Reviewerly/exHarmony huggingface dataset:
A Deep Genetic Programming Based Methodology for Art Media Classification Robust to Adversarial Perturbations

Art Media Classification problem is a current research area that has attracted attention due to the complex extraction and analysis of features of high-value art pieces. The perception of the attributes can not be subjective, as humans sometimes follow a biased interpretation of artworks while ensuring automated observation’s trustworthiness. Machine Learning has outperformed many areas through its learning process of artificial feature extraction from images instead of designing handcrafted feature detectors. However, a major concern related to its reliability has brought attention because, with small perturbations made intentionally in the input image (adversarial attack), its prediction can be completely changed. In this manner, we foresee two ways of approaching the situation: (1) solve the problem of adversarial attacks in current neural networks methodologies, or (2) propose a different approach that can challenge deep learning without the effects of adversarial attacks. The first one has not been solved yet, and adversarial attacks have become even more complex to defend. Therefore, this work presents a Deep Genetic Programming method, called Brain Programming, that competes with deep learning and studies the transferability of adversarial attacks using two artworks databases made by art experts. The results show that the Brain Programming method preserves its performance in comparison with AlexNet, making it robust to these perturbations and competing to the performance of Deep Learning.
Refining Implicit Argument Annotation for UCCA

Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation’s foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.
Differential Privacy Approach to Solve Gradient Leakage Attack in a Federated Machine Learning Environment

The growth of federated machine learning in recent times has dramatically leveraged the traditional machine learning technique for intrusion detection. Keeping the dataset for training at decentralized nodes, federated machine learning have kept the people’s data private; however, federated machine learning mechanism still suffers from gradient leakage attacks. Adversaries are now taking advantage of those gradients and can reconstruct the people’s private data with greater accuracy. Adversaries are using these private network data later on to launch more devastating attacks against users. At this time, it becomes essential to develop a solution that prevents these attacks. This paper has introduced differential privacy, which uses Gaussian and Laplace mechanisms to secure updated gradients during the communication. Our result shows that clients can achieve a significant level of accuracy with differentially private gradients.
Vehicle Safety of the Velocity Obstacle Algorithm

This paper presents a mathematical analysis of the velocity obstacle algorithm applied to a nonholonomic vehicle for avoiding a moving obstacle in the plane. The velocity obstacle algorithm can be used for local navigation among dynamic obstacles by continually computing a set of unsafe velocities, and avoid the velocities inside of this set. The method is commonly used for reactive collision avoidance as it requires only limited knowledge of the obstacle behaviour and is computationally inexpensive. A drawback of previous analyses is the assumption that the vehicle and the obstacle are constrained to follow specific types of paths, or the velocities are assumed constant. We analyze the algorithm without such constraints and derive a set of conditions to prove that vehicle safety can be guaranteed in the general case. Moreover, we prove that the method can safely be applied to vehicles subject to nonholonomic constraints, with limited turning rates.
Preventing Fake Information Generation Against Media Clone Attacks

Fake media has been spreading due to remarkable advances in media processing and machine leaning technologies, causing serious problems in society. We are conducting a research project called Media Clone aimed at developing methods for protecting people from fake but skillfully fabricated replicas of real media called media clones. Such media can be created from fake information about a specific person. Our goal is to develop a trusted communication system that can defend against attacks of media clones. This paper describes some research results of the Media Clone project, in particular, various methods for protecting personal information against generating fake information. We focus on 1) fake information generation in the physical world, 2) anonymization and abstraction in the cyber world, and 3) modeling of media clone attacks.
Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task.

We describe the joint submission of the University of Edinburgh and Charles University, Prague, to the Czech/English track in the WMT 2020 Shared Task on News Translation. Our fast and compact student models distill knowledge from a larger, slower teacher. They are designed to offer a good trade-off between translation quality and inference efficiency. On the WMT 2020 Czech ↔ English test sets, they achieve translation speeds of over 700 whitespace-delimited source words per second on a single CPU thread, thus making neural translation feasible on consumer hardware without a GPU.
Findings of the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT.

We describe the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT. In both tasks, the community studied German to Upper Sorbian and Upper Sorbian to German MT, which is a very realistic machine translation scenario (unlike the simulated scenarios used in particular in much of the unsupervised MT work in the past). We were able to obtain most of the digital data available for Upper Sorbian, a minority language of Germany, which was the original motivation for the Unsupervised MT shared task. As we were defining the task, we also obtained a small amount of parallel data (about 60000 parallel sentences), allowing us to offer a Very Low Resource Supervised MT task as well. Six primary systems participated in the unsupervised shared task, two of these systems used additional data beyond the data released by the organizers. Ten primary systems participated in the very low resource supervised task. The paper discusses the background, presents the tasks and results, and discusses best practices for the future.
RETRACTED: Target detection algorithm for basketball moving images based on improved gaussian mixture model

The various methods used to detect and monitor basketball are explored in detail. Object detection and tracking and video Object detection and tracking are realized in a high-performance proportional manner. But most methods suffer from difficult predictions. The problematic reduction can occur at any stage, such as preprocessing, disassembly, feature extraction, background subtraction, and hole-filled ball tracking. In this article, the ball detection and tracking method based on the study method path is the growing method in the region, the public method, and the average transformation method using HSV color space and system features. Extensive inquiries into strategies, implementation issues, and future development trends. This research, edge detection, grayscale processing, object capture, target recognition, image detection technology, etc. are integrated into the game video's actual need to achieve the various requirements of motion image detection. At the same time, it has established a testing ground for research players, sports recognition, sports behavior judgment, etc. Background subtraction is a typical computer vision for Jobs. Methods examined Pixel is commonly used. Develop practical adaptive algorithms. Use a Gaussian probability density mixture. The recursive formula is used. Choose the appropriate number of parameters that are continuously updated and regions Per Pixel. The image data normalization and space-based communication planning and coordination of the work authorization model of small l is the static representation.
Hindi-Marathi Cross Lingual Model.

Machine Translation (MT) is a vital tool for aiding communication between linguistically separate groups of people. The neural machine translation (NMT) based approaches have gained widespread acceptance because of its outstanding performance. We have participated in WMT20 shared task of similar language translation on Hindi-Marathi pair. The main challenge of this task is by utilization of monolingual data and similarity features of similar language pair to overcome the limitation of available parallel data. In this work, we have implemented NMT based model that simultaneously learns bilingual embedding from both the source and target language pairs. Our model has achieved Hindi to Marathi bilingual evaluation understudy (BLEU) score of 11.59, rank-based intuitive bilingual evaluation score (RIBES) score of 57.76 and translation edit rate (TER) score of 79.07 and Marathi to Hindi BLEU score of 15.44, RIBES score of 61.13 and TER score of 75.96.
CAMV: Class Activation Mapping Value Towards Open Set Fine-Grained Recognition

Open Set Fine-Grained Recognition (OSFGR) aims at distinguishing known fine-grained categories from the data (contain known and unknown categories). The feature distribution of the known fine-grained category usually has the characteristics of small inter-class variance and large intra-class variance. Directly using the traditional Open Set Recognition (OSR) method on OSFGR does not achieve competitive performance. This is mainly due to the fact that the traditional OSR method is designed based on SoftMax function, whose translation invariance for input weakens the representation ability for the known fine-grained category in OSFGR. To settle this problem, we present a unified method based on class activation mapping value (CAMV) for OSFGR, which preserves the original discriminate feature. Besides, most OSR methods are based on the discrimination model with multi-class thresholds. When most known categories need to recognize, it will face the high computational cost and low efficiency. To overcome this problem, we propose the discrimination model with fusion one-class thresholds, which can greatly improve the inference speed without losing the recognition accuracy. Meanwhile, our idea can settle the extreme situation of OSR (only one known category). Our method achieves the state-of-art performance on the fine-grained open set datasets consisting of 4 public fine-grained datasets.
Quantum Algorithms for Machine Learning and Optimization

Improvising the performance of machine learning for applications in the field of computer science leads to create new algorithms. As these are being optimized, using the algorithms of the classical machine learning in the area of quantum computing are being widely researched. First, we can consider general optimization problems with only functional Evaluations. Quantum Speedups of the problems can be improved compared to the classical algorithms. A frame work for quantum inspired classical algorithms can be introduced. To study the various problems on quantum computers on testing properties and compare the quantum speed ups with the classical counter parts.
Educational Data Mining to Improve Decision Support on the Ratio of students and Study Groups in Elementary Schools in Indonesia using K-Means Method

In the world of education, the process of processing space in learning activities is very important.This is in order to prevent unwanted behavior and can direct student activities.The purpose of the study was to analyze whether the mapping of regions (provinces) on the ratio of students to study groups (abbreviated as rombel) could be done by utilizing artificial intelligence techniques.The data source was obtained from the Ministry of Education and Culture which was processed by the Central Statistics Agency (abbreviated as BPS) for the academic year 2018/2019 which consisted of 34 records.The research is aimed at student to class ratios at the primary school level.The technique used is the kmeans method which is part of data mining.The analysis process was carried out with the help of Rapid Miner software.Two clusters of mapping labels were used, namely the largest student-to-class ratio cluster (K1) and the smallest student-to-class ratio cluster (K2).The analysis results show that 14 provinces are in the largest cluster (K2) with an average per class = 24.2 and 20 provinces are in the smallest cluster (K2) with an average per class = 19.1.The validity test was carried out by testing the cluster results (k = 2) with Davies Bouldin was 0.570.The cluster results are optimal.The validity test was also conducted on the results of the cluster with Performance (Classification) with the results of classification error: 0.00%.The results of the analysis can be used as input for the government in making policies so that the quality of human resources is increasingly competitive and can compete regionally and internationally.
People Under Surveillance Tracker Prototype Development with Bluetooth Low Energy to Suppress the Covid-19 Spread

The new coronavirus has infected people around the world.In cases or reports relating to the coronavirus, there are many terms or diction used by the Indonesian government, one of which is ODP (Insider Monitoring).Unsupervised ODP is one of the reasons for the increasing number of positive cases of COVID-19.Based on existing references, there are no studies that develop applications to monitor ODP.So that we will build a mobile-based ODP TRACKING prototype that aims to prevent the COVID-19 that can be caused by ODP by using the Rapid Application Development (RAD) methodology.The ODP Tracker application requires users such as the community, COVID-19 Village Handling Task Force, puskesmas, sub-district & district handling units, and the system administrator.Each of these users has a different role in the application but is related to one another.Several prototypes of the ODP Tracker application interface are shown in this study, including the Registration form interface, the ODP tracking interface, and the ODP report interface.The ODP Tracker application prototype can help the government or Covid-19 handling officers in Indonesia to track and monitor the whereabouts and conditions of people with ODP status.
Analyzing the Cyber Risk in Critical Infrastructures

Information and communication technology (ICT) plays an important role in critical infrastructures (CIs). Some ICT-based services are in itself critical for the functioning of society while other ICT elements are essential for the functioning of critical processes within CIs. Moreover, many critical processes within CIs are monitored and controlled by industrial control systems (ICS) also referred to as operational technology (OT). In line with the CI-concept, the concept of critical information infrastructure (CII) is introduced comprising both ICT and OT. It is shown that CIIs extend beyond the classical set of CIs. The risk to society due to inadvertent and deliberate CI/CII disruptions has increased due to the interrelation, complexity, and dependencies of CIs and CIIs. The cyber risk due to threats to and vulnerabilities of ICT and OT is outlined. Methods to analyze the cyber risk to CI and CII are discussed at both the organization, national, and the service chain levels. Cyber threats, threat actors, and the organizational, personnel, and technological cyber security challenges are outlined. An outlook is given to near future cyber security risk challenges, and therefore upcoming risk, stemming from (industrial) internet of things and other new cyber-embedded technologies.
Channel Positive and Negative Feedback Network for Target Tracking

Aiming at alleviate the detrimental effect of similar object interferences and target state changes in SiamRPN tracker, a Channel Positive and Negative Feedback Network (CPFN) is proposed, in which the Gaussian score map is generated by the feature channels selected by a Gaussian kernel, and the map is combined with the classification branches of SiamRPN. In this way, the feature channels are divided into positive feedback channels and interference channels, and these feature channels are effectively utilized. In addition, a channel weight update strategy is proposed to enhance the robustness of the tracker and avoid template pollution caused by inadequate template update. Extensive experiments on tracking benchmarks including VOT2016, VOT2018, VOT2019, OTB100, UAV123, LaSOT and GOT-10k show that the proposed CPFN outperforms the state-of-the-art methods based on small backbone network in terms of accuracy and achieves high-speed tracking.
Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Data mining is an unsupervised learning technique to extract the insights and hidden relationships among data. Data mining has more importance in data science and machine learning because through data mining all hidden information is shown to determine various aspects of the data set. Clustering is a data mining technique to group the data, on the basis of similarity measures. The objects or data points in a cluster are similar. Similarly, objects or data points in another cluster will also be similar. But when these clusters are compared, they are dissimilar to each other. Clustering is considered the most important unsupervised learning technique because it deals with finding a structure in a collection of unlabeled data. Clustering can be done by the different approaches like partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering. These clustering approaches can be done by the numbers of algorithms, such as K-means clustering, Fuzzy C-means clustering, Hierarchical clustering, DBSCAN, OPTICS, STING, ROCK and CACTUS. This proposed paper contained reviews of the above techniques by using a more powerful programming language (Python) as a tool. Evidence attainable from this study is helpful for researchers to select an appropriate clustering approach based on their domain.
Nonlocality of tripartite orthogonal product states

Local distinguishability of orthogonal product states is an area of active research in quantum information theory. However, most of the relevant results about local distinguishability are found in bipartite quantum systems and very few are known in multipartite cases. In this paper, we construct a locally indistinguishable subset in $${\mathbb {C}}^{2d}\bigotimes {\mathbb {C}}^{2d}\bigotimes {\mathbb {C}}^{2d}$$ , $$d\ge 2$$ that contains $$18(d-1)$$ orthogonal product states. Further, we generalize our method to arbitrary tripartite quantum systems $${\mathbb {C}}^{k}\bigotimes {\mathbb {C}}^{l}\bigotimes {\mathbb {C}}^{m}$$ . This result helps us to understand the role of nonlocality without entanglement in multipartite quantum systems. Finally, we prove that a three-qubit GHZ state is sufficient as a resource to distinguish each of the above classes of states.
Cut out the annotator, keep the cutout: better segmentation with weak supervision

Constructing large, labeled training datasets for segmentation models is an expensive and labor-intensive process. This is a common challenge in machine learning, addressed by methods that require few or no labeled data points such as few-shot learning (FSL) and weakly-supervised learning (WS). Such techniques, however, have limitations when applied to image segmentation---FSL methods often produce noisy results and are strongly dependent on which few datapoints are labeled, while WS models struggle to fully exploit rich image information. We propose a framework that fuses FSL and WS for segmentation tasks, enabling users to train high-performing segmentation networks with very few hand-labeled training points. We use FSL models as weak sources in a WS framework, requiring a very small set of reference labeled images, and introduce a new WS model that focuses on key areas---areas with contention among noisy labels---of the image to fuse these weak sources. Empirically, we evaluate our proposed approach over seven well-motivated segmentation tasks. We show that our methods can achieve within 1.4 Dice points compared to fully supervised networks while only requiring five hand-labeled training points. Compared to existing FSL methods, our approach improves performance by a mean 3.6 Dice points over the next-best method.
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 26,878 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 F1 of 51.1 over cross-modal questions, substantially outperforming a strong baseline that achieves 42.9 F1, but still lags significantly behind human performance, which is at 85.2 F1.