Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized remotely guided preventive exercise therapy for a healthy heart (PRIORITY): protocol for an assessor-blinded, multicenter randomized controlled trial

Aims A key treatment for patients with varying stages of heart failure with preserved ejection fraction (HFpEF) is exercise. Yet, despite a Class 1A recommendation, only one-third of patients exercise sufficiently. A huge treatment gap exists between guidelines and clinical practice. PRIORITY aims to establish the feasibility, clinical effectiveness and cost-effectiveness of a hybrid centre and home-based personalized exercise and physical activity intervention for patients along the HFpEF continuum. Methods An assessor-blinded, multicenter randomized controlled trial will be conducted among 312 patients along the HFpEF continuum. Participants will be randomized (1:1) to the PRIORITY intervention or a comparator group receiving only a written exercise prescription. Participants in the PRIORITY group will receive 18 supervised centre-based exercise sessions during one year, supplemented with a remotely guided home-based physical activity program. Outcomes will be assessed at baseline, 4 months, one and two years. The primary outcome is the peak oxygen uptake (pVO2) at 1-year. Secondary outcomes include physical activity, other physical fitness parameters, cardiovascular health, echocardiographic parameters, health-related quality of life and costs at 1-year FU. Machine learning algorithms will analyse big data on physical activity collected during the 1-year intervention to develop models that can predict physical activity uptake and adherence as well as changes in fitness and health. A cost-utility analysis will be performed to evaluate the cost-effectiveness of the PRIORITY intervention compared to the control condition. Discussion We anticipate that participants in the supervised home-based exercise intervention group will have a greater increase in pVO2 compared to those receiving a written exercise prescription. Trial registration number This trial is registered at ClinicalTrials.gov (NCT04745013) and is currently in the recruitment stage.
Abnormal plasma ceramides refine high-risk patients with worsening heart failure

Background Worsening heart failure (WHF) is a heterogeneous clinical syndrome with poor prognosis. More effective risk stratification tools are required to identify high-risk patients. Evidence suggest that aberrant ceramide accumulation can be affected by heart failure risk factors and as a driver of tissue damage. We hypothesized that specific ceramide lengths and ratios serve as biomarkers for risk stratification in WHF patients by reflecting pathological changes of distinct organ dysfunctions. Medthods We measured seven plasma ceramides using liquid chromatography-mass spectrometry (LC-MS) in 1,558 patients, including 1,262 participants in retrospective discovery set and 296 WHF patients in prospective validation set in BIOMS-HF study (Registry Study of Biomarkers in Heart Failure). Univariable and multivariable logistic regression models were constructed to identify associations of ceramides with organ dysfunctions. Results We constructed three ceramide-based scores linked independently to heart, liver, and kidney dysfunction, with ceramides and ratios included in each score specifying systemic inflammation, chronic metabolic disorder, and water-sodium retention. The combined ceramide heart failure score (CHFS) was independently associated with adverse outcomes [Hazard Ratio, 2.80 (95% CI: 1.78–4.40; P < 0.001); 2.68 995% CI: 1.12–6.46; P = 0.028)] and improved the predictive value of Acute Decompensated Heart Failure National Registry score and BNP [net reclassification index, 0.34 (95% confidence interval, CI: 0.19–0.50); 0.42 (95% CI: 0.13–0.70)] in the discovery and validation set, respectively. Lower BNP levels, but higher CHFS had the highest hazard of future adverse events in WHF patients. Conclusion Abnormal plasma ceramides, associated with heart and peripheral organ dysfunctions, provide incremental prognostic information over the ADHERE score and brain natriuretic peptide concentration for risk stratification in WHF patients. This may facilitate the reclassification of high-risk patients in need of aggressive therapeutic interventions.
Stackelberg Security Games with Contagious Attacks on a Network: Reallocation to the Rescue

In the classic network security games, the defender distributes defending resources to the nodes of the network, and the attacker attacks a node, with the objective of maximizing the damage caused. In this paper, we consider the network defending problem against contagious attacks, e.g., the attack at a node u spreads to the neighbors of u and can cause damage at multiple nodes. Existing works that study shared resources assume that the resource allocated to a node can be shared or duplicated between neighboring nodes. However, in the real world, sharing resource naturally leads to a decrease in defending power of the source node, especially when defending against contagious attacks. Therefore, we study the model in which resources allocated to a node can only be transferred to its neighboring nodes, which we refer to as a reallocation process. We show that the problem of computing optimal defending strategy is NP-hard even for some very special cases. For positive results, we give a mixed integer linear program formulation for the problem and a bi-criteria approximation algorithm. Our experimental results demonstrate that the allocation and reallocation strategies our algorithm computes perform well in terms of minimizing the damage due to contagious attacks.
Functional Requirements for Interactive Bias-Audit Tools

Interactive toolkits can be used to audit automated decision-making models for discriminatory biases. In this paper, we present a rubric listing the functional requirements such toolkits need to fulﬁl to make them usable in practice. The rubric was based on a literature review of interview studies with industry practitioners, and other possible auditors.
Inferring Values via Hybrid Intelligence

Values, such as freedom and safety, are the core motivations that guide us humans. A prerequisite for creating value-aligned multiagent systems that involve humans and artificial agents is value inference, the process of identifying values and reasoning about human value preferences. We introduce a framework that connects the value inference steps, and motivate why a hybrid intelligence approach is instrumental for its success. We also highlight the multidisciplinary research challenges that hybrid value inference entails.
An Exceptional Points Removing Based Optimal Algorithm for Estimating Fundamental Matrix

This paper proposes an exceptional points removing based optimal algorithm for estimating the fundamental matrix. Firstly, the exceptional points are eliminated, and the initial value of the fundamental matrix is obtained. Secondly, a criterion of minimizing the epipoalr distance is used to nonlinearly optimize the initial fundamental matrix. It can be seen from the experiment results that this algorithm eliminates the bad effect of exceptional points on the fundamental matrix, thus it is more precise and has an advantage over other conventional algorithms in terms of both average residual error and average epipolar distance.
Medical image registration method based on mixed mutual information

Traditionally, the similarity metric is based on Shannon's entropy. Through the analysis of Renyi's entropy, it is found that Renyi's entropy can remove some unwanted local optimum, smooth out difficult optimization terrain accordingly; Shannon's entropy has the "depth" of the basin of attraction, making the registration function easier to be optimized. So a new similarity measure based on mixed mutual information was proposed. The measures based on different entropy were used in different searching phases, and global optimization algorithm and local one were used individually. At first, the global optimization algorithm was used to find the local extrema of generalized mutual information measure based on Renyi's entropy. Then, the local one was used to locate the global optimal solution by searching the current local optimal ones, and the generalized mutual information measure based on Shannon's entropy was taken as the objective function.
Computationally Efficient Variational Approximations for Bayesian Inverse Problems

The major drawback of the Bayesian approach to model calibration is the computational burden involved in describing the posterior distribution of the unknown model parameters arising from the fact that typical Markov chain Monte Carlo (MCMC) samplers require thousands of forward model evaluations. In this work, we develop a variational Bayesian approach to model calibration which uses an information theoretic criterion to recast the posterior problem as an optimization problem. Specifically, we parameterize the posterior using the family of Gaussian mixtures and seek to minimize the information loss incurred by replacing the true posterior with an approximate one. Our approach is of particular importance in underdetermined problems with expensive forward models in which both the classical approach of minimizing a potentially regularized misfit function and MCMC are not viable options. We test our methodology on two surrogate-free examples and show that it dramatically outperforms MCMC methods.
SoK: Evaluations in Industrial Intrusion Detection Research

Industrial systems are increasingly threatened by cyberattacks with potentially disastrous consequences. To counter such attacks, industrial intrusion detection systems strive to timely uncover even the most sophisticated breaches. Due to its criticality for society, this fast-growing field attracts researchers from diverse backgrounds, resulting in 130 new detection approaches in 2021 alone. This huge momentum facilitates the exploration of diverse promising paths but likewise risks fragmenting the research landscape and burying promising progress. Consequently, it needs sound and comprehensible evaluations to mitigate this risk and catalyze efforts into sustainable scientific progress with real-world applicability. In this paper, we therefore systematically analyze the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. Our analysis of 609 publications shows that the rapid growth of this research field has positive and negative consequences. While we observe an increased use of public datasets, publications still only evaluate 1.3 datasets on average, and frequently used benchmarking metrics are ambiguous. At the same time, the adoption of newly developed benchmarking metrics sees little advancement. Finally, our systematic analysis enables us to provide actionable recommendations for all actors involved and thus bring the entire research field forward.
minBERT for Sentiment Analysis, Paraphrase Detection, and Semantic Textual Similarity

In this project, we focus on improving the performance and generalization of natural language processing (NLP) tasks such as sentiment analysis (SST), paraphrase detection, and semantic textual similarity (STS) by implementing a multitask-based model. We proposed using a BERT-based architecture called minBERT adapted for multitask learning and experimented with various additional model architectures, pre-training, and fine-tuning techniques to improve the performance of the baseline model. We found that using Cosine similarity (Cos) and increasing the model size through hidden MLP layers and ReLU activation (ReLU) were particularly effective for the STS and Paraphrase tasks, respectively. We also used GradNorm and PCGrad to balance gradients across diverse tasks and address conflicting gradients between tasks in multitask learning. Additionally, we employed data engineering techniques (such as looping over smaller datasets (Loop) and normalizing labels) and used ensembling to determine the most optimal model architecture. The best overall model incorporated the Cos, ReLU, and Loop extensions, improving the baseline average score by 0.13 on the Dev set and significantly improving the task-wise scores for Paraphrase and STS by 0.22 and 0.17, respectively. Our results demonstrated that a BERT-based multi-task learning architecture, combined with various pre-training, fine-tuning, and ensembling techniques, can significantly improve the performance of NLP tasks such as our three downstream tasks.
Glaucoma Surgery Outcome Prediction Using Progress Notes: A Comparative Study

We aimed to predict glaucoma surgery outcomes of patients who underwent a surgery at Stanford using pre-operative data. This is a critical problem in the field of ophthalmology as glaucoma is one of the leading cause of blindness worldwide. We developed various BERT-based multi-modal fusion models (sum, learnable sum, multiply, concatenate, SVD, multi-layer concatenation) leveraging both EHR structured data and progress notes. We used these architectures to develop a model that combines BERT’s output into a recurrent neural networks (RNN) and implemented time embeddings (time2Vec) to exploit the temporal nature of the relationship between surgery outcome prediction and physician notes. We benchmarked the different methods and highlighted that pre-training greatly increases performance while the best-performing model is the late concatenation fusion model. Our contribution showed how we could extract some predictive power from these notes but also how current SOTA methods struggle with tasks humans also have a hard time with, such as this one.
Aesthetics as a Criterion: Navigating Solution Spaces Utilizing Computer Vision, the Aesthetic Measure, and Artificial Neural Networks

A computational framework is proposed to quantify the aesthetic experience of perceiving architecture. This framework is used as the criterion for navigating a design solution space. A parametric model’s solution space is represented as a set of computer-generated images. We utilize computer vision to recognize parts and the relations between parts and the whole. These values are inserted into an adaptation of Birkhoff’s aesthetic measure formula, calibrated via crowdsourced hedonic response. An artificial neural network (ANN) model is trained to predict the hedonic response of images using (A) parts relations as inputs and (B) the average hedonic responses as target outputs. The prediction of the ANN is used as a fitness function for optimization. The same public evaluates the parametric model’s output to compare the ANN model’s effectiveness in predicting their response. The method is useful in translating formal qualities into quantities and navigating solution spaces, especially with surrogate models.
3D Partial Shape Retrieval Based on Local Bag-of-Words Models

The authors propose a new 3D shape representation,"local bag-of-words models" and use overlapped "local bag-of-words models" which improves the robustness of the algorithm by avoiding shape segmentation.The detailed steps are also proposed to apply the "Earth Mover's distance" and "pyramid match kernels" algorithms to quantify partial shape similarity.It proves that the proposed distance measure mitigates the distance under-estimation problem of global bag-of-words model.Finally,the experiments firmly demonstrate the effectiveness of the proposed approach to 3D partial shape retrieval.
Privacy-Informed Consensus-Based Secondary Control in Cyber-Physical DC Microgrids

This letter proposes a privacy-informed consensus-based distributed secondary control strategy for cyber-physical dc microgrids. The proposed secondary control approach relies on output masks that transform the physical states of distributed generation (DG) units to some auxiliary states by adding local perturbation signals, whose functional form and parameters are local and chosen independently by each DG unit. The proposed privacy-preserving secondary control scheme ensures voltage balancing and proportional current sharing in dc microgrids without disclosing the physical states of distributed generation units to their neighbors. Simulation case studies confirm the theoretical results of this letter.
Tomato Leaf Disease Classification using Convolutional Neural Network Model

Tomatoes and potatoes are the most commonly grown crops worldwide. The tomato contains antioxidant properties to prevent the cells from radicals. It can also protect the human body from cancer and sunburn. Tomatoes come under the human essential diet. But there are various diseases that are harmful to the plant's health and have an impact on its growth. Accurate and rapid disease identification of tomato plants is critical to increasing their long-term agricultural productivity. Disease identification can be done on the basis of physical changes in the leaves of the tomato plant. Detection of plant leaf diseases at early stages is beneficial to the Indian economy. In this work, tomato leaf diseases have been detected and classified using a deep learning model called Convolutional Neural Network. For performing the implementation, the Kaggle-based tomato leaf dataset has been used. This dataset contains nine different kinds of leaf diseases and a healthy leaf class. The proposed CNN model has been implemented with Adam and SGD optimizer. The results have shown that SGD performs best when compared with Adam. The accuracy and loss metrics have been used for predicting the performance outcome of the classification model. The results show that the CNN model achieves an accuracy (0.9966) and loss value(0.0044) when implemented with SGD optimizer.
Smart Meters:Cyber Security Issues and Their Solutions

Smart meter, the advanced energy meters [1] is one of the most important components of Smart grid (regarded as the future generation of today's power infrastructure distribution). If the power company begins for the smart grid implementation, every house and premises need to be installed with smart meters. All information such as consumers' private information and consumers' electricity transactions stored in a database. So, Attacks on Data in smart meters is considered our main concern. These databases may be got into with or fall into the hands of blackmailers, criminals, cyber hackers, power company workers, and other anonymous groups who may perform spiteful actions, which have no limits. Several attacks on data and existing solutions to such attacks are discussed in this paper. In our proposed model, algorithms like RSA, Blowfish, AES, DES, 3DES are used. Two cryptographic algorithms are proposed- Hybrid RSA-Blowfish Algorithm and Eigenvalue Encryption Algorithm. In the Hybrid RSA - Blowfish Algorithm, the plain text is taken and encrypted into an intermediate cipher using the Blowfish algorithm. Then this intermediate cipher is again enciphered using the RSA algorithm which now creates a more protected cipher producing a public and a private key. In the Eigenvalue Encryption Algorithm, the Eigenvalue of the matrix taken is calculated and the positive eigenvalue becomes the key. This key is then multiplied with subsequent reversed ASCII values of the plain text and a numerical series and forms the ciphertext. In this paper, the output of both the algorithms along with the decryption process is shown. Later on, our proposed algorithms with the existing algorithms are compared on the basis of four parameters- Avalanche Effect, Encryption Time, Decryption Time and Memory Used. A comparative study based on the Statistical Data is done and graphically shown. The attacks prevented by our proposed algorithms are also discussed in this paper.
Application Of An Analog-to-Information Converter In Multichannel Radio Systems

The development of multichannel radio systems leads to a significant increase in data flows that must be transferred from analog-to-digital converters to a computing device. The maximum sampling rate of today's ADCs on the market limits the maximum bandwidth of the signals that can be sampled by the receivers of multichannel radio systems, which does not allow such systems to achieve good resolution. Also, the number of samples that can be obtained at very high sampling rates raises a serious memory capacity issue and increases the processing power required for the digital processing units. One of the approaches that allows solving this problem is the use of analog-to-information converters, which allow performing the compression operation in the process of signal digitization and overcoming the requirement of the Nyquist Rate. In this article, the existing methods for constructing analog-to-information converters are considered, modeling of known algorithms is carried out, and an approach is proposed for creating an analog-information converter in multichannel radio engineering systems.
Integrating Information in Visual and Thermal Image for Crack and Moisture Detection in Concrete Structures through CNNs

The life assessment of buildings is crucial for the stability of the structure and the safety of its inhabitants. Due to environmental factors and ageing, structural deterioration takes place, resulting in fractures and seepage. Therefore, there is a need for inspection tools, preferably non-invasive in nature. Recently the inexpensive cost of the necessary experimental equipment and the general availability of image processing tools have led to its extensive use for damage identification. In this research, an efficient parallel deep-learning model is presented in order to identify cracks in buildings and categorize moisture present in each image. Information from both optical and thermal images has been incorporated into the model. As a first stage of processing, the morphological operation was performed on the thermal images for easier classification of moisture. For moisture classification, the model was able to get an accuracy of 83.33%. The R-CNN network, which was utilized for the detection of cracks, possessed a greater average precision value of 72.27%, along with an intersection over union (IoU) value of 0.5.
A study of CNN models for re-identification of vehicles

Vehicle Re-identification has evolved in recent times. Initially, clicking a single picture of a vehicle or a car was done manually, inviting the workforce to complete a specified task. With the growth in technology, the method and techniques in Vehicle Re-Id also have advanced, transforming from manual to automation. Surveillance cameras were used to capture vehicle images and retrieve information about a specific vehicle. Re-trieving and identifying the images of the vehicle is done using computer vision, the most important branch of computer science and artificial intelligence. Earlier, Vehicle Re-Id implemented a single algorithm on a dataset, making the corresponding result insufficient to determine its effects. This paper proposes a brief survey of multi-modal techniques and methods for vehicle re-identification and fingerprinting. The different attributes of the vehicle are considered for ANPR (Automatic number plate recognition) for identifying the number plate, focusing on the vehicle's details or features as the initial phase of identification, and then the vehicle number plate.