Reviewerly/exHarmony huggingface dataset:
Building ML Based Intelligent System to Analyze Production LSI Live Site Incidents

Providing support on the rolled-out application/services is one of the major factors in increasing the customer satisfaction which in turn increases the customer retention. Since we are in the era of automation where most of the day-to-day jobs are taken care of or are facilitated by the technologies around us, hence there is a need to reduce manual effort in triaging the support tickets and hence facilitating the person on call to better close the tickets on time with proper remediation. The machine learning model which will be the product of this complete paper will not only help in classifying the tickets but also, if applicable will give the best possible remediation of the ticket there by reducing the manual effort and the time taken on providing necessary solution on the ticket. The objectives of the work are as follows - a) Understand the data that is present in the ticket and figure out the basic understanding like, categories of issues, trends etc. b) Prepare the data which is ready for applying different classification algorithms. d) Identify the best machine learning model which can classify the new incident with utmost accuracy. e) Prepare a machine learning model which can suggest the best possible remediation of the ticket. f) Integrate the best classification model and solution recommender model and wrap it as an API which can be used by end user.
Paraphrasing Emotions in Portuguese

In this paper we describe the cooperation between the NooJ-based paraphrasing system eSPERTo and the broad-coverage emotion annotation of Linguateca’s corpora, Emocionário, to develop strategies for analyzing the paraphrasing of emotions. We start by briefly introducing these two projects. Then, we describe how we selected five parallel texts in European and Brazilian Portuguese, automatically annotated them with Emocionário, and carefully revised the annotation, while paying special attention to multiword units and other phrases representing emotion paraphrastic units, and created a set of putative paraphrasing grammars in NooJ. We also comment on the translations and discuss disagreements on the annotation of emotions, as well as on formalization strategies used in the internal structure of the paraphrases.
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. In this paper, we point out and solve a novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem by enabling each client to exploit multi-source data distributions under the challenging constraint of data decentralization. Our approach transmits the distribution information across clients in a privacy-protecting way through an effective continuous frequency space interpolation mechanism. With the transferred multi-source distributions, we further carefully design a boundary-oriented episodic learning paradigm to expose the local learning to domain distribution shifts and particularly meet the challenges of model generalization in medical image segmentation scenario. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks. The code is available at "https://github.com/liuquande/FedDG-ELCFS".
Multi-channel image deblurring using coded flashes

Motion blur is easily observable in an image having moving objects, and the coded exposure photography is one of the well-known solutions to its removal. In this paper, we propose a novel coded exposure photography technique to capture images of moving objects using coded flashes. Noting that the camera acquires a multi-channel image at once by using a color filter array, we modulate each channel image differently by using multi-band coded flashes. By modulating the point spread function of the acquired channel image to have a jointly invertible point spread function, its deconvolution performance is significantly improved compared to the existing single image coded exposure photography. We prove the effectiveness of the proposed method through experiments using synthetic and real data.
Impressões da equipe de enfermagem acerca da pandemia da COVID-19

The aim was to identify the impressions of nursing professionals about work in the pandemic of the new coronavirus and describe these impressions. Descriptive cross-sectional study developed from the qualitative approach. The Free and Informed Consent Term was prepared in clear, easy to understand language, providing information to the research participants, as well as guaranteeing anonymity and the refusal to participate in the study and the withdrawal from the study at any time, without any burden. for the study participant. Nurses are on the front lines and are the basis of any response activity. There is a fine line between dying and living, there is a set of factors in addition to the severity of living with the virus, prejudice, stigma, breaking social and emotional relationships are factors that make the subjects feel helpless and depressed. , leading to a negative perspective of this new context experienced. For the study subjects, training and PPE bring protection and safety, but they need to be accompanied by technical training and knowledge, as well as safety, respect and appreciation of nursing professionals.
Poster: Development of Managerial Skills in Engineering University Students in the Context of Modern Industrial Revolutions

The paper theoretically substantiates the need for managerial skills development in engineering students. The paper is focused on the basic traits necessary for the successful formation of managerial skills in engineering students, the impact of gender and “emotional quotient” (EQ) on leadership style, and the development of “hard” and “soft” skills. For a manager, there are no absolutely bad or good personal qualities, but their totality is a manageable resource. The gender factor here has a significant impact on management style and quality. Training programs based on students’ personal characteristics and specific needs, a combination of coaching, training and individual classes guarantee the required level of managerial knowledge and skills. The emphasis is on the fact that training technologies and coaching methods are effective for the formation and development of managerial skills among students of engineering specialties. The authors believe that under current conditions, a greater emphasis in management practice should be placed on innovative principles and methods related to the formation and development of neuroplasticity, Neuro Science, EQ, etc. in engineering students.
Attention-based cross-modality interaction for multispectral pedestrian detection

Multispectral pedestrian detection has attracted extensive attention, as paired RGB-thermal images can provide complementary patterns to deal with illumination changes in realistic scenarios. However, most of the existing deep-learning-based multispectral detectors extract features from RGB and thermal inputs separately, and fuse them by a simple concatenation operation. This fusion strategy is suboptimal, as undifferentiated concatenation for each region and feature channel may hamper the optimal selection of complementary features from different modalities. To address this limitation, in this paper, we propose an attention-based cross-modality interaction (ACI) module, which aims to adaptively highlight and aggregate the discriminative regions and channels of the feature maps from RGB and thermal images. The proposed ACI module is deployed into multiple layers of a two-branch-based deep architecture, to capture the cross-modal interactions from diverse semantic levels, for illumination-invariant pedestrian detection. Experimental results on the public KAIST multispectral pedestrian benchmark show that the proposed method achieves state-of-the-art detection performance.
Getting a Head Start on Program Synthesis with Genetic Programming

We explore how to give Genetic Programming (GP) a head start to synthesize a programming problem. Our method uses a related problem and introduces a schedule that directs GP to solve the related problem first either fully or to some extent first, or at the same time. In addition, if the related problem’s solutions are written by students or evolved by GP, we explore the extent to which initializing the GP population with some of these solutions provides a head start. We find that having a population solve one programming problem before working to solve a related programming problem helps to a greater extent as the targeted problems and the intermediate problems themselves are selected to be more challenging.
Deep Neural Network Based Malicious Network Activity Detection Under Adversarial Machine Learning Attacks

Machine learning-based computational intelligence methods are used more often recently in the cybersecurity area, especially for malicious network activity detection. ML based solutions have been used and discussed by a significant number of authors in literature. Several methods, including deep learning, are used to develop models for solving this issue. So far, attackers try to generate malicious activities in a network to put down several system services or steal some information from the databases. More recent designs of security components use predictive modeling approach to detect such kind of attacks. Thus, the new target for the attackers is machine learning algorithm itself. Previous studies in cybersecurity have almost exclusively focused on attack detection in a network. Another promising line of attack detection research would be machine learning algorithm protection. There are some attacks against deep learning models in the literature, including fast-gradient sign method (FGSM) attack. This attack is the purest form of the gradient-based evading technique that is used by attackers to evade the classification model. This paper presents a new approach to protect a malicious activity detection model from the FGSM attack. Hence, we explore the power of applying adversarial training to build a robust model against FGSM attacks. Accordingly, (1) dataset enhanced with the adversarial examples; (2) deep neural network-based detection model is trained using the KDDCUP99 dataset to learn the FGSM based attack patterns. We applied this training model to the benchmark cyber security dataset.
Fuzzy Clustering of Influence of Oxygen on Oxidation of Isobutylene in Pulse Mode on Tin-Antimony Catalyst

The research of impact of oxygen on oxidation of isobutylene on tin-antimony catalyst was carried out by using impulsive microcatalytic methods. The effect of oxygen in the gas phase on the state of the catalyst surface, which consists in a change in its chemical composition, leading to a change in the specific activity of the catalyst, has been experimentally proved. The dependence of the activity of a catalyst that has undergone preliminary exposure in an oxygen-containing gas stream on the concentration of oxygen in a pulse is found. The obtained data are comparable with the results of a similar study carried out under conditions excluding changes in the state of the catalyst surface under the influence of oxygen in the gas phase. The observed differences in the nature of the dependence of the rates of deep and soft oxidation of isobutylene reflect the effect of oxygen on the state of the working surface layer of the catalyst.
Elimination of CMOS fixed pattern noise based on PCA wavelet transform

This paper presents an effective fixed pattern noise (FPN) removal method (PCA wavelet method). Compared with the traditional filtering method and statistical method, PCA wavelet method has better denoising effect. Compared with the optimization method, PCA wavelet method has faster processing speed. It is a comprehensive FPN removal method. Compared with the optimization method, this method does not need complicated parameter adjustment process, but has similar denoising effect with the optimization method. This method is very suitable for the engineering application which has certain requirements for calculation speed and denoising effect.
Extraction of Question-related Sentences for Reading Comprehension Tests via Attention Mechanism

Attention mechanism is now being widely used in machine translation, image segmentation, and many other neural network-based applications. In the attention mechanism, there is an index called "attention score" which could reflect the relevance degrees between the output tokens with the input tokens. In this paper, we proposed a method using the attention score in extracting the question-related sentences from the articles of reading comprehension tests. We investigated and compared the accuracies of the extracted sentences when in word-level embedding, sentence-level embedding, and using the traditional cosine similarity. Through the evaluation of the randomly selected results of the extracted sentences, the method using the attention mechanism with sentence-level embedding obtained the accuracy of 88.3%, which was 80.9% when using word-level embedding and 83.5% when using the general cosine similarity. Meanwhile, the sentence-level embedding method also obtained the highest precision and recall in the three methods. The results suggest that using the attention mechanism with sentence-level embedding could extract the question-related sentences more accurately than traditional cosine similarity.
Identification of the Exclusivity of Individual’s Typing Style Using Soft Biometric Elements

Biometric is used as a main security fence in a computer system. The unique characteristics of a person can be distinguished from each other. Human’s biometrics can be categorized into three types: morphological, biological and behavioural. Morphological biometrics uses physical features for recognition. Biological biometrics used to identify user based on biological features. Behavioural biometrics such as gender, culture, height and weight can be used as an additional security measure within a system. These biometric behavioural features are also known as soft biometric. This study uses soft biometric elements (gender, culture, region of birth and educational level) in the keystroke dynamic study to distinguish typing patterns in each of these categories. The Support Vector Machine (SVM) classification method is used to perform this classification for soft biometric identification. The results of this study have shown that soft biometrics in keystroke dynamic can be used to distinguish group of individuals typing.
Towards Low Cost and Smart Load Testing as a Service Using Containers

Providing end-users with high quality e-commerce, online communication, education services requires careful performance monitoring, tuning and prediction under heavy traffic loads. To address this issue, we propose and evaluate a novel methodology using Docker containers for load testing. Our experience over several benchmarks, local machines vs. Cloud, and web servers suggest that load testing as a service requires a multi-dimensional optimization over slave counts, network latencies, bandwidth, and traffic patterns and there are opportunities for learning these parameters that can later be modelled into a smart load testing algorithm, with machine learning at the driver seat. Beyond the ease and speed of deployment, containers and cloud also provide a low cost alternative to load testing; we completed our cloud experiments by spending only $10. The only disadvantage of public clouds can be their centralized nature and distance to real customer bases.
Light Field Reconstruction with Arbitrary Angular Resolution Using a Deep Coarse-To-Fine Framework

Densely-sampled light fields (LFs) are favorable for numerous applications like 3D scene reconstruction, virtual reality, et al., while the acquisition is costly. Most of the current view synthesis approaches need to sample the input LFs in a special or regular pattern, which makes the actual acquisition difficult. In this article, a new coarse-to-fine deep learning framework is presented to reconstruct densely-sampled LFs with arbitrary angular resolution. Concretely, a rough reconstruction based on meta-learning is performed on each epipolar plane image (EPI) to achieve arbitrary proportion of upsampling, followed by a refinement with 3D convolutional neural networks (CNNs) on stacked EPIs. Both modules are differentiable so that the network is end-to-end trainable. In addition, these two steps are performed on 3D volumes extracted from LF data first horizontally, and then vertically, forming a pseudo-4DCNN which can synthesize 4D LFs from a group of sparse input views effectively. The key advantage is to efficiently synthesize LFs with arbitrary angular resolution using a single model. The presented approach compares superiorly against various state-of-the-art methods on various challenging scenes.
Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

The Quantum Fisher Information (QFI) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFI matrix for a model with $d$ parameters, however, is computationally expensive and generally requires $\mathcal{O}(d^2)$ function evaluations. To remedy these increasing costs in high-dimensional parameter spaces, we propose using simultaneous perturbation stochastic approximation techniques to approximate the QFI at a constant cost. We present the resulting algorithm and successfully apply it to prepare Hamiltonian ground states and train Variational Quantum Boltzmann Machines.
The Application of Big Data Technology in the Construction of Smart Campus in Vocational Colleges

Abstract Informational education is a new requirement for education in the information age and it is an inevitable trend in the development of education modernization. In the rapid development of the Internet, the use of cloud computing technology can play a greater role in the construction of smart campuses. It can not only improve the school’s digital service management level, but also promote the progress of education. This article briefly introduces the concept and necessity of smart campus construction, analyzes the role of big data technology in the construction of smart campus, and proposes smart campus construction measures based on big data.
Secure Watermark for Deep Neural Networks with Multi-task Learning

Deep neural networks are playing an important role in many real-life applications. After being trained with abundant data and computing resources, a deep neural network model providing service is endowed with economic value. An important prerequisite in commercializing and protecting deep neural networks is the reliable identification of their genuine author. To meet this goal, watermarking schemes that embed the author's identity information into the networks have been proposed. However, current schemes can hardly meet all the necessary requirements for securely proving the authorship and mostly focus on models for classification. To explicitly meet the formal definitions of the security requirements and increase the applicability of deep neural network watermarking schemes, we propose a new framework based on multi-task learning. By treating the watermark embedding as an extra task, most of the security requirements are explicitly formulated and met with well-designed regularizers, the rest is guaranteed by using components from cryptography. Moreover, a decentralized verification protocol is proposed to standardize the ownership verification. The experiment results show that the proposed scheme is flexible, secure, and robust, hence a promising candidate in deep learning model protection.
Implementation of HSV- based Thresholding Method for Iris Detection

Image thresholding is one of the most frequently used methods in image processing to perform digital image processing. Image thresholding has a technique that can separate the image object from its background. This is a technique that is quite good and effective for segmenting love. In this study, the threshold method used will be combined with the HSV mode for color detection. The threshold method will separate the object and the image background, while HSV will help improve the segmentation results based on the Hue, Saturation, Value values to be able to detect objects more accurately. Segmentation is carried out using the original input image without pre-processing or direct segmentation. As we know that in digital image processing, there are steps that are usually done to get a good input image, namely pre-processing. In this pre-processing stage, processes such as image conversion and image intensity changes are carried out so that the input image is better. Therefore, even though the input image is used without going through the pre-processing stage, the object can be segmented properly based on the color type of the object. The results of this segmentation can later be used for recognition and identification of image objects. The results of the test method for object segmentation achieved a color similarity level of 25%, with an accuracy rate of 75% in detecting uniform color objects. So that this method can be one of the most effective methods in segmenting image objects without pre-processing or direct thresholding
Applications of Computational Intelligence in Computer Music Composition

Engaging computers in composing musical pieces is a challenging and trending field of research. The musical tasks that can be performed or aided by computers’ computational powers, are numerous. This paper is concerned with applications of computational intelligence in music composition. Its main objective is to survey various computational intelligence techniques for performing miscellaneous music composition tasks. To achieve this objective, we first define each music composition task, then we discuss the recent applications of each, and the techniques adopted in them. We also highlight the most suitable techniques for performing each task. Our study shows that the most suitable techniques for human composers imitative systems are case-based reasoning and artificial neural networks. It is also shown that Markov models are more suitable for predicting musical notes based on the given previous notes. Genetic algorithms excel in chord progressions generation. Deep neural networks are clever at capturing temporal information of a musical piece. The state-of-the-art generative adversarial networks produce music as close as possible to real compositions. At the end of this study, we shed the light on many future research directions in the field of computer music composition.