Reviewerly/exHarmony huggingface dataset:
Automatic Recognition of Student Emotions Based on Deep Neural Network and Its Application in Depression Detection

The mental health rating scales combined with psychological expert consultation are easily influenced by subjective factors from psychological experts and lack objectivity and scientificalness. Due to the serious influence of depression tendency on learning and living of college students, a novel DNN network model framework based on context emotion information is designed to achieve the automatic auxiliary emotion classification. More corpus training sample can be received by heightening the sample length without reducing samples' number. Firstly, the existing sample feature is input into the recognition model to encode depression related features, and then the MADN features of the samples in the two adjacent segments are input into the above trained model in order for fine-tuning and optimization. Compared with the existing optimal method, the proposed model improves the recognition accuracy in the diagnosis of depression. From the analysis of the experimental results, it is known that deep learning network can monitor the emotional state of college students with high precision, which can accurately identify the patients with depression. The deep learning model can take effective measures to prevent the depression of college students, and discover the depression of college students, alleviate and treat the depression of college students, reduce the depression rate of college students.
Image feature learning with a genetic programming autoencoder

Learning features from raw data is an important topic in machine learning. This paper presents a novel GP approach to learn high-level features from 2D images. It is a generative approach that resembles the concept of an autoencoder. Our approach executes multiple GP runs, each run generates a (partial) model that focuses on a particular high-level feature of the training images. Then, it combines the models generated by each run into a parametric function that reconstructs the observed images. We evaluated our approach on the popular MNIST dataset of 2D images representing handwritten digits. Our evaluation results show that our parametric approach can precisely reconstruct the MNIST hand-written digits.
Winnowing with Gradient Descent

The performance of multiplicative updates is typically logarithmic in the number of features when the targets are sparse. Strikingly, we show that the same property can also be achieved with gradient descent updates. We obtain this result by rewriting the non-negative weights $w_i$ of multiplicative updates by $u_i^2$ and then performing a gradient descent step w.r.t. the new $u_i$ parameters. We apply this method to the Winnow update, the Hedge update, and the unnormalized and normalized exponentiated gradient (EG) updates for linear regression. When the original weights $w_i$ are scaled to sum to one (as done for Hedge and normalized EG), then in the corresponding reparameterized update, the $u_i$ parameters are now divided by $\Vert\mathbf{u}\Vert_2$ after the gradient descent step. We show that these reparameterizations closely track the original multiplicative updates by proving in each case the same online regret bounds (albeit in some cases, with slightly different constants).\n\nAs a side, our work exhibits a simple two-layer linear neural network that, when trained with gradient descent, can experimentally solve a certain sparse linear problem (known as the Hadamard problem) with exponentially fewer examples than any kernel method.
EFFECTIVE DIGITAL TRANSFORMATION IN THE CONTEXT OF HIGHER EDUCATION

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 1027-1036ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0354Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Accurate Computation of Fractional-Order Exponential Moments

Exponential moments (EMs) are important radial orthogonal moments, which have good image description ability and have less information redundancy compared with other orthogonal moments. Therefore, it has been used in various fields of image processing in recent years. However, EMs can only take integer order, which limits their reconstruction and antinoising attack performances. The promotion of fractional-order exponential moments (FrEMs) effectively alleviates the numerical instability problem of EMs; however, the numerical integration errors generated by the traditional calculation methods of FrEMs still affect the accuracy of FrEMs. Therefore, the Gaussian numerical integration (GNI) is used in this paper to propose an accurate calculation method of FrEMs, which effectively alleviates the numerical integration error. Extensive experiments are carried out in this paper to prove that the GNI method can significantly improve the performance of FrEMs in many aspects.
Set the Configuration for the Heart of the OS

This paper presents a study on the practicality of operating system (OS) kernel debloating---reducing kernel code that is not needed by the target applications---in real-world systems. Despite their significant benefits regarding security (attack surface reduction) and performance (fast boot times and reduced memory footprints), the state-of-the-art OS kernel debloating techniques are seldom adopted in practice, especially in production systems. We identify the limitations of existing kernel debloating techniques that hinder their practical adoption, including both accidental and essential limitations. To understand these limitations, we build an advanced debloating framework named \tool which enables us to conduct a number of experiments on different types of OS kernels (including Linux and the L4 microkernel) with a wide variety of applications (including HTTPD, Memcached, MySQL, NGINX, PHP and Redis). Our experimental results reveal the challenges and opportunities towards making kernel debloating techniques practical for real-world systems. The main goal of this paper is to share these insights and our experiences to shed light on addressing the limitations of kernel debloating in future research and development efforts.
An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model

The microseismic signals in the coal minefield are very complex because of its special environment with a large number of blast vibration signals, and how to effectively identify the microseismic signals is still a big problem. S transform (ST) and Manifold Learning (ML) methods are introduced to extract the characteristics of the microseismic signals, and Gaussian Mixture Model based on the improved Bee Colony optimization algorithm (IBC-GMM) is established to identify the microseismic signals accurately. Firstly, the time-frequency characteristics of microseismic signals in coal mine are extracted by ST analysis. It is found that there are obvious time-frequency differences between rock-fracturing signals and blast vibration signals. Blast vibration signals have short duration, high frequency, and complex frequency spectrum, and their dominant frequencies are mainly over 100 Hz. However, rock-fracturing signals are relatively slow, with low frequency and stable spectrum change, and their dominant frequencies are generally below 100 Hz. Then, combining with the microseismic data of Xiashinjie coal mine in Tongchuan, China, the feature dimension reduction is carried out by Manifold Learning (ML) method, and the processed feature vectors are automatically recognized by IBC-GMM. Field test results show that the method summarizes the characteristics of the microseismic wave which are difficult to emerge as the learning vector, and the features reflect the key features of microseismic signals well. The identification accuracy is as high as 94%, and its recognition effect is superior to other recognition models (such as traditional Gaussian Mixture Model based on Expectation-Maximum (EM-GMM), Backpropagation (BP) neural network, Random Forests (RF), Bayes (Bayes) methods, and Logistic Regression (LR) method). Therefore, IBC-GMM could be used to mine engineering microseismic monitoring waveform recognition to provide the reference.
Image restoration method based on GAN and multi-scale feature fusion

In the research of existing image restore algorithms, defective images and target images exist in pairs, which leads to poor applicability of the algorithm. Aiming at the shortcomings of the existing algorithms, such as low accuracy and poor visual consistency, an image restoration method based on GAN and multi-scale feature fusion was proposed. The generator of this algorithm uses a standard encoder-decoder structure. The encoder based on the VGG-16 full convolutional neural network is used to extract the features of the defect image. Low-dimensional features and high Dimensional features are fused to enrich the input of deep convolutions. Aiming at the problems of large error oscillation amplitude, gradient disappearance or gradient explosion in GAN training, the idea of WGAN is used, and the EM distance is used to simulate the sample data distribution. The similarity between the output restored image and the target image is enhanced by introducing L1 loss. This paper performs experiments on the same face data set. The experimental results show that the algorithm improves the accuracy of the restored image and can generate more realistic restored images.
Introduction to cinematic scientific visualization

The Advanced Visualization Lab (AVL) is part of the the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. The AVL is led by Professor Donna Cox, who coined the term "Renaissance Team", with the belief that bringing together specialists of diverse backgrounds creates a team that is greater than the sum of its parts, and members of the AVL team reflect that in our interdisciplinarity. We specialize in creating high-quality cinematic scientific visualizations of supercomputer simulations for public outreach.
On Transversality of Bent Hyperplane Arrangements and the Topological Expressiveness of ReLU Neural Networks

Let $F: \mathbb{R}^n \rightarrow \mathbb{R}$ be a feedforward ReLU neural network. It is well-known that for any choice of parameters, $F$ is continuous and piecewise affine-linear. We lay some foundations for a systematic investigation of how the architecture of $F$ impacts the geometry and topology of its possible decision regions, $F^{-1}((-\infty, t))$ and $F^{-1}((t, \infty))$, for binary classification tasks. Following the classical progression for smooth functions in differential topology, we first define the notion of a generic, transversal ReLU neural network and show that almost all ReLU networks are generic and transversal. We then define a partially oriented linear 1-complex in the domain of $F$ and identify properties of this complex that yield an obstruction to the existence of bounded connected components of a decision region. We use this obstruction to prove that a decision region of a generic, transversal ReLU network $F: \mathbb{R}^n \rightarrow \mathbb{R}$ with a single hidden layer of dimension $n+1$ can have no more than one bounded connected component.
Measuring Triplet Trustworthiness in Knowledge Graphs via Expanded Relation Detection

Nowadays, large scale knowledge graphs are usually constructed by (semi-)automatic information extraction methods. Nevertheless, the technology is not perfect, because it cannot avoid introducing erroneous triplets into knowledge graphs. As a result, it is necessary to carry out some screening of the trustworthiness of the triplets in knowledge graphs before putting them into industrial use. In this paper, we propose a novel framework named as KGerd, for measuring triplet trustworthiness via expanded relation detection. Given a triplet (h, r, t), we center our framework on the basis of the classic translation-based mechanism among h, r and t. Besides translation-based relation detection, we introduce two additional types of relation detection approaches, which consider, respectively, to expand the task vertically by leveraging abstract versions of the relation r, as well as laterally by generating connecting paths between the entities h and t. The three detection results are then combined to provide a trustworthiness score for decision making. Comprehensive experiments on real-life datasets demonstrate that our proposed model KGerd is able to offer better performance over its state-of-the-art competitors.
Deep State-Space Generative Model For Correlated Time-to-Event Predictions

Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events (e.g., kidney failure, mortality) by explicitly modeling the temporal dynamics of patients' latent states. Based on these learned patient states, we further develop a new general discrete-time formulation of the hazard rate function to estimate the survival distribution of patients with significantly improved accuracy. Extensive evaluations over real EMR data show that our proposed model compares favorably to various state-of-the-art baselines. Furthermore, our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
Assurance for CyberPhysical Systems: Addressing Supply Chain Challenges to Trustworthy Software-Enabled Things

Software is playing a pivotal role in most enterprises, whether they realize it or not, and with the proliferation of Industrial Internet of Things (IoT) and other CyberPhysical systems across our society and critical infrastructure and our collective love affair with automation, optimization, and “smart” devices, the role of these types of systems is only going to increase. This talk addresses the myriad of issues that underlie unsafe, insecure, and unreliable software and provides the insights of the Industrial Internet Consortium and other government and industry efforts on how to conquer them and pave the way to a marketplace of trustworthy software-enabled connected things. As the experience of several sectors has shown, the dependence on connected software needs to be met with a strong understanding of the risks to the overall trustworthiness of our software-based capabilities that we, our enterprises, and our world utilize. In many of these new connected systems issues of safety, reliability, and resilience rival or dominate concerns for security and privacy, the long-time focus of many in the IT world. Without a scalable and efficient method for managing these risks so our enterprises can continue to benefit from these advancements that powers our military, commercial industries, cities, and homes to new levels of efficiency, versatility, and cost effectiveness we face the potential for harm, death, and destructiveness. In such a marketplace, creating, exchanging, and integrating components that are trustworthy as well as entering into value-chain relationships with trustworthy partners and service suppliers will be common if we can provide a method for explicitly defining what is meant by the word trustworthy. The approach being pursued by these groups for applying Software Assurance to these systems and their Supply Chains by leveraging Structured Assurance Cases (the focus of this paper), Software Bill of Materials, and secure development practices applied to the evolving Agile and DevSecOps methodologies, is to explicitly identify the detailed requirements “about what we need to know about something for it to be worthy of our trust” and to do that in a way that we can convey that basis of trust to others that: can scale; is consistent within different workflows; is flexible to differing sets of hazards and environments; and is applicable to all sectors, domains, and industries.
On a Redundancy of AIFV-m Codes for m =3,5

Hu, Yamamoto, Honda proposed the binary AIFVm codes and proved that the worst-case redundancy of optimal binary AIFV-m codes is exactly 1/m for m ∈{2,3,4}. We derive a new upper bound on the redundancy of optimal binary AIFV-3 codes when the probability of the most likely source symbol is known. Furthermore, it is proved by using the redundancy bound of optimal binary AIFV-3 codes that the worst-case redundancy of optimal binary AIFV-5 codes is exactly 1/5.
Utilization of Dynamic Telecytopathology for Rapid Onsite Evaluation of Touch Imprint Cytology of Needle Core Biopsy: Diagnostic Accuracy and Pitfalls

Background: In the era of personalized medicine, there has been an increase in demand on cytopathology service to perform rapid onsite evaluation of touch imprints (ROSETIs) of needle core biopsies (NCBs) to ensure sample adequacy and provide preliminary diagnosis. Limited publications have addressed use of telecytopathology for ROSETIs. We present our experience with telecytopathology-guided ROSETIs of NCBs. Materials and Methods: Cytotechnologist onsite transmitted real-time images of Diff-QUIK stained touch imprints of NCBs using an Olympus digital camera through Ethernet to a pathologist. The preliminary diagnosis from telecytopathology-guided ROSETIs was compared with that of the final NCB for diagnostic accuracy. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Results: Of 186 cases, 163 (87%) were concordant and 23 (13%) were discordant. Of the 23 discordant cases, 14 (58%) were diagnosed as benign with final NCB diagnoses as malignant and 9 (41%) were suspicious on ROSETIs with final NCB diagnoses as benign. The causes of discordance among cases categorized as benign on preliminary and malignant on final included interpretative error (9) and sampling (6). Six cases categorized as suspicious on preliminary and negative on final biopsy diagnosis correlated with concurrent fine-needle aspiration, raising the possibility of loss of diagnostic areas in processed tissue cores. Remaining three cases in this category represented misinterpretation of reactive cells. Sensitivity, specificity, PPV, and NPV were 87.9%, 95.7%, 97.1%, and 82.7%, respectively. Conclusions: Telecytopathology-guided ROSETI yields high accuracy for preliminary interpretation of NCBs and may be utilized as an effective substitute for conventional microscopy.
A Semi-quantum Group Signature Scheme Based on Bell States

In this paper, we propose a group signature scheme based on Bell states, which restricts the group member Alice to transmit the signature message to a fully quantum ability group manager TP through semi-quantum communication, then TP sends the signature message to an outsider verifier Bob to verify the signature. The signature message is sent by TP which can ensure Alice’s anonymous in group signature scheme. Besides, the limited Alice is a “classic” user in semi-quantum communication and can only measure, prepare, reorder and send quantum states only in the classical basis { $$ \left| 0 \right\rangle $$ , $$ \left| 1 \right\rangle $$ }, while the quantum TP can perform arbitrary quantum operations. Besides, the “classical” user Alice does not require quantum memory. Finally, the security analysis shows that the scheme can not only resist the repudiation of the signer and denial of the verifier, but also detect the forgery of the attacker, and the efficiency of the scheme is slightly higher than most of the previous signature schemes.
VitaFALL: Advanced Multi-Threshold Based Reliable Fall Detection System

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for significant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. So even for the daily activity in the life of aged people, an automatically fall detecting system and vital signs examining system become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device, could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people. Methods: Comparison with Present Algorithms, there are various benefits regarding privacy, success rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algorithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%, ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS module so that aged can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The experiment results show the adequacy of the proposed approach.
Alternativas para a prática de exercícios físicos durante a pandemia da COVID-19 e distanciamento social

during the COVID-19 pandemic and social distancing
Demonstrating the voice-based exploration of large data sets with CiceroDB-zero

This demonstration enables participants to explore large data sets via voice interfaces. The focus of the demonstration is on methods generating concise speech descriptions of query results, specified by users via voice input. The technical novelty of the demonstrated system lies in the fact that processing overheads are mostly moved into a pre-processing phase, generating speeches for batches of queries defined via templates. Visitors can access the demo via smart speakers on-site or via their own smart phones. They will be able to customize the generated voice descriptions and to tune voice output methods.
Towards Semantic Aspect-Based Sentiment Analysis for Arabic Reviews

Sentiment analysis is a text mining discipline that aims to identify and extract subjective information. This growing field results in the emergence of three levels of granularity (document, sentence, and aspect). However, both the document and sentence levels do not find what exactly the opinion holder likes and dislikes. Furthermore, most research in this field deals with English texts, and very limited researches are undertaken on Arabic language. In this paper, the authors propose a semantic aspect-based sentiment analysis approach for Arabic reviews. This approach utilizes the semantic of description logics and linguistic rules in the identification of opinion targets and their polarity.