papers, running machine learning algorithms for learning and automated classification, and displaying and exporting the final set of papers. Manual literature reviews are compared with automated reviews, and similarities and differences between the two approaches in terms of duration, accuracy, and ease of use are discussed. This study concludes that the best results in terms of sensitivity and accuracy for the automated literature review process are achieved by using a combined machine learning model, which uses multiple unweighted machine learning models. Cross-testing the models on two alternative datasets revealed an overlap in the machine learning hyperparameters. The stable sensitivity and accuracy in the tests indicate the potential for generalized use in automated literature review.
Digital Tools in Education as Part of Ecosystem Approach during Foreign Language Acquisition

The ecosystem approach develops communication skills and promotes creativity by digitalizing the academic environment. The authors analyzed the ecosystem approach from economic, philosophical, psychological, and pedagogical perspectives. They described the possibilities of using digital tools in the process of teaching a foreign language to university students of IT departments. The article introduces the basic trends in academic digitalization. Digital tools can organize the academic environment in the process of teaching a foreign language, e.g., the Miro virtual whiteboard, the Nearpod platform, the 4portfolio.ru portal, the Google Calendar service, etc. An empirical survey proved that the ecosystem approach is an efficient tool of second language acquisition, which develops creative interaction, generates innovative solutions, increases positive motivation, develops business communication skills, and provides business communication experience.
An Ensemble Approach for Detecting Malaria Using Classification Algorithms

Health care is an integral part of our human life. The field of health care has grown beyond imagination in the last few years with the impact of machine learning. Machine learning applications that are using data have a greater role in health care. These applications are used to solve many health issues in the society. However, developing these applications involves transforming the data from the original format to a format that is understandable by the system. It also involves using suitable machine learning algorithms appropriate for the problem to be solved. This work discusses about a framework that is developed to detect malaria, based on the medical data captured from the patient’s records who were admitted in Father Muller Medical college hospital from 2014 to 2018 for malaria. Three algorithms have been used in this experimental study. Out of XGB classifier, random forest classifier, and gradient boost classification algorithms, random forest algorithm gave us a better accuracy of 90.97%.
Current status, application, and challenges of the interpretability of generative adversarial network models

Abstract The generative adversarial network (GAN) is one of the most promising methods in the field of unsupervised learning. Model developers, users, and other interested people are highly concerned about the GAN mechanism where the generative model and the discriminative model learn from each other in a gameplay manner, which generates a causal relationship among output features, internal network structure, feature extraction process, and output results. Through the study of the interpretability of GANs, the validity, reliability, and robustness of the application of GANs can be verified, and the weaknesses of the GANs in specific applications can be diagnosed, which can provide support for designing better network structures. It can also improve security and reduce the decision‐making and prediction risks brought by GANs. In this article, the study of the interpretability of GANs is explored, and ways of the evaluation of the application effect of GAN interpretability techniques are analyzed. Besides, the effect of interpretable GANs in fields such as medical treatment and military is discussed, and current limitations and future challenges are demonstrated.
Using Whole Slide Gray Value Map to Predict HER2 Expression and FISH Status in Breast Cancer

Accurate detection of HER2 expression through immunohistochemistry (IHC) is of great clinical significance in the treatment of breast cancer. However, manual interpretation of HER2 is challenging, due to the interobserver variability among pathologists. We sought to explore a deep learning method to predict HER2 expression level and gene status based on a Whole Slide Image (WSI) of the HER2 IHC section. When applied to 228 invasive breast carcinoma of no special type (IBC-NST) DAB-stained slides, our GrayMap+ convolutional neural network (CNN) model accurately classified HER2 IHC level with mean accuracy 0.952 ± 0.029 and predicted HER2 FISH status with mean accuracy 0.921 ± 0.029. Our result also demonstrated strong consistency in HER2 expression score between our system and experienced pathologists (intraclass correlation coefficient (ICC) = 0.903, Cohen's κ = 0.875). The discordant cases were found to be largely caused by high intra-tumor staining heterogeneity in the HER2 IHC group and low copy number in the HER2 FISH group.
VF-CART: A communication-efficient vertical federated framework for the CART algorithm

With growing concerns about privacy and the fact that data are distributed among multiple parties in realistic scenarios, vertical federated learning (VFL) is becoming increasingly important. There is an increasing trend in adapting machine learning algorithms to the VFL setting. As a category of prevalent machine learning algorithms, decision tree and random forests in VFL have attracted widespread interest. However, existing frameworks suffer either from potential privacy breaches or high communication consumption. To close this gap, we propose a communication-efficient vertical federated framework for the classification and regression tree (CART) algorithm called VF-CART, and extend it to random forests (RFs). Specifically, we convert feature values into bin values and build a histogram for each feature. By employing a hash function and homomorphic encryption to secretly choose the best split, a participant with labels cannot obtain the sample subsets for each split. In addition, the number of ciphertexts transmitted between entities is reduced significantly in both the training and prediction stages. Participants who do not have the labels communicated only once with the third-party server during the tree-building stage. During the prediction stage, only one ciphertext must be transmitted to predict a sample. Finally, we conducted experiments using both real-world and synthetic datasets. The experimental results demonstrate that the VF-CART algorithm significantly reduced the volume of communication.
Semi-Supervised Clustering Ensemble Based on Cluster Consensus Selection

AbstractAbstractEnsemble clustering emerged as an important extension of classical clustering problems and is one of the most recent advances in unsupervised learning. Its purpose is to combine the results obtained using different algorithms by a consensus function so that the final solution is more favorable than the individual clustering algorithms. In this study, we propose a semi-supervised clustering ensemble framework using cluster consensus selection, which tries to improve the accuracy of clustering results. In general, there are two types of semi-supervised clustering algorithms, including constraint-based and metric-based. Here, the proposed ensemble clustering algorithm is equipped with a semi-supervised clustering mechanism based on pairwise constraints. Since the complexity of consensus functions scales with the number of clustering methods, processing big data for ensemble clustering is sometimes slow or impossible. Usually, all primary clusters from all clustering methods are used in the consensus function. However, the merit of clusters from different methods can be considered to improve the consensus quality. Accordingly, we propose a cluster consensus selection approach that selects a subset of meriting primary clusters to participate in the final consensus. Here, Normalized Mutual Information (NMI) is developed to measure the merit of clusters. Meanwhile, reducing the number of primary clusters in the consensus function can enable big data clustering. The proposed algorithm is very computationally efficient and provides linear complexity in clustering. Experimental results show the effectiveness of the proposed algorithm in terms of different performance metrics such as NMI, ARI and CPCC.Keywords: Consensus selectionensemble clusteringNMIsemi-supervised clustering Data AvailabilityNo data were used to support this study.
The Digital Chameleon: Factors Affecting Perceptions of Convergence in Computer-Mediated Communication

Communication Accommodation Theory (CAT) is a framework used to analyze and predict linguistic and behavioral adjustments within interpersonal communication. As computer-mediated communication (CMC) becomes more prevalent, applying this framework provides insight into interpersonal communication via a screen. Two studies explored CAT applied to CMC with participants observing linguistic adjustments in text-message screenshots. Results indicated that likability, empathy, gender, and textisms each play a role in how we perceive and interact with others during digital communications. Implications are discussed.
Differentially private block coordinate descent

Deep learning models have revolutionized AI tasks by producing accurate predictions. These models′ success largely depends on precise training using large-scale datasets, mostly crowdsourced from the target population. The training datasets may contain sensitive personal information, and the model parameters can encode this information on internal wirings of hidden layers, thus bearing the risk of a privacy breach. The modern trend of sharing trained models has grown the privacy breach risk manifold. The performance of existing privacy-preserving deep learning models trying to address this issue is unsatisfactory. Consequently, only a marginal proportion of these privacy-preserved models have been adopted in the industry. Therefore, we developed the first differentially private version of the block coordinate descent (BCD) algorithm. Our proposed mechanism considerably cuts the privacy cost by injecting an appropriate amount of noise in block variables. It achieves high accuracy comparable to its non-private equivalents. It improves the convergence speed and provides a provable privacy guarantee by performing privacy accounting using advanced composition and moments accountant methods. We empirically evaluate the robustness of our proposed mechanism on benchmark datasets. The results demonstrate the competitive performance in terms of both privacy cost reduction and speedy convergence against the state-of-the-art differential privacy-based mechanisms.
Comparison Fletcher-Reeves and Polak-Ribiere ANN Algorithm for Forecasting Analysis

Abstract Each method and algorithm ANN has different performances depending on the algorithm used and the parameters given. The purpose of this research is to obtain the best algorithm information from the two algorithms that will be compared based on the performance value or the smallest / lowest MSE value so that it can be used as a reference and information for solving forecasting problems. The ANN algorithms compared were Conjugate Gradient Fletcher-Reeves and Conjugate Gradient Polak-Ribiere. The conjugate gradient algorithm can solve unlimited optimization problems and is much more efficient than gradient descent-based algorithms because of its faster turnaround time and less iteration. The research data used for the forecasting analysis of the two algorithms are data on the number of rural poor people in Sumatra, Indonesia. 6-10-1, 6-15-1, and 6-20-1 architectural analysis. The results showed that the Polak-Ribiere Conjugate Gradient algorithm with the 6-10-1 architecture has the best performance results and the smallest / lowest MSE value compared to the Fletcher-Reeves algorithm and two other architectures. So it can be concluded that the 6-10-1 architectural architecture with the Conjugate Gradient Polak-Ribiere algorithm can be used to solve forecasting problems because the training time to achieve convergence is not too long, and the resulting performance is quite good.