Reviewerly/exHarmony huggingface dataset:
Partial 3D Image Reconstruction by Cuboids Using Stable Computation of Hahn Polynomials

In this paper, we will present a new method of partial reconstruction of the 3D image. The latter is based on the following two concepts: one is the stable discrete orthogonal Hahn polynomials (DOHPs), which significantly reduces numerical defects. The other is the 3D image cuboid representation (ICR) to accelerate the computation time of discrete orthogonal Hahn moments (DOHMs) and improve the quality of 3D image reconstruction. The results of the simulation confirm the ability of the 3D image reconstruction using the proposed process.
Word Sense Disambiguation using KeNet

The highly studied Natural Language Processing (NLP) problem Word Sense Disambiguation (WSD) is the process of removing the ambiguities of multiple-sense words that have the same morphological structure. The first step of WSD is to list the probable meanings of the word.The next step is identify the meaning which is used in the context within the sentence. A Turkish WordNet called KeNet is used to list the word-senses. The elimination of the ambiguity was done with BERT word embeddings by comparing with the meaning via cosine similarity. The impact of the system is evaluated by both with and without adding it to a search engine of Turkish news. Each news of topmost 10 news returned for each query is manually labeled as related or not related.Results of the labeling, relatedness of the first n documents, and ordered biased precision metrics are evaluated. Positive increment on the results is shown when the WSD modul is added on the system.
Task Similarity Aware Meta Learning: Theory-inspired Improvement on MAML

Few-shot learning ability is heavily desired for machine intelligence. By meta-learning a model initialization from training tasks with fast adaptation ability to new tasks, model-agnostic meta-learning (MAML) has achieved remarkable success in a number of few-shot learning applications. However, theoretical understandings on the learning ability of MAML remain absent yet, hindering developing new and more advanced meta learning methods in a principle way. In this work, we solve this problem by theoretically justifying the fast adaptation capability of MAML when applied to new tasks. Specifically, we prove that the learnt meta-initialization can quickly adapt to new tasks with only a few steps of gradient descent. This result, for the first time, explicitly reveals the benefits of the unique designs in MAML. Then we propose a theory-inspired task similarity aware MAML which clusters tasks into multiple groups according to the estimated optimal model parameters and learns group-specific initializations. The proposed method improves upon MAML by speeding up the adaptation and giving stronger few-shot learning ability. Experimental results on the few-shot classification tasks testify its advantages. TensorFlow code will be released to reproduce our results.
AWLCO: All-Window Length Co-Occurrence

Analyzing patterns in a sequence of events has applications in text analysis, computer programming, and genomics research. In this paper, we consider the all-window-length analysis model which analyzes a sequence of events with respect to windows of all lengths. We study the exact co-occurrence counting problem for the all-window-length analysis model. Our first algorithm is an offline algorithm that counts all-window-length co-occurrences by performing multiple passes over a sequence and computing single-window-length co-occurrences. This algorithm has the time complexity O(n) for each window length and thus a total complexity of O(n²) and the space complexity O(|I|) for a sequence of size n and an itemset of size |I|. We propose AWLCO, an online algorithm that computes all-window-length co-occurrences in a single pass with the time complexity of O(n) and space complexity of O(√{n|I|}), assuming perfect hashing. Following this, we generalize our use case to patterns in which we propose an algorithm that computes all-window-length co-occurrence with time complexity O(n|I|), assuming perfect hashing, with an additional pre-processing step and space complexity O(√{n|I|}+|I|), plus the overhead of the Aho-Corasick algorithm [Aho and Corasick, 1975].
Vehicle Recognition using extensions of Pattern Descriptors

Abstract Vehicle identification and classification for still images are incredibly useful and can be extended to a range of traffic surveillance operations. Reliable and accurate recognition of vehicles is however a challenging issue due to changes in vehicle appearance and illumination difference in real time scene. In this paper, we present a simple and effective way of vehicle recognition technique based on vehicle’s local texture features extraction and classification. The local features are extracted individually using the Local Binary Pattern (LBP), Median Binary Pattern (MBP), Gradient directional pattern (GDP), and Local Arc Pattern (LAP) descriptors and feed into Support Vector Machine (SVM) for classification. We also focus on vehicle classification using various color spaces like RGB, HSV, YCbCr for the texture descriptors extraction. The primary focus is to observe the effect of colour information on vehicle classification efficiency across different colour spaces. Initially, experiments are conducted for the classification of gray-level vehicle images of five different classes from the CompCars dataset. Then experiments are extended to different color spaces for the same dataset for color texture classification. The integration of different colour details increases the efficiency of vehicle classification, according to the experimental results.
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation

This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing.We show that self-training, active learning and data augmentation techniques can improve the generalization ability of the model on the unlabeled target domain data without accessing source domain data.We also perform detailed ablation studies and error analyses for our time expression recognition systems to identify the source of the performance improvement and give constructive feedback on the temporal normalization annotation guidelines.
AI@EDGE: A Secure and Reusable Artificial Intelligence Platform for Edge Computing

Artificial Intelligence (AI) has become a major innovative force and a major pillar in the fourth industrial revolution. This trend has been acknowledged by the European Commission, who has pointed out how high-performance, intelligent, and secure networks are fundamental for the evolution of the multiservice Next Generation Internet (NGI). While great progress has been done in the accuracy and performance of AI-enabled platforms, their integration in autonomous decision-making and critical systems requires end-to-end quality assurance. AI@EDGE addresses these challenges harnessing the concept of “reusable, secure, and trustworthy AI for network automation”. To this end, AI@EDGE targets significant breakthroughs in two fields: (i) general-purpose frameworks for closed-loop network automation capable of supporting flexible and programmable pipelines for the creation, utilization, and adaptation of the secure, reusable, and trustworthy AI/ML models; and (ii) converged connect-compute platform for creating and managing resilient, elastic, and secure end-to-end slices supporting a diverse range of AI-enabled network applications. Cooperative perception for vehicular networks, secure, multi-stakeholder AI for Industrial Internet of Things, aerial infrastructure inspections, and in-flight entertainment are the uses cases targeted by AI@EDGE to maximise its commercial, societal, and environmental impact.
A framework for comprehensive fraud management using actuarial techniques

A framework for comprehensive fraud management using actuarial techniques - Fraud acts as a major deterrent to a company’s
Agile-LSTM: Acclimatizing Convolution Neural Network for Crowd Behaviour Analysis

Crowd behaviour analysis forms a central basis for having peaceful event organization, with a minimum calamitous situation in the place of public congregation. Conventionally, the practice of hand-crafted techniques, while dealing with crowd behaviour analysis, are unable to get rid of nonlinearity in the complex real-world video sequences because of severe occlusion, perspective variation and many more. In this paper, we propound Agile-LSTM framework by using the fusion of pre-trained neural network Resnoate18 and LSTM model that considers spatial and temporal feature extraction for analysing crowd behaviour in automated surveillance systems. The Agile-LSTM model is built using Python and trained on a Kaggle datasets and behaves datasets. Training results for Agile-LSTM convolutional neural network show the average accuracy reaches 90% is achieved. Thereby, this model helps in detecting any abnormal behaviour in an automated surveillance-based system.
Movie recommendation system using clustering mining with Python

Abstract The research we are pursuing here is about building a system which we will use and mine a movie review database using concepts of statistics like Euclidean Distance algorithm. This will help us determine similar review according to the ratings. What we plan on achieving through this approach is to analyze the reviews to suggest a suitable critic according to one’s preferences and one’s taste. We will do this according to the review we select and our algorithm which we use upon it. This algorithm will be the key to find those similar reviews. We are going to use Python programming to program this experiment. We have chosen to pursue this because of not the reviewers being incorrect or correct but merely different according to their preferences and their lean towards a certain genre and we know that a reviewer should also match the taste of the person who want to feel benefited of the review itself. So, we plan to make system that could help with that.
Optimisation of agricultural economic structure based on factor analysis and intelligent algorithms

In order to improve the effect of agricultural economic structure optimisation, this paper builds an agricultural economic structure optimisation analysis system based on factor analysis and intelligent algorithms, and uses a set of low-dimensional hidden factor vectors to describe the covariance matrix structure of high-dimensional observation vectors. Moreover, this paper combines the algorithm with the actual situation to obtain the optimisation analysis process of the agricultural economic structure. On this basis, this paper analyses the optimisation of agricultural economic structure in practice with a case analysis method. Moreover, this paper uses mathematical analysis to process agricultural economic structure data, and uses mathematical statistics to visualise the process of data processing. In addition, this paper combines the weighted average to perform parameter processing, combines with the comparative analysis method to obtain the agricultural economic results parameters, and analyses these parameters. Finally, on the basis of the above, this paper proposes the corresponding optimisation strategy of agricultural economic structure. The research results show that the method proposed in this paper has a certain effect.
Faster Algorithms for Bounded Tree Edit Distance.

Tree edit distance is a well-studied measure of dissimilarity between rooted trees with node labels. It can be computed in O(n³) time [Demaine, Mozes, Rossman, and Weimann, ICALP 2007], and fine-grained hardness results suggest that the weighted version of this problem cannot be solved in truly subcubic time unless the APSP conjecture is false [Bringmann, Gawrychowski, Mozes, and Weimann, SODA 2018].We consider the unweighted version of tree edit distance, where every insertion, deletion, or relabeling operation has unit cost. Given a parameter k as an upper bound on the distance, the previous fastest algorithm for this problem runs in O(nk³) time [Touzet, CPM 2005], which improves upon the cubic-time algorithm for k≪ n^{2/3}. In this paper, we give a faster algorithm taking O(nk² log n) time, improving both of the previous results for almost the full range of log n ≪ k≪ n/√{log n}.
Image reconstruction reveals the impact of aging on face perception.

Extensive work has demonstrated an age-related decline in face recognition, but the nature and the extent of aging-related alterations in face representations remain unclear. Here, we address these issues using an image reconstruction approach to reveal the content of visual representations. Healthy young and older adults provided similarity judgments for pairs of face images. Facial shape and surface features were subsequently derived and combined into image reconstructions of facial appearance. Both objective and experimental evaluations revealed that reconstructions were successful for every participant. Critically, shape and surface properties, such as eye shape and skin tone, were less accurately represented in older than young individuals. Yet, age-related differences in face representations, though significant, were less pronounced than those due to individual variability. Our results provide novel insights into age-related changes in visual perception and demonstrate the utility of image reconstruction to uncovering internal representations across a variety of populations. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Estrategias basadas en análisis de redes sociales para optimizar el clima de aprendizaje en la universidad

The aim of this study was to detect leaders among a group of university students based on their centrality scores and, from these scores, to distribute roles to help enhance learning climate. We understood that learning climate improved when the construction of networked knowledge was stimulated from the perspective of Computer-Supported Collaborative Learning. We conducted the educational experiment in a virtual environment. An online discussion forum was set up in a loo- sely structured format, and the students’ centrality scores were calculated from the social network they generated in the forum. Our findings show that student connectivity increased significantly and that several leadership styles were detected. Based on these leadership styles we designed strategies for optimizing learning climate in a self-regulated and stable way. Based on the type of centrality, we detected leaders in terms of their popularity, sociability, closeness to others, control of information flowing through the network, and influence. The novelty of this study resides in the incipient production of educational technology based on Social Network Analysis and, specifically, on the design of centrality-based strategies for optimizing climate in the university classroom.
A Hybrid Authentication Policy for Digital Image Processing in Big Data Platform

Big data term includes all types of structured and unstructured data in its database. The extraction of data from big data can be done through Map-Reduce, Hadoop and many more. Now a days, Digital image processing is needing its data security due to its use in widespread firms e.g. (institutions, corporate companies, marketing, streaming etc.) in the form of fingerprint, palmprint, face- recognition etc. The matching and compression process of images is not enough today to secure it. This paper proposes a hybrid authentication framework used in Big data for various types of image data. This framework also used a cryptographic approach with Local Binary Patterns Technique (LBP) image data to ensure better security.
Few‐shot action recognition using task‐adaptive parameters

Few-shot action recognition aims to recognise unseen actions given a few examples. Thus, this letter proposes a model named meta relation network (Meta RN) to address such problem. This model contains two parts: a MetaNet and a relation network. Relation network is utilised to extract video features and classify actions. A second-order pooling followed by power normalization is used for feature enhancement, and target videos are finally classified by exploring nonlinear distance relations. The MetaNet module is designed to model different task distributions and generate task-adaptive parameters for the embedding layer of the relation network in different tasks. Experimental results on two public action recognition datasets demonstrate that the network achieves higher accuracies than several state-of-the-art approaches.
Measuring driver cognitive distraction through lips and eyebrows

&lt;span&gt;Cognitive distraction is one of the several contributory factors in road accidents. A number of cognitive distraction detection methods have been developed. One of the most popular methods is based on physiological measurement. Head orientation, gaze rotation, blinking and pupil diameter are among popular physiological parameters that are measured for driver cognitive distraction. In this paper, lips and eyebrows are studied. These new features on human facial expression are obvious and can be easily measured when a person is in cognitive distraction. There are several types of movement on lips and eyebrows that can be captured to indicate cognitive distraction. Correlation and classification techniques are used in this paper for performance measurement and comparison. Real time driving experiment was setup and faceAPI was installed in the car to capture driver’s facial expression. Linear regression, support vector machine (SVM), static Bayesian network (SBN) and logistic regression (LR) are used in this study. Results showed that lips and eyebrows are strongly correlated and have a significant role in improving cognitive distraction detection. Dynamic Bayesian network (DBN) with different confidence of levels was also used in this study to classify whether a driver is distracted or not.&lt;/span&gt;
Boosting Offline Reinforcement Learning with Residual Generative Modeling

Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration.Current offline RL research includes: 1) generative modeling, i.e., approximating a policy using fixed data; and 2) learning the state-action value function. While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected. In this paper, we analyze the error in generative modeling. We propose AQL (action-conditioned Q-learning), a residual generative model to reduce policy approximation error for offline RL. We show that our method can learn more accurate policy approximations in different benchmark datasets. In addition, we show that the proposed offline RL method can learn more competitive AI agents in complex control tasks under the multiplayer online battle arena (MOBA) game, Honor of Kings.
Reward-Constrained Behavior Cloning

Deep reinforcement learning (RL) has demonstrated success in challenging decision-making/control tasks. However, RL methods, which solve tasks through maximizing the expected reward, may generate undesirable behaviors due to inferior local convergence or incompetent reward design. These undesirable behaviors of agents may not reduce the total reward but destroy the user experience of the application. For example, in the autonomous driving task, the policy actuated by speed reward behaves much more sudden brakes while human drivers generally don’t do that. To overcome this problem, we present a novel method named Reward-Constrained Behavior Cloning (RCBC) which synthesizes imitation learning and constrained reinforcement learning. RCBC leverages human demonstrations to induce desirable or human-like behaviors and employs lower-bound reward constraints for policy optimization to maximize the expected reward. Empirical results on popular benchmark environments show that RCBC learns signiﬁcantly more human-desired policies with performance guarantees which meet the lower-bound reward constraints while performing better than or as well as baseline methods in terms of reward maximization.
Fairness-Guaranteed Transcoding Task Assignment for Viewer-Assisted Crowdsourced Livecast Services

Recent years have witnessed an outstanding increase in popularity of Crowdsourced Livecast Services (CLS), which is the latest trend in social media. In CLS, transcoding enormous video contents from massive broadcasters and providing high-quality CLS for global viewers with heterogeneous devices are computation-intensive as well as time-consuming. There are some schemes that design viewer-assisted transcoding scheme, but it is challenging to achieve an efficient and fair task assignment due to the dynamic of computing and communication resources. This paper introduces a viewer-assisted CLS framework and focuses on proposing an innovative fairness-guaranteed task assignment scheme, which is a key challenge in this context. Considering the dynamic nature of viewers’ computing and communication resources and stability, a dynamic programming problem with fairness and QoS constraints is formulated. To solve the problem, we devise a Fair Bandit (FB) algorithm based on the Combinatorial Multi-Armed Bandit (CMAB). Finally, the effectiveness of proposed scheme is demonstrated by trace-driven simulations.
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks

Gradient estimation and vector space projection have been studied as two distinct topics. We aim to bridge the gap between the two by investigating how to efficiently estimate gradient based on a projected low-dimensional space. We first provide lower and upper bounds for gradient estimation under both linear and nonlinear projections, and outline checkable sufficient conditions under which one is better than the other. Moreover, we analyze the query complexity for the projection-based gradient estimation and present a sufficient condition for query-efficient estimators. Built upon our theoretic analysis, we propose a novel query-efficient Nonlinear Gradient Projection-based Boundary Blackbox Attack (NonLinear-BA). We conduct extensive experiments on four image datasets: ImageNet, CelebA, CIFAR-10, and MNIST, and show the superiority of the proposed methods compared with the state-of-the-art baselines. In particular, we show that the projection-based boundary blackbox attacks are able to achieve much smaller magnitude of perturbations with 100% attack success rate based on efficient queries. Both linear and nonlinear projections demonstrate their advantages under different conditions. We also evaluate NonLinear-BA against the commercial online API MEGVII Face++, and demonstrate the high blackbox attack performance both quantitatively and qualitatively. The code is publicly available at this https URL.
Hybrid Denoising Based Correlation Power Analysis for AES

The correlation power analysis is greatly affected by the noise in the power data. To address this issue, this paper proposes a hybrid denoising-based correlation power analysis method. Power consumption data of AES-128 algorithm in DPAContest dataset are used. The data is first preprocessed by Butterworth low-pass filter and then fed to Empirical Mode Decomposition for decomposition. Wavelet threshold denoising method is used to further remove noise in the high frequency part. By attacking the filtered data, the hybrid denoising-based correlative power analysis method is compared to the correlative power analysis with only Butterworth filter and the common correlative power analysis method without filtering. Experimental results show that the hybrid denoising method has better performance.