Reviewerly/exHarmony huggingface dataset:
Visual Features with Spatio-Temporal-Based Fusion Model for Cross-Dataset Vehicle Re-Identification

Vehicle re-identification (Re-Id) is the key module in an intelligent transportation system (ITS). Due to its versatile applicability in metropolitan cities, this task has received increasing attention these days. It aims to identify whether the specific vehicle has already appeared over the surveillance network or not. Mostly, the vehicle Re-Id method are evaluated on a single dataset, in which training and testing of the model is performed on the same dataset. However in practice, this negatively effects model generalization ability due to biased datasets along with the significant difference between training and testing data; hence, the model becomes weak in a practical environment. To demonstrate this issue, we have empirically shown that the current vehicle Re-Id datasets are usually strongly biased. In this regard, we also conduct an extensive study on the cross and the same dataset to examine the impact on the performance of the vehicle Re-Id system, considering existing methods. To address the problem, in this paper, we have proposed an approach with augmentation of the training dataset to reduce the influence of pose, angle, camera color response, and background information in vehicle images; whereas, spatio-temporal patterns of unlabelled target datasets are learned by transferring siamese neural network classifiers trained on a source-labelled dataset. We finally calculate the composite similarity score of spatio-temporal patterns with siamese neural-network-based classifier visual features. Extensive experiments on multiple datasets are examined and results suggest that the proposed approach has the ability to generalize adequately.
Robust Linear Regression: Optimal Rates in Polynomial Time

We obtain robust and computationally efficient estimators for learning several linear models that achieve statistically optimal convergence rate under minimal distributional assumptions. Concretely, we assume our data is drawn from a $k$-hypercontractive distribution and an $\epsilon$-fraction is adversarially corrupted. We then describe an estimator that converges to the optimal least-squares minimizer for the true distribution at a rate proportional to $\epsilon^{2-2/k}$, when the noise is independent of the covariates. We note that no such estimator was known prior to our work, even with access to unbounded computation. The rate we achieve is information-theoretically optimal and thus we resolve the main open question in Klivans, Kothari and Meka [COLT'18]. Our key insight is to identify an analytic condition that serves as a polynomial relaxation of independence of random variables. In particular, we show that when the moments of the noise and covariates are negatively-correlated, we obtain the same rate as independent noise. Further, when the condition is not satisfied, we obtain a rate proportional to $\epsilon^{2-4/k}$, and again match the information-theoretic lower bound. Our central technical contribution is to algorithmically exploit independence of random variables in the sum-of-squares framework by formulating it as the aforementioned polynomial inequality.
Uncertainty Avoider Interval Type II Defuzzification Method

One of the IT2FS (interval type-2 fuzzy system) defuzzification methods uses the iterative KM algorithm. Because of the iterative nature of KM-type reduction, it may be a computational bottleneck for the real-time applications of IT2FSs. There are several other interval type-2 defuzzification methods suffering from lack of meaningful relationship between membership function uncertainties and changing of system output due to lack of clearly defined variables related to uncertainty in their methods. In this paper, a new approach for IT2FS defuzzification is presented by reconfiguring interval type-2 fuzzy sets and how uncertainties are present in them. This closed-formula method provides meaningful relation between the presence of uncertainty and its effect on system output. This study investigates uncertainty avoidance that the output of IT2FS obtained by centroid or bisection methods in comparison with type-1 fuzzy system (T1FLS) moves to points with less uncertainty. Uncertainty can enter into T1FSs and affect system response. Finally, for proving the affectivity of the proposed defuzzification method and uncertainty avoidance, several investigations are done and a prototype two-input one-output IT2FS MATLAB code is enclosed.
Consumer health information needs in China – a case study of depression based on a Social Q&amp;A community

Abstract Background The social Q&amp;A community quickly becomes a popular platform for consumers to find health information because of its convenience and interactivity. Methods Based on the 10,861 depression questions collected in the Zhihu, the largest Q&amp;A platform in China, we divided the healthy information needs description into nine categories with Latent Dirichlet Allocation (LDA). We also divided the healthy information needs type into Physiological, affective and cognitive needs based on the Wilson model. Results The results show that the largest categories are depression symptom and social activities while the less concerned health information is prevention and medical insurance. More attention is paid to cognitive needs. We also find there is no strong correlation between attention and needs type. Conclusions The purpose of this paper is to refine the consumer health information needs types to better understand the consumer health information characteristic in China.
DevOps Project Management Tools for Sprint Planning, Estimation and Execution Maturity

Abstract The goal of DevOps is to cut down the project timelines, increase the productivity, and manage rapid development-deployment cycles without impacting business and quality. It requires efficient sprint management. The objective of this paper is to develop different sprint level project management tools for quick project level Go/No-Go decision making (using real-time projects data and machine learning), sprint estimation technique (gamified-consensus based), statistical understanding of overall project management maturity, project sentiment &amp; perception. An attempt is made to device a model to calibrate the perception or the tone of a project culture using sentiment analysis.
Enhancement of Nonuniformly Illuminated Underwater Images

Underwater image capturing is a challenging task due to attenuation of light in water. Scattering and absorption are the results of light attenuation which lead to faded colors and reduced contrast of images, respectively. To deal with these issues and to provide better visual quality image, various enhancement methods have been proposed. This paper proposes the Dual Domain-based Underwater Image Enhancement (DDUIE) method. DDUIE method provides contrast stretching in approximation band of discrete wavelet transformed image followed by intensity adjustment of different color channels in spatial domain. To further improve the color quality, the image is processed in HSV (Hue–Saturation–Value) color space. Result analysis indicates better results for DDUIE method over state-of-the-art methods. Subjective results of DDUIE method show minimization of the bluish-green effect and reduction of nonuniform illumination up to a certain extent. These lead to enhanced color and image details. Quantitative results show that the Underwater Image Quality Measure (UIQM) and Underwater Color Image Quality Evaluation (UCIQE) values between 1 and 2 and between 0 and 1 have been achieved, respectively, which significantly illustrate that images have been enhanced efficiently and also entropy values between 7 and 8 depict the effectiveness of the proposed method in terms of image details.
Teaching potential of integrated learning technologies Smart, Stem and Steam

At the current stage of digitization of the Russian economy, educational institutions urgently need new, more advanced and effective training technologies that help improve the quality of training of highly competent and competitive specialists who comply with the social order of society digital. These technologies should promote the introduction of robotic and cyber-physical systems in the training of future specialists. The article shows that SMART, STEM and STEAM integrated learning technologies that encompass the theoretical and practical components of educational programs with the help of special technological equipment: robotic complexes, virtual learning tools and cyber systems, can be used as technologies in the digital education. It is proven that the practical application of mathematical, physical, engineering and technological knowledge, modeling and design of robotic cybernetic devices and others, allow us to more effectively solve the tasks of the competition-oriented educational paradigm, create additional conditions for the development and realization of the personal potential of the students. The integrative didactic potential of SMART, STEM and STEAM technologies is determined, ensuring the achievement of the synergistic effect in the educational process and increasing the productivity of education subjects through the combination of the individual means of the technologies studied in a single system. The advantages of digital education are analyzed, which are optimized using the integrative teaching potential of SMART, STEM and STEAM technologies. Based on the characteristics of the integrative didactic potential of SMART, STEM and STEAM technologies, it is concluded that they are promising in the era of the digitization of Russian education and viable to use them in order to maintain the effectiveness of the educational process.
Learning Interpretable and Statistically Significant Knowledge from Unlabeled Corpora of Social Text Messages: A Novel Methodology of Descriptive Text Mining

Though the strong evolution of knowledge learning models has characterized the last few years, the explanation of a phenomenon from text documents, called descriptive text mining, is still a difficult and poorly addressed problem.The need to work with unlabeled data, explainable approaches, unsupervised and domain independent solutions further increases the complexity of this task.Currently, existing techniques only partially solve the problem and have several limitations.In this paper, we propose a novel methodology of descriptive text mining, capable of offering accurate explanations in unsupervised settings and of quantifying the results based on their statistical significance.Considering the strong growth of patient communities on social platforms such as Facebook, we demonstrate the effectiveness of the contribution by taking the short social posts related to Esophageal Achalasia as a typical case study.Specifically, the methodology produces useful explanations about the experiences of patients and caregivers.Starting directly from the unlabeled patient's posts, we derive correct scientific correlations among symptoms, drugs, treatments, foods and so on.1 https://www.kaggle.com/
Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset

Heart failure is a leading cause of mortality and morbidity worldwide. Acute heart failure, broadly defined as rapid onset of new or worsening signs and symptoms of heart failure, often requires hospitalization and admission to the intensive care unit (ICU). This acute condition is highly heterogeneous and less well-understood as compared to chronic heart failure. The ICU, through detailed and continuously monitored patient data, provides an opportunity to retrospectively analyze decompensation and heart failure to evaluate physiological states and patient outcomes.The goal of this study is to examine the prevalence of cardiovascular risk factors among those admitted to ICUs and to evaluate combinations of clinical features that are predictive of decompensation events, such as the onset of acute heart failure, using machine learning techniques. To accomplish this objective, we leveraged tele-ICU data from over 200 hospitals across the United States.We evaluated the feasibility of predicting decompensation soon after ICU admission for 26,534 patients admitted without a history of heart failure with specific heart failure risk factors (ie, coronary artery disease, hypertension, and myocardial infarction) and 96,350 patients admitted without risk factors using remotely monitored laboratory, vital signs, and discrete physiological measurements. Multivariate logistic regression and random forest models were applied to predict decompensation and highlight important features from combinations of model inputs from dissimilar data.The most prevalent risk factor in our data set was hypertension, although most patients diagnosed with heart failure were admitted to the ICU without a risk factor. The highest heart failure prediction accuracy was 0.951, and the highest area under the receiver operating characteristic curve was 0.9503 with random forest and combined vital signs, laboratory values, and discrete physiological measurements. Random forest feature importance also highlighted combinations of several discrete physiological features and laboratory measures as most indicative of decompensation. Timeline analysis of aggregate vital signs revealed a point of diminishing returns where additional vital signs data did not continue to improve results.Heart failure risk factors are common in tele-ICU data, although most patients that are diagnosed with heart failure later in an ICU stay presented without risk factors making a prediction of decompensation critical. Decompensation was predicted with reasonable accuracy using tele-ICU data, and optimal data extraction for time series vital signs data was identified near a 200-minute window size. Overall, results suggest combinations of laboratory measurements and vital signs are viable for early and continuous prediction of patient decompensation.
Multimedia Quality of Experience (QoE): Current Status and Future Direction

Quality of Experience (QoE) is becoming an important factor of User-Centred Design (UCD). The deployment of pure technical measures such as Quality of Service (QoS) parameters to assess the quality of multimedia applications is phasing out due to the failure of those methods to quantify true user satisfaction. Though significant research results and several deployments have occurred and been realized over the last few years, focusing on QoE-based multimedia technologies, several issues both of theoretical and practical importance remain open. Accordingly, the papers of this Special Issue are significant contribution samples within the general ecosystem highlighted above, ranging from QoE in the capture, processing and consumption of next-generation multimedia applications. In particular, a total of five excellent articles have been accepted, following a rigorous review process, which address many of the aforementioned challenges and beyond.
Stream-51: Streaming Classification and Novelty Detection from Videos

Deep neural networks are popular for visual perception tasks such as image classification and object detection. Once trained and deployed in a real-time environment, these models struggle to identify novel inputs not initially represented in the training distribution. Further, they cannot be easily updated on new information or they will catastrophically forget previously learned knowledge. While there has been much interest in developing models capable of overcoming forgetting, most research has focused on incrementally learning from common image classification datasets broken up into large batches. Online streaming learning is a more realistic paradigm where a model must learn one sample at a time from temporally correlated data streams. Although there are a few datasets designed specifically for this protocol, most have limitations such as few classes or poor image quality. In this work, we introduce Stream-51, a new dataset for streaming classification consisting of tempo- rally correlated images from 51 distinct object categories and additional evaluation classes outside of the training distribution to test novelty recognition. We establish unique evaluation protocols, experimental metrics, and baselines for our dataset in the streaming paradigm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Classification Algorithm Optimization Based on Triple-GAN

Generating an Adversarial network (GAN) has shown great development prospects in image generation and semi-supervised learning and has evolved into TripleGAN. However, there are still two problems that need to be solved in Triple-GAN: based on the KL divergence distribution structure, gradients are easy to disappear and training instability occurs. Since Triple-GAN tags the samples manually, the manual marking workload is too large. Marked uneven and so on. This article builds on this improved Triple-GAN model (Improved Triple-GAN), which uses Random Forests to classify real samples, automate tagging of leaf nodes, and use Least Squares Generative Adversarial Networks (LSGAN) ideological structure loss function to avoid gradients disappear. Experiments were performed on the Improved Triple-GAN model and the Triple-GAN model using the MINIST, cifar10 and cifar100 datasets respectively, experiments show that the error rate of generated samples is greatly reduced. At the same time, the classification effect of the data set and the sharpness of the samples are greatly improved. And it has greatly improved the stability of model training and automation of labels.
Virtual Reality learning environments and technological mediation in construction practice

Training based on Virtual Reality (VR) has reached a level of maturity that renders it applicable in many industries. By moving through learner interaction and representational fidelity, from navigable to interactable VR learning environments, the relationships between trainees, VR technology, and the world that trainees experience change. These relationships are central to the theory of technological mediation. In this theory, the central idea is that humans’ perceptions and interpretations of reality are transformed when the latter are mediated by technology. Using this theory, the objective of this study is to provide insights into the different roles that mediation, in the relationship between trainees or users of this technology and construction practice, may play in the new type of context-realistic VR learning environments. To this end, the development of a framework for generating an innovative VR tool for training operators on construction sites was chosen as the subject of a case study. By focusing on the development phases of this framework one is able to scrutinise the underlying elements that shape the changing relationship between trainees, the VR environment, and the real world.
Proper Learning, Helly Number, and an Optimal SVM Bound

The classical PAC sample complexity bounds are stated for any Empirical Risk Minimizer (ERM) and contain an extra logarithmic factor $\log(1/ε)$ which is known to be necessary for ERM in general. It has been recently shown by Hanneke (2016) that the optimal sample complexity of PAC learning for any VC class C is achieved by a particular improper learning algorithm, which outputs a specific majority-vote of hypotheses in C. This leaves the question of when this bound can be achieved by proper learning algorithms, which are restricted to always output a hypothesis from C. In this paper we aim to characterize the classes for which the optimal sample complexity can be achieved by a proper learning algorithm. We identify that these classes can be characterized by the dual Helly number, which is a combinatorial parameter that arises in discrete geometry and abstract convexity. In particular, under general conditions on C, we show that the dual Helly number is bounded if and only if there is a proper learner that obtains the optimal joint dependence on $ε$ and $δ$. As further implications of our techniques we resolve a long-standing open problem posed by Vapnik and Chervonenkis (1974) on the performance of the Support Vector Machine by proving that the sample complexity of SVM in the realizable case is $Θ((n/ε)+(1/ε)\log(1/δ))$, where $n$ is the dimension. This gives the first optimal PAC bound for Halfspaces achieved by a proper learning algorithm, and moreover is computationally efficient.
Predictive Information Accelerates Learning in RL

The Predictive Information is the mutual information between the past and the future, I(X_past; X_future). We hypothesize that capturing the predictive information is useful in RL, since the ability to model what will happen next is necessary for success on many tasks. To test our hypothesis, we train Soft Actor-Critic (SAC) agents from pixels with an auxiliary task that learns a compressed representation of the predictive information of the RL environment dynamics using a contrastive version of the Conditional Entropy Bottleneck (CEB) objective. We refer to these as Predictive Information SAC (PI-SAC) agents. We show that PI-SAC agents can substantially improve sample efficiency over challenging baselines on tasks from the DM Control suite of continuous control environments. We evaluate PI-SAC agents by comparing against uncompressed PI-SAC agents, other compressed and uncompressed agents, and SAC agents directly trained from pixels. Our implementation is given on GitHub.
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification

Unsupervised domain adaptive person Re-IDentification (ReID) is challenging because of the large domain gap between source and target domains, as well as the lackage of labeled data on the target domain. This paper tackles this challenge through jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification. The local one-hot classification assigns images in a training batch with different person IDs, then adopts a Self-Adaptive Classification (SAC) model to classify them. The global multi-class classification is achieved by predicting labels on the entire unlabeled training set with the Memory-based Temporal-guided Cluster (MTC). MTC predicts multi-class labels by considering both visual similarity and temporal consistency to ensure the quality of label prediction. The two classification models are combined in a unified framework, which effectively leverages the unlabeled data for discriminative feature learning. Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks. For example, under unsupervised setting, our method outperforms recent unsupervised domain adaptive methods, which leverage more labels for training.
Improving Farmers’ Participation in Agri Supply Chains with Blockchain and Smart Contracts

Farmers are inhibited from Agri supply chains due to upfront transaction costs. There is ample evidence that farmers' participation in supply chains produce benefits for farmers. This paper analyses how blockchain might support farmers to reduce transaction costs and improve their involvement in supply chains by reviewing the literature. The study contributes some theoretical insights to our knowledge on how transaction costs can be reduced by implementing supply chain collaboration with blockchain technology. It further presents some design implications for a blockchain-enabled system that would also support collective marketing. Practitioners in the Agri domain can use this analysis as a support for developing a system that improves farmers' participation in Agri supply chains.