Reviewerly/exHarmony huggingface dataset:
Prediction of cooling moisture content after cut tobacco drying process based on a particle swarm optimization-extreme learning machine algorithm

&lt;abstract&gt; &lt;p&gt;The stability of the moisture content of the cigarette is an important index to evaluate the quality of the cigarette. The cooling moisture content after cut tobacco drying process is a key factor affecting the stability of the moisture content of the cigarette. In order to realize its accurate prediction and ensure the stability, in Honghe cigarette factory, a cooling moisture content prediction model is built based on a particle swarm optimization-extreme learning machine (PSO-ELM) algorithm via the historical production data. Besides, the proposed PSO-ELM algorithm is also compared with multiple linear regression (MLR), support vector machine (SVM) and the traditional extreme learning machine (ELM) algorithms in the same data set on the prediction. The prediction accuracy of PSO-ELM method is the highest and the average error of the prediction standard is the lowest. The results indicated the proposed method can achieve a better prediction performance over compared methods and it provides a new method to realize the prediction of the cooling moisture content after cut tobacco drying process.&lt;/p&gt; &lt;/abstract&gt;
NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM – ADVANTAGES AND DISADVANTAGES

The purpose of the publication is to analyse popular classification algorithms in machine learning. The following classifiers were studied: Naive Bayes Classifier, Decision Tree and AdaBoost Ensemble Algorithm. Their advantages and disadvantages are discussed. Research shows that there is no comprehensive universal method or algorithm for classification in machine learning. Each method or algorithm works well depending on the specifics of the task and the data used.
Increasing the Level of Professional Competence Using a Virtual Educational Environment

The article addresses the issues of creating a virtual educational environment using special equipment. The 3DVista Virtual Tour program was chosen as the development environment for the virtual tour, using features related to e-Learning and automatic integration with LMS Moodle. The virtual space of the created educational environment provides an opportunity for the existence of various forms of educational communication, based on the capabilities of modern teaching tools and modern information and communication technologies, allows you to provide conditions for improving the quality of education, exchanging of views, and mutual consultation. The use of web-quest technology will allow young people to shape their common understanding of the profession, realize the demands placed on professionals in this sphere, will contribute to identify their personal and professional interests and aptitudes, and will also help schoolchildren and students to make independently and consciously their professional choice This technology fosters the development of communication skills and a more intensive exchange of information in the process of learning or mastering new academic material. All this ensures an increase in the level of professional competence of schoolchildren and students.
On the Limit of Artificial Intelligence

This article asks how can we articulate the limit of artificial intelligence, which virtually has no limit? Or maybe the definition of AI already implies its limit, how Marvin Minsky once declared that there is no generally accepted theory of intelligence, and that AI is only one particular way of modelling it. This article revisits the debate between Minsky and Hubert Dreyfus and repositions them in terms of an opposition between mechanism and organism, in order to expose the limit of Dreyfus’s Heideggerian critique. It suggests reflecting on the relation between noodiversity and technodiversity to methodologically broaden the concept of intelligence, and on how different concepts of intelligence could be thought by introducing the Chinese philosopher Mou Zongsan’s interpretation of Kant’s intellectual intuition.
Explainable AI for medical imaging: explaining pneumothorax diagnoses with Bayesian teaching

Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential for great gains in radiologist productivity and, by extension, public health. However, these gains are contingent on building and maintaining experts’ trust in the AI agents. Explainable AI may build such trust by helping medical experts to understand the AI decision processes behind diagnostic judgements. Here we introduce and evaluate explanations based on Bayesian Teaching, a formal account of explanation rooted in the cognitive science of human learning. We find that medical experts exposed to explanations generated by Bayesian Teaching successfully predict the AI’s diagnostic decisions and are more likely to certify the AI for cases when the AI is correct than when it is wrong, indicating appropriate trust. These results show that Explainable AI can be used to support human-AI collaboration in medical imaging.
Towards a Trustworthy Semantics-Based Language Framework via Proof Generation

Abstract We pursue the vision of an ideal language framework , where programming language designers only need to define the formal syntax and semantics of their languages, and all language tools are automatically generated by the framework. Due to the complexity of such a language framework, it is a big challenge to ensure its trustworthiness and to establish the correctness of the autogenerated language tools. In this paper, we propose an innovative approach based on proof generation . The key idea is to generate proof objects as correctness certificates for each individual task that the language tools conduct, on a case-by-case basis, and use a trustworthy proof checker to check the proof objects. This way, we avoid formally verifying the entire framework, which is practically impossible, and thus can make the language framework both practical and trustworthy . As a first step, we formalize program execution as mathematical proofs and generate their complete proof objects. The experimental result shows that the performance of our proof object generation and proof checking is very promising.
Discriminative attention-augmented feature learning for facial expression recognition in the wild

Facial expression recognition (FER) in-the-wild is challenging due to unconstraint settings such as varying head poses, illumination, and occlusions. In addition, the performance of a FER system significantly degrades due to large intra-class variation and inter-class similarity of facial expressions in real-world scenarios. To mitigate these problems, we propose a novel approach, Discriminative Attention-augmented Feature Learning Convolution Neural Network (DAF-CNN), which learns discriminative expression-related representations for FER. Firstly, we develop a 3D attention mechanism for feature refinement which selectively focuses on attentive channel entries and salient spatial regions of a convolution neural network feature map. Moreover, a deep metric loss termed Triplet-Center (TC) loss is incorporated to further enhance the discriminative power of the deeply-learned features with an expression-similarity constraint. It simultaneously minimizes intra-class distance and maximizes inter-class distance to learn both compact and separate features. Extensive experiments have been conducted on two representative facial expression datasets (FER-2013 and SFEW 2.0) to demonstrate that DAF-CNN effectively captures discriminative feature representations and achieves competitive or even superior FER performance compared to state-of-the-art FER methods.
Animatable Neural Radiance Fields for Human Body Modeling.

This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a dynamic scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. Instead, we introduce neural blend weight fields to produce the deformation fields. Based on the skeleton-driven deformation, blend weight fields are used with 3D human skeletons to generate observation-to-canonical and canonical-to-observation correspondences. Since 3D human skeletons are more observable, they can regularize the learning of deformation fields. Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. Experiments show that our approach significantly outperforms recent human synthesis methods. The code will be available at this https URL.
Efficient Traffic Accident Warning Based on Unsupervised Prediction Framework

Recognizing potentially hazardous objects is crucial in the field of transportation, especially in assisted and unmanned driving. However, most existing studies do not focus on defensive driving as they only identify accidents ahead of the vehicle in which they are not involved. In this paper, a driving assistance system is proposed to predict the risk score of potential targets ahead of the vehicle and provide an early warning, which relies on a deep architecture called Fusion-Residual Predictive Network (FRPN) that fused multi-scale residual features and improved adversarial learning. This architecture provides an environment for the generator to perform joint learning from ground-truth images and discriminators with gradient penalty constraints. The deeper convolutional neural network can greatly improve the quality of the image by fusing residual features. Several deep convolutional neural network models were used to evaluate the method on various datasets; among them, the prediction model based on the VGG network, with peak signal-to-noise ratio of 32.67 and structural similarity index of 0.921, respectively, yielded the best results. Subsequently, we utilize the tracking model to design a risk score evaluation method based on the location of the target and it have an improvement in ability to give early warning with 1.95s earlier in the best case. These results prove that our method can effectively reduce the risk of traffic accidents.
A Study on the Performance of Unconstrained Very Low Resolution Face Recognition: Analyzing Current Trends and New Research Directions

In the past decade, research in the face recognition area has advanced tremendously, particularly in uncontrolled scenarios (face recognition in the wild). This advancement has been achieved partly due to the massive popularity and effectiveness of deep convolutional neural networks and the availability of larger unconstrained datasets. However, several face recognition challenges remain in the context of very low resolution homogeneous (same domain) and heterogeneous (different domain) face recognition. In this survey, we study the seminal and novel methods to tackle the very low resolution face recognition problem and provide an in-depth analysis of their design, effectiveness, and efficiency for a real-time surveillance application. Furthermore, we analyze the advantage of employing deep learning convolutional neural networks, while presenting future research directions for effective deep learning network design in this context.
DeepGTT: A general trajectory tracking deep learning algorithm based on dynamic law learning

Deep learning technology provides novel solutions for increasingly complex target tracking requirements. For traditional target tracking models, the movement of the target need to be simulated by a predefined mathematical model. However, it is extremely difficult to obtain sufficient information in advance, which makes it challenging to track changeable and noisy trajectories in a timely and precise manner. A deep learning framework is constructed for automatic trajectory tracking based on learning the dynamic laws of motion, called DeepGTT. Specifically, a trajectory generator and a trajectory mapper were designed to standardise trajectory data and construct trajectory mapping, which enable the long short-term memory–based tracking network to learn general dynamic laws. Then, to discuss the interpretability of the model, the mechanism of the deep learning framework is considered and a memory factor matrix is defined. Finally, extensive experiments are conducted on various weak manoeuvring and manoeuvring scenarios to evaluate the algorithm. Experimental results demonstrate that the DeepGTT algorithm remarkably improves accuracy and efficiency compared with most conventional algorithms and state-of-the-art methods. In addition, interpretability experiments qualitatively prove that the tracking network can perceive dynamic laws when estimating the target state.
A New Approach to Overgenerating and Scoring Abstractive Summaries

We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' needs.Abstractive summarizers trained on single reference summaries may struggle to produce outputs that achieve multiple desirable properties, i.e., capturing the most important information, being faithful to the original, grammatical and fluent.In this paper, we propose a two-staged strategy to generate a diverse set of candidate summaries from the source text in stage one, then score and select admissible ones in stage two.Importantly, our generator gives a precise control over the length of the summary, which is especially well-suited when space is limited.Our selectors are designed to predict the optimal summary length and put special emphasis on faithfulness to the original text.Both stages can be effectively trained, optimized and evaluated.Our experiments on benchmark summarization datasets suggest that this paradigm can achieve state-of-the-art performance.
Deconstructing the SolarWinds breach

The now infamous SolarWinds breach is still causing ramifications for the businesses involved. It won't be long until we see another attack of this kind, because most organisations currently don't have the ability to quickly find and respond to attackers that have infiltrated their networks. Lavi Lazarovitz of CyberArk Labs dissects the SolarWinds case, examining the major steps used by the attackers. And he makes recommendations for how organisations can avoid falling victim to this form of attack in the future. In 2021 we’ve already seen a number of far-reaching cyber attacks. The now infamous SolarWinds breach is still causing ramifications for the businesses involved, and the recent hack on Microsoft exchange servers has reportedly put more than 3,000 UK email servers at risk. 1
Diagnose Like A Pathologist: Weakly-Supervised Pathologist-Tree Network for Slide-Level Immunohistochemical Scoring

The immunohistochemistry (IHC) test of biopsy tissue is crucial to develop targeted treatment and evaluate prognosis for cancer patients. The IHC staining slide is usually digitized into the whole-slide image (WSI) with gigapixels for quantitative image analysis. To perform a whole image prediction (e.g., IHC scoring, survival prediction, and cancer grading) from this kind of high-dimensional image, algorithms are often developed based on multi-instance learning (MIL) framework. However, the multi-scale information of WSI and the associations among instances are not well explored in existing MIL based studies. Inspired by the fact that pathologists jointly analyze visual fields at multiple powers of objective for diagnostic predictions, we propose a Pathologist-Tree Network (PTree-Net) to sparsely model the WSI efficiently in multi-scale manner. Specifically, we propose a Focal-Aware Module (FAM) that can approximately estimate diagnosis-related regions with an extractor trained using the thumbnail of WSI. With the initial diagnosis-related regions, we hierarchically model the multi-scale patches in a tree structure, where both the global and local information can be captured. To explore this tree structure in an end-to-end network, we propose a patch Relevance-enhanced Graph Convolutional Network (RGCN) to explicitly model the correlations of adjacent parent-child nodes, accompanied by patch relevance to exploit the implicit contextual information among distant nodes. In addition, tree-based self-supervision is devised to improve representation learning and suppress irrelevant instances adaptively. Extensive experiments are performed on a large-scale IHC HER2 dataset. The ablation study confirms the effectiveness of our design, and our approach outperforms state-of-the-art by a large margin.
Learning Compact Spatio-Temporal Features for Fast Content based Video Retrieval

Videos are recorded and uploaded daily to the sites like YouTube, Facebook etc. from devices such as mobile phones and digital cameras with less or without metadata (semantic tags) associated with it. This makes extremely difficult to retrieve similar videos based on this metadata without using content based semantic search. Content based video retrieval is problem of retrieving most similar videos to a given query video and has wide range of applications such as video browsing, content filtering, video indexing, etc. Traditional video level features based on key frame level hand engineered features which does not exploit rich dynamics present in the video. In this paper we propose a fast content based video retrieval framework using compact spatio-temporal features learned by deep learning. Specifically, deep CNN along with LSTM is deploy to learn spatio-temporal representations of video. For fast retrieval, binary code is generated by hashing learning component in the framework. For fast and effective learning of hash code proposed framework is trained in two stages. First stage learns the video dynamics and in second stage compact code is learn using learned video’s temporal variation from the first stage. UCF101 dataset is used to test the proposed method and results compared by other hashing methods. Results show that our approach is able to improve the performance over existing methods.
The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits

Scores based on Shapley values are widely used for providing explanations to classification results over machine learning models. A prime example of this is the influential SHAP-score, a version of the Shapley value that can help explain the result of a learned model on a specific entity by assigning a score to every feature. While in general computing Shapley values is a computationally intractable problem, it has recently been claimed that the SHAP-score can be computed in polynomial time over the class of decision trees. In this paper, we provide a proof of a stronger result over Boolean models: the SHAP-score can be computed in polynomial time over deterministic and decomposable Boolean circuits. Such circuits, also known as tractable Boolean circuits, generalize a wide range of Boolean circuits and binary decision diagrams classes, including binary decision trees, Ordered Binary Decision Diagrams (OBDDs) and Free Binary Decision Diagrams (FBDDs). We also establish the computational limits of the notion of SHAP-score by observing that, under a mild condition, computing it over a class of Boolean models is always polynomially as hard as the model counting problem for that class. This implies that both determinism and decomposability are essential properties for the circuits that we consider, as removing one or the other renders the problem of computing the SHAP-score intractable (namely, #P-hard).
Constraint Relation Between Steerability and Concurrence for Two‐Qubit States

Abstract Entanglement and steering are used to describe quantum correlation. And steerable states form a strict subset of entangled states. A natural question arises concerning how much territory steerability occupies entanglement for a general two‐qubit entangled state. In this work, the constraint relation between steerability and concurrence is investigated by using two kinds of evolutionary states and any two‐qubit states (or lots of randomly generated states). By combining the theoretical and numerical proofs, the upper and lower boundaries of steerability are obtained. And the lower boundary can be used as a sufficient criterion for steering detection. Furthermore, a special kind of mixed state transformed by performing an arbitrary unitary transformation on Werner‐like state is considered, and a sufficient steering criterion described by concurrence and purity is proposed.
Comprehensive Analysis of Augmented Reality Technology in Modern Healthcare System

The recent advances of Augmented Reality (AR) in healthcare have shown that technology is a significant part of the current healthcare system.In recent days, augmented reality has proposed numerous intelligent applications in the healthcare domain including, wearable access, telemedicine, remote surgery, diagnosis of medical reports, emergency medicine, etc.These developed augmented healthcare applications aim to improve patient care, increase efficiency, and decrease costs.Therefore, to identify the advances of AR-based healthcare applications, this article puts on an effort to perform an analysis of 45 peerreviewed journal and conference articles from scholarly databases between 2011 and 2020.It also addresses concurrent concerns and their relevant future challenges including, user satisfaction, convenient prototypes, service availability, maintenance cost, etc.Despite the development of several AR healthcare applications, there are some untapped potentials regarding secure data transmission, which is an important factor for advancing this cuttingedge technology.Therefore, this paper also analyzes distinct AR security and privacy including, security requirements (i.e., scalability, confidentiality, integrity, resiliency, etc.) and attack terminologies (i.e.sniffing, fabrication, modification, interception, etc.).Based on the security issues, in this paper, we propose an artificial intelligence-based dynamic solution to build an intelligent security model to minimize data security risks.This intelligent model can identify seen and unseen threats in the threat detection layer and thus can protect data during data transmission.In addition, it prevents external attacks in the threat elimination layer using threat reduction mechanisms.
DeepfakeNet, an Efficient Deepfake Detection Method

Different CNNs models do not perform well in deepfake detection in cross datasets.This paper proposes a deepfake detection model called DeepfakeNet, which consists of 20 network layers.It refers to the stacking idea of ResNet and the split-transform-merge idea of Inception to design the network block structure, That is, the block structure of ResNeXt.The study uses some data of FaceForensics++, Kaggle and TIMIT datasets, and data enhancement technology is used to expand the datasets for training and testing models.The experimental results show that, compared with the current mainstream models including VGG19, ResNet101, ResNeXt50, XceptionNet and GoogleNet, in the same dataset and preset parameters, the proposed detection model not only has higher accuracy and lower error rate in cross dataset detection, but also has a significant improvement in performance.