Reviewerly/exHarmony huggingface dataset:
Towards Causal Federated Learning For Enhanced Robustness and Privacy

Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.
VariTex: Variational Neural Face Textures

Deep generative models can synthesize photorealistic images of human faces with novel identities. However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we propose VariTex - to the best of our knowledge the first method that learns a variational latent feature space of neural face textures, which allows sampling of novel identities. We combine this generative model with a parametric face model and gain explicit control over head pose and facial expressions. To generate complete images of human heads, we propose an additive decoder that adds plausible details such as hair. A novel training scheme enforces a pose-independent latent space and in consequence, allows learning a one-to-many mapping between latent codes and pose-conditioned exterior regions. The resulting method can generate geometrically consistent images of novel identities under fine-grained control over head pose, face shape, and facial expressions. This facilitates a broad range of downstream tasks, like sampling novel identities, changing the head pose, expression transfer, and more. Code and models are available for research on https://mcbuehler.github.io/VariTex.
On the Impact of Random Seeds on the Fairness of Clinical Classifiers

Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness across demographic groups in clinical prediction tasks over electronic health records (EHR) in MIMIC-III -- the standard dataset in clinical NLP research. Apparent subgroup performance varies substantially for seeds that yield similar overall performance, although there is no evidence of a trade-off between overall and subgroup performance. However, we also find that the small sample sizes inherent to looking at intersections of minority groups and somewhat rare conditions limit our ability to accurately estimate disparities. Further, we find that jointly optimizing for high overall performance and low disparities does not yield statistically significant improvements. Our results suggest that fairness work using MIMIC-III should carefully account for variations in apparent differences that may arise from stochasticity and small sample sizes.
Towards Unbiased Random Features with Lower Variance For Stationary  Indefinite Kernels

Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite. And for non-stationary kernels, the corresponding RFF could be converted to that for stationary indefinite kernels when the inputs are restricted to the unit sphere. Numerous methods provide accessible ways to approximate stationary but indefinite kernels. However, they are either biased or possess large variance. In this article, we propose the generalized orthogonal random features, an unbiased estimation with lower variance.Experimental results on various datasets and kernels verify that our algorithm achieves lower variance and approximation error compared with the existing kernel approximation methods. With better approximation to the originally selected kernels, improved classification accuracy and regression ability is obtained with our approximation algorithm in the framework of support vector machine and regression.
Semi-supervised Learning Framework for UAV Detection

The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment. However, little or no attention has been given to the application of unsupervised or semi-supervised algorithms for UAV detection. In this paper, we proposed a semi-supervised technique and architecture for detecting UAVs in an environment by exploiting the RF signals (i.e., fingerprints) between a UAV and its flight-controller communication under wireless inference such as Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet packet transform, we estimated the second moment statistic (i.e., variance) of the coefficients in each packet as a feature set. We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. When detecting the presence of RF-based UAV, we achieved an accuracy of 96.7$\%$ and 86$\%$ at a signal-to-noise ratio of 30~dB and 18~dB, respectively. The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment.
MultiModalQA: Complex Question Answering over Text, Tables and Images

When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources. While interest in models that reason over multiple pieces of evidence has surged in recent years, there has been relatively little work on question answering models that reason across multiple modalities. In this paper, we present MultiModalQA(MMQA): a challenging question answering dataset that requires joint reasoning over text, tables and images. We create MMQA using a new framework for generating complex multi-modal questions at scale, harvesting tables from Wikipedia, and attaching images and text paragraphs using entities that appear in each table. We then define a formal language that allows us to take questions that can be answered from a single modality, and combine them to generate cross-modal questions. Last, crowdsourcing workers take these automatically-generated questions and rephrase them into more fluent language. We create 29,918 questions through this procedure, and empirically demonstrate the necessity of a multi-modal multi-hop approach to solve our task: our multi-hop model, ImplicitDecomp, achieves an average F1of 51.7 over cross-modal questions, substantially outperforming a strong baseline that achieves 38.2 F1, but still lags significantly behind human performance, which is at 90.1 F1
LIBRA: An Economical Hybrid Approach for Cloud Applications with Strict SLAs

Function-as-a-Service (FaaS) has recently emerged to reduce the deployment cost of running cloud applications compared to Infrastructure-as-a-Service (IaaS). FaaS follows a serverless 'pay-as-you-go' computing model; it comes at a higher cost per unit of execution time but typically application functions experience lower provisioning time (startup delay). IaaS requires the provisioning of Virtual Machines, which typically suffer from longer cold-start delays that cause higher queuing delays and higher request drop rates. We present LIBRA, a balanced (hybrid) approach that leverages both VM-based and serverless resources to efficiently manage cloud resources for the applications. LIBRA closely monitors the application demand and provisions appropriate VM and serverless resources such that the running cost is minimized and Service-Level Agreements are met. Unlike state of the art, LIBRA not only hides VM cold-start delays, and hence reduces response time, by leveraging serverless, but also directs a low-rate bursty portion of the demand to serverless where it would be less costly than spinning up new VMs. We evaluate LIBRA on real traces in a simulated environment as well as on the AWS commercial cloud. Our results show that LIBRA outperforms other resource-provisioning policies, including a recent hybrid approach - LIBRA achieves more than 85% reduction in SLA violations and up to 53% cost savings.
Improving Online Performance Prediction for Semantic Segmentation

In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i.e., during inference, which is of high importance in safety-critical applications such as autonomous driving. Here, many high-level decisions rely on such DNNs, which are usually evaluated offline, while their performance in online operation remains unknown. To solve this problem, we propose an improved online performance prediction scheme, building on a recently proposed concept of predicting the primary semantic segmentation task's performance. This can be achieved by evaluating the auxiliary task of monocular depth estimation with a measurement supplied by a LiDAR sensor and a subsequent regression to the semantic segmentation performance. In particular, we propose (i) sequential training methods for both tasks in a multi-task training setup, (ii) to share the encoder as well as parts of the decoder between both task's networks for improved efficiency, and (iii) a temporal statistics aggregation method, which significantly reduces the performance prediction error at the cost of a small algorithmic latency. Evaluation on the KITTI dataset shows that all three aspects improve the performance prediction compared to previous approaches.
Look Closer to Segment Better: Boundary Patch Refinement for Instance  Segmentation

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the PolyTransform + SegFix baseline, we reached 1st place on the Cityscapes leaderboard.
PENGEMBANGAN MODUL DIGITAL MATERI TEKS CERITA NARASI PADA SISWA KELAS VII SMP

The background of the research was due to the COVID-19 pandemic which was not over yet and the need for teaching materials that could be used digitally. This is of course based on the results of a need assessment or analysis of product needs in the two schools that are partners in this research. The product that appears in this study is a digital module that contains narrative text material for seventh grade students of junior high school. The purpose of this research is to find out; 1) the stages in developing a digital module for narrative text material for seventh grade junior high school students, 2) the feasibility of developing a digital module for narrative text material for seventh grade junior high school students, 3) implementing digital module development for seventh grade junior high school students. The type of research used in this research is research and development with the ADDIE development model (Analysis, Design, Development, Implementation and Evaluation). The research subjects are validators in the form of media experts, material experts, learning experts (users), and students. While the object of this research is the development of a digital module containing narrative text material for seventh grade students of junior high school. Data collection techniques used in this study were observation, interviews, and questionnaires. Meanwhile, the data analysis technique used in this study uses quantitative-descriptive analysis, because it presents data from the validation results in the form of numbers and describes the stages in product development in the form of digital modules. The results of this study are as follows, showing that the teaching materials that have been developed can be categorized as "Very Appropriate". This is evidenced by the results of the average score obtained from the experts, amounting to 97.6. The average score obtained from media experts is 106, from material experts is 96, and from teaching experts (teachers as users) is 92. The results of student responses to digital modules that have been implemented are 95.5. Based on this explanation, it can be concluded that research on the development of digital modules for narrative text material for seventh grade students of junior high school is expected to be able to use this digital module optimally in the process of teaching and learning activities and of course it can improve the quality of the process and learning outcomes in narrative texts.
A Summary of Covid-19 Datasets

This research presents a review of main datasets that are developed for COVID-19 research. We hope this collection will continue to bring together members of the computing community, biomedical experts, and policymakers in the pursuit of effective COVID-19 treatments and management policies. Many organizations, such as the World Health Organization (WHO), John Hopkins, National Institute of Health (NIH), COVID-19 open science table and such, in the world, have made numerous datasets available to the public. However, these datasets originate from a variety of different sources and initiatives. The purpose of this research is to summarize the open COVID-19 datasets to make them more accessible to the research community for health systems design and analysis. We also discuss the numerous resources introduced to support text mining applications throughout the COVID-19 literature; more precisely, we discuss the corpora, modelling resources, systems, and shared tasks introduced for COVID-19.
Learning Structured Information from Small Datasets of Heterogeneous Unstructured Multipage Invoices

We propose an end to end approach using graph construction and semantic representation learning to solve the problem of structured information extraction from heterogeneous, semi-structured, and high noise human readable documents. Our system first converts PDF documents into single connected graphs where we represent each token on the page as a node, with vertices consisting of the inverse euclidean distances between tokens. Token, lines, and individual character nodes are augmented with dense text model vectors. We then proceed to represent each node as a vector using a tailored GraphSAGE algorithm that is then used downstream by a simple feedforward network. Using our approach, we achieve state-of-the-art methods when benchmarked against our dataset of 205 PDF invoices. Along with generally published metrics, we introduce a highly punitive yet application specific informative metric that we use to further measure the performance of our model.
Cryptographic Algorithms Identification based on Deep Learning

The identification of cryptographic algorithms is the premise of cryptanalysis which can help recover the keys effectively. This paper focuses on the construction of cryptographic identification classifiers based on residual neural network and feature engineering. We select 6 algorithms including block ciphers and public keys ciphers for experiments. The results show that the accuracy is generally over 90% for each algorithm. Our work has successfully combined deep learning with cryptanalysis, which is also very meaningful for the development of modern cryptography and pattern recognition.
Evaluation of Machines Learning Algorithms in Detection of Malware-based Phishing Attacks for Securing E-Mail Communication

Malicious software, commonly known as Malware is one of the most significant threats facing Internet users today. Malware-based phishing attacks are among the major threats to Internet users that are difficult to defend against because they do not appear to be malicious in nature. There were several initiatives in combating phishing attacks but there are many difficulties and obstacles encountered. This study deals with evaluation of machine learning algorithms in detection of malware-based phishing attacks for securing email communication. It deeply evaluate the efficacy of the algorithms when integrated with major open-source security mail filters with different mitigation techniques. The main classifiers used such as SVM, KNN, Logistic Regression and Naïve Bayes were evaluated using performance metrics namely accuracy, precision, recall and f-score. Based on the findings, the study proposed improvement for securing e-mail communication against malware-based phishing using the best performing machine-learning algorithm to keep pace with malware evolution.
Crypto your Belongings by Two Pin Authentication using Ant Algorithm based Technique

Everyone realize data is one of the important strategic for any company to run and win the business. Let it be a mobile apps, websites and so on, there are more chances that our personal data like images, videos, texts get expose while we share across for different purposes. Even though the company says app, website forms are encrypted, the said company itself uses the data internally for their business development. This research presents how one can secure own’s data themselves before sending. There are many cryptography methods that has evolved from time to time. Upon researching and analyzing, I present a unique method to encrypt and decrypt the data, using combination of techniques such as Cryptographic technique, ANT Algorithm based formula and logic gates that would provide stronger protection to the data. Secure your images, videos with a 2-pin authentication and protection to encrypt and decrypt the data. A user must provide 2 different symmetric pins to encrypt and decrypt, where first pin shall be up to 4-digit secret pin and a second pin is a single digit pin. Single digit pin acts on how many stages the encryption takes place. The proposed method had been experimented on several images and videos. This study reveals, A combination of secret keys, ANT algorithm and Logic gates makes difficult for anyone to hack the data. This unique methodology helps us to protect our data more safely at source device itself.
A Pedestrian Counting Scheme for Video Images

Pedestrian counting aims to compute the numbers of pedestrians entering and leaving an area of interest based on object detection and tracking techniques. This paper proposes a simple and effective approach of pedestrian counting that can effectively solve the problem of pedestrian occlusion.Firstly, the moving objects are detected by the median filtering and foreground extraction with the improved mixed Gaussian model. And then the HOG (Histogram of oriented gradient) features detection and the SVM (Support vector machine) classification are applied to identify the pedestrians. A pedestrian dataset containing 1500 positive samples, 12000 negative samples, and 420 hard examples, which gave the false discriminant results with the initial classifier, also considered as negative samples to enhance classification capability is employed. In addition, the Kalman filtering with BLOB analysis for dynamic target tracking is chosen to predict pedestrian trajectory.This method greatly reduces the target misjudgment caused by overlapping and completes the two-way counting. Experiments on pedestrian tracking and counting in video images demonstrate promising performance with satisfactory recognition rate and processing time.
The relationship between teaching staff procrastination and their organizational and professional characteristics

Introduction. Reforming general secondary education requires a change in the role of a teacher (facilitator, moderator, tutor) and requires their ability to introduce new learning technologies and developed soft skills (the ability to work in a team, solve problems and find creative solutions, etc.). Teachers often lack confidence in their abilities, fear failure, and want to postpone the plans for their professional improvement and personal growth, which is called procrastination.There fore, in order to prevent procrastination, there is a need to study the level of its manifestation in pedagogical workers and to determine the factors that effectthis phenomenon. Aim.To determine the levels of procrastination in teaching staff and to find out the relationship between them and teachers' organizational and professional characteristics (self-organization of activities, planning effectiveness, professional motivation, organizational abilities, professional burnout). Methods. To determine the level of teaching staff procrastination, we used the Degree of Procrastination questionnaire by M. Kiseleva. In order to determine teachers' organizational and professional characteristics, five questionnaires were used: O. Mandrikova Self-organization of the Activity, Efficiency of Planning,A. Rean Motivation for Success and Fear of Failure, Express Assessment of Organizational Abilities, and Burnout syndrome in Human-Human Professions. Results. There was a relationship between the level of teaching staff procrastination and their organizational and professional factors such as effective planning, success motivation and fear of failure, organizational abilities, and professional burnout. No statistically significant relationship was found between the level of teaching staff procrastination and their self-organization of the activities. Conclusions.High levels of teaching staff procrastination have a negative impact on the development of teachers' professional motivation, readiness to plan and fulfill professional duties on time, as well as contributes to their emotional exhaustion, reduces their optimism and positive attitude towards work and colleagues, etc.
Social reflection as the basis of the mechanism of economic socialization of the individual

Introduction. Economic socialization of the individual, which regulates socio-economic processes, takes place through the mechanism of social reflection and is realized in the system of social relations. Social reflection allows creating conditions for the development of the individual including their economic socialization. Aim. To make a theoretical study of the role of social reflection in economic society as the basis of the mechanism of economic socialization. Methods. Theoretical analysis of social reflection and its interpretation in psychological science. Results. The concept of reflection is a psychological phenomenon. It is the basis for personal independence and activity in the process of developing economic knowledge and economic experience in relations with other people. Conclusions. The concept of social reflection is a purely psychological phenomenon that represents a mental rational process or act aimed at analyzing and understanding of oneself, one's own actions, behavior, language, experience, feelings, states, abilities, character, and interpersonal relationships. The result of reflection is the manifestation of a social theory that explains the essence of economic socialization of the individual.