Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Framework for Classification of Water Quality in Social Media Data

This paper describes the method proposed by team FAST-NU-DS, for the task of WaterMM: Water Quality in Social Multimedia at MediaEval, 2021. The task aims to analyze water security, safety, and quality of water and build a classifier that differentiates whether the tweet is discussing water quality issues. The task includes a dataset in the form of tweets containing the tweet’s text, their meta-data, and a few tweets also contain images. The proposed method has performed pre-processing steps on the text and tags of the dataset and applied Bidirectional Encoder Representations from Transformers (BERT). The proposed method has applied Visual Geometry Group (VGG16) pre-trained on the ImageNet dataset for the binary classification of images. The proposed method has achieved a 0.31 F1 score for text-only content. Moreover, the combination of text and images provided a 0.24 F1 score.
Disaster based Visual Sentiment Analysis using Deep Learning

In caseof a disaster, a largenumber of relevant and irrelevant images are propagated through social networks. In this case, the sentiment identification of such disasters is important to speed up relief work in the affected region. This paper describes the contribution of FAST-NU-DS team for the Visual Sentiment Analysis: A Natural Disaster MediaEval Use-case [4] held at MediaEval 2021. Various pre-trained deep learning-based models for the single-label classification and multi-label classification tasks have been used for feature extraction and classification. Data augmentation techniques to over-sample minority classes were used to deal with the inherent imbalance nature of the dataset. For a single-label and multi-label classification of tasks, VGG16 proved to be more useful than ResNet50. In this work, we achieved a 0.65 weighted F1 score for the first single label classification subtask and 0.54 and 0.41 weighted-F1 scores for the second and third multi-label subtasks, respectively.
The BLAS API of BLASFEO

Basic Linear Algebra Subroutines For Embedded Optimization (BLASFEO) is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization and other applications targeting relatively small matrices. BLASFEO defines an application programming interface (API) which uses a packed matrix format as its native format. This format is analogous to the internal memory buffers of optimized BLAS, but it is exposed to the user and it removes the packing cost from the routine call. For matrices fitting in cache, BLASFEO outperforms optimized BLAS implementations, both open source and proprietary. This article investigates the addition of a standard BLAS API to the BLASFEO framework, and proposes an implementation switching between two or more algorithms optimized for different matrix sizes. Thanks to the modular assembly framework in BLASFEO, tailored linear algebra kernels with mixed column- and panel-major arguments are easily developed. This BLAS API has lower performance than the BLASFEO API, but it nonetheless outperforms optimized BLAS and especially LAPACK libraries for matrices fitting in cache. Therefore, it can boost a wide range of applications, where standard BLAS and LAPACK libraries are employed and the matrix size is moderate. In particular, this article investigates the benefits in scientific programming languages such as Octave, SciPy, and Julia.
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet

Local divisive normalization provides a phenomenological description of many nonlinear response properties of neurons across visual cortical areas. To gain insight into the utility of this operation, we studied the effects on AlexNet of a local divisive normalization between features, with learned parameters. Developing features were arranged in a line topology, with the inﬂuence between features determined by an exponential function of the distance between them. We compared an AlexNet model with no normalization or with canonical normalizations (Batch, Group, Layer) to the same models with divisive normalization added. Divisive normalization always improved performance for models with batch or group or no normalization, generally by 1-2 percentage points, on both the CIFAR-100 and ImageNet databases. To gain insight into mechanisms underlying the improved performance, we examined several aspects of network representations. In the early layers both canonical and divisive normalizations reduced manifold capacities and increased average dimension of the individual categorical manifolds. In later layers the capacity was higher and manifold dimension lower for models roughly in order of their performance improvement. Examining the sparsity of activations across a given layer, divisive normalization layers increased sparsity, while the canonical normalization layers decreased it. Nonetheless, in the ﬁnal layer, the sparseness of activity increased in the order of no normalization, divisive, combined, and canonical. We also investigated how the receptive ﬁelds (RFs) in the ﬁrst convolutional layer (where RFs are most interpretable) change with normalization. Divisive normalization enhanced RF Fourier power at low wavelengths,
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game

The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience. Yet, policy gradient methods with self-play suffer convergence problems to a Nash Equilibrium (NE) in multi-agent situations. Counterfactual regret minimization (CFR) has a convergence guarantee to a NE in 2-player zero-sum games, but it usually needs domain-speciﬁc abstractions to deal with large-scale games. Inheriting merits from both methods, in this paper we extend the actor-critic algorithm framework in deep reinforcement learning to tackle a large-scale 2-player zero-sum imperfect-information game, 1-on-1 Mahjong, whose information set size and game length are much larger than poker. The proposed algorithm, named Actor-Critic Hedge (ACH), modiﬁes the policy optimization objective from originally maximizing the discounted returns to minimizing a type of weighted cumulative counterfactual regret. This modiﬁcation is achieved by approximating the regret via a deep neural network and minimizing the regret via generating self-play policies using Hedge. ACH is theoretically justiﬁed as it is derived from a neural-based weighted CFR, for which we prove the convergence to a NE under certain conditions. Experimental results on the proposed 1-on-1 Mahjong benchmark and benchmarks from the literature demonstrate that ACH outperforms related state-of-the-art methods. Also, the agent obtained
Combining Multiple Deep-learning-based Image Features for Visual Sentiment Analysis

This paper presents our team’s (IDT-ITI-CERTH) proposed method for the Visual Sentiment Analysis task of the Mediaeval 2021 benchmarking activity. Visual sentiment analysis is a challenging task as it involves a high level of subjectivity. The most recent works are based on deep convolutional neural networks, and exploit transfer learning from other image classification tasks. However, transferring knowledge from tasks other than image classification has not been investigated in the literature. Motivated by this, in our approach we examine the potential of transferring knowledge from several pre-trained networks, some of which are out-of-domain. We concatenate these diverse feature vectors and construct an image representation that is used to train a classifier for each of the three subtasks of this Mediaeval task. Due to a bug in the original submission file, the official scores we got are 0.595, 0.479 and 0.380 for subtasks 1,2 and 3 respectively.
Pareto Policy Adaptation

We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for Pareto optimality, we are able to design a simple policy gradient al-gorithm that approximates the Pareto front and infers the unknown preferences. Our method relies on a projected gradient descent solver that identifies common ascent directions for all objectives. Leveraging the solution of that solver, we introduce Pareto Policy Adaptation ( PPA ), a loss function that adapts the policy to be optimal with respect to any distribution over preferences. PPA uses implicit differentiation to back-propagate the loss gradient bypassing the operations of the projected gradient descent solver. Our approach is straightforward, easy to implement and can be used with all existing policy gradient and actor-critic methods. We evaluate our method in a series of reinforcement learning tasks.
Predictive Uncertainty Masks from Deep Ensembles in Automated Polyp Segmentation

This paper presents the submission of team F-HOST for the Medico: Transparency in Medical Image Segmentation task held at Media-Eval 2021. We propose a U-Net-based ensemble model for solving the automatic polyp segmentation task and interpret the predictions using a specific method for obtaining uncertainty. Our predicted segmentation masks show a mean Dice score of 45 . 01% based on the test data. The corresponding uncertainties show systematic errors towards the training data, which indicates overfitting.
Accountability and Empathy by Design

Bystander intervention can reduce the amount of cyberbullying victimization on social media, but bystanders often fail to act. Limited accountability for their behavior and a lack of empathy for the victim are frequently cited as reasons for why bystanders do not act against cyberbullying. We developed design interventions that aimed to increase accountability and empathy among bystanders. In Study 1, participants were experimentally exposed to three social media posts with different types of empathy nudges. Empathy nudges embedded into social media posts displayed the potential to motivate empathy. In Study 2, participants took part in a 3-day experiment that simulated a social media experience. Results suggested that increased social transparency on social media promoted accountability through heightened self-presentation concerns, but empathy nudges did not encourage greater bystander empathy. Both accountability and empathy predicted bystander intervention, but the types of bystander actions promoted by each mechanism differed. We consider how these results contribute to theories of bystander behavior and designing social media to promote prosocial behaviors.
The Internet's Hidden Rules

Norms are central to how online communities are governed. Yet, norms are also emergent, arise from interaction, and can vary significantly between communities---making them challenging to study at scale. In this paper, we study community norms on Reddit in a large-scale, empirical manner. Via 2.8M comments removed by moderators of 100 top subreddits over 10 months, we use both computational and qualitative methods to identify three types of norms: macro norms that are universal to most parts of Reddit; meso norms that are shared across certain groups of subreddits; and micro norms that are specific to individual, relatively unique subreddits. Given the size of Reddit's user base---and the wide range of topics covered by different subreddits---we argue this represents the first large-scale census of the norms in broader internet culture. In other words, these findings shed light on what Reddit values, and how widely-held those values are. We conclude by discussing implications for the design of new and existing online communities.
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning

Modular Reinforcement Learning, where the agent is assumed to be morphologically structured as a graph, for example composed of limbs and joints, aims to learn a policy that is transferable to a structurally similar but different agent. Compared to traditional Multi-Task Reinforcement Learning, this promising approach allows us to cope with inhomogeneous tasks where the state and action space dimensions differ across tasks. Graph Neural Networks are a natural model for representing the pertinent policies, but a recent work has shown that their multihop message passing mechanism is not ideal for conveying important information to other modules and thus a transformer model without morphological information was proposed. In this work, we argue that the morphological information is still very useful and propose a transformer policy model that effectively encodes such information. Specifically, we encode the morphological information in terms of the traversal-based positional embedding and the graph-based relational embedding. We empirically show that the morphological information is crucial for modular reinforcement learning, substantially outperforming prior state-of-the-art methods on multi-task learning as well as transfer learning settings with different state and action space dimensions.
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs

Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) based method to learn scenario representation for a class of SIP instances. Specifically, we design a graph convolutional network based encoder to embed each scenario with the deterministic part of its instance (i.e. context) into a low-dimensional latent space, from which a decoder reconstructs the scenario from its latent representation conditioned on the context. Such a design effectively captures the dependencies of the scenarios on their corresponding instances. We apply the trained encoder to two tasks in typical SIP solving, i.e. scenario reduction and objective prediction. Experiments on two graph-based SIPs show that the learned representation significantly boosts the solving performance to attain high-quality solutions in short computational time, and generalizes fairly well to problems of larger sizes or with more scenarios.
Improving the Accuracy of Learning Example Weights for Imbalance Classification

To solve the imbalanced classiﬁcation, methods of weighting examples have been proposed. Recent work has studied to assign adaptive weights to training examples through learning mechanisms. Speciﬁcally, similar to classiﬁcation models, the weights are regarded as parameters that need to be learned. However, the algorithms in recent work use local information to approximately optimize the weights, which may lead to inaccurate learning of the weights. In this work, we ﬁrst propose a novel mechanism of learning with a constraint, which can accurately train the weights and model. Then, we propose a combined method of our learning mechanism and the existing work, which can promote each other to perform better. Our method can be applied to any type of deep network model. Experiments show that compared with state-of-the-art algorithms, our method has signiﬁcant improvement in varieties of settings, including text and image classiﬁ-cation over different imbalance ratios, binary and multi-class classiﬁcation.
Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes

In this work we theoretically contribute to neural network approximation by pro-viding a novel tropical geometrical viewpoint to structured neural network compression. In particular, we show that the approximation error between two neural networks with ReLU activations and one hidden layer depends on the Hausdorff distance of the tropical zonotopes of the networks. This theorem comes as a ﬁrst step towards a purely geometrical interpretation of neural network approximation. Based on this theoretical contribution, we propose geometrical methods that employ the K-means algorithm to compress the fully connected parts of ReLU activated deep neural networks. We analyze the error bounds of our algorithms theoretically based on our approximation theorem and evaluate them empirically on neural network compression. Our experiments follow a proof-of-concept strategy and indicate that our geometrical tools achieve improved performance over relevant tropical geometry techniques and can be competitive against non-tropical methods.
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search

One-shot Neural Architecture Search (NAS) usually constructs an overparameterized network, which we call a supernet, and typically adopts sharing parameters among the sub-models to improve computational efficiency. Oneshot NAS often repeatedly samples sub-models from the supernet and trains them to optimize the shared parameters. However, this training strategy suffers from multi-model forgetting. Training a sampled sub-model overrides the previous knowledge learned by the other sub-models, resulting in an unfair performance evaluation between the sub-models. We propose Supernet with Unbiased MetaFeatures for Neural Architecture Search (SUMNAS), a supernet learning strategy based on meta-learning to tackle the knowledge forgetting issue. During the training phase, we explicitly address the multi-model forgetting problem and help the supernet learn unbiased meta-features, independent from the sampled submodels. Once training is over, sub-models can be instantly compared to get the overall ranking or the best sub-model. Our evaluation on the NAS-Bench-201 and MobileNet-based search space demonstrate that SUMNAS shows improved ranking ability and finds architectures whose performance is on par with existing state-of-the-art NAS algorithms.
A stochastic techno-economic comparison of alternative jet fuel production pathways

This study quantifies and compares the costs of production for six alternative jet fuel pathways using consistent financial and technical assumptions. Uncertainty was propagated through the analysis using Monte Carlo simulations. The six processes assessed were hydroprocessed ester and fatty acids (HEFA) using soybean oil, yellow grease, and tallow; advanced fermentation (AF) using corn grain, sugarcane, and herbaceous biomass; conventional gasification and Fischer-Tropsch (FT) using municipal solid waste; aqueous phase processing (APP) using woody biomass; hydrothermal liquefaction (HTL) using woody biomass; and fast pyrolysis and hydroprocessing (FPH) using corn stover. The results indicate that none of the six processes would be profitable in the absence of government incentives, with HEFA using yellow grease, HEFA using tallow, and FT revealing the lowest mean jet fuel prices at $0.91/liter ($0.66/liter to $1.24/liter), $1.06/liter ($0.79/liter to $1.42/liter), and $1.15/liter ($0.95/liter to $1.39/liter), respectively. The highest mean NPV was the NPV calculated for HEFA yellow grease with a mean value (in $B) of -0.112 (95% range of -0.412 to 0.179), followed by HEFA tallow with -0.202 (-0.517 to 0.100) and FT with -0.210 (-0.424 to 0.033). This study also quantifies plant performance in the United States with a policy analysis. The alternative fuel production models were used to examine the economic viability of each pathway under a variety of existing and potential regulatory scenarios. Results indicate that some pathways could achieve positive NPV with relatively high likelihood under existing policy supports, with HEFA and FPH revealing the highest probability of positive NPV at 94.9% and 99.7%, respectively, in the best-case scenario. Thesis Supervisor: Steven R. H. Barrett Title: Leonardo Associate Professor of Aeronautics and Astronautics
Investigation Efficiency and Microcontroller-Based Energy Flow Control for Wind-Solar-Fuel Cell Hybrid Energy Generation System with Battery Storage

In this proposal, microcontroller-based energy flow control was designed in order to effectively and efficiently enable the use of energy sources in a hybrid energy generation system including wind, solar, and hydrogen energy. It was assumed that the hybrid energy generation system is dynamic during the design of the microcontroller-based energy flow control. A wind–solar energy generation system was determined as the base load power plant. Depending on the demand, the battery group and fuel cell were activated effectively. If an energy surplus occurred, it was stored in battery groups and transformed into hydrogen energy via a hydrogen generator simultaneously. In addition to providing energy sustainability, a constant active status of the energy storage group was prevented and the physical life of the group was prolonged by means of the microcontroller-based control system. If consumer demand could not be met by the main energy sources including wind and solar energy, the battery groups and fuel cell were activated and provided the energy sustainability. After a certain level of charge was reached in the battery group, it was deactivated via the control system in order to prevent unnecessary use of energy. By means of the microcontroller-based control system, the usage of energy generated with the hybrid energy generation system was analysed according to its efficiency.
Perceived exertion, postural control, and muscle recruitment in three different quadruped exercises performed by healthy women

Although quadruped exercises (QE) have been a part of rehabilitation and sports programs, there is no clarity on how these exercises challenge the musculoskeletal system. Therefore, this cross-sectional study investigated the perceived exertion, postural demands, and muscle recruitment profiles imposed by three QE postures. Surface electromyographic (sEMG) signals were recorded from transverse abdominis, longissimus dorsi, multifidus, and iliocostalis lumborum from 30 sedentary healthy women, bilaterally. They performed the classic quadruped exercise (CQ), a variation with shoulder flexion (FQ), and the homolateral quadruped (HQ). Borg scores (BS) and the center of pressure (CoP) from the palmar statokinesiogram were also recorded. Surface EMG signals were normalized using the myoelectric activity recorded from two other postures while performing isometric voluntary contractions (IVC). Results were analyzed using one- (CoP) and three-way (sEMG data) ANOVA with Bonferroni post hoc tests (α = 0.05). The Borg scale was analyzed using the Friedman test. The CQ provided lower BS and CoP than HQ (p < 0.05), followed by a higher sEMG activity (∼51% of IVC) than FQ (∼47% of IVC; p = 0.53) and HQ (∼44% of IVC; p = 0.01). In turn, HQ provided greater BS (p > 0.05) than CQ and FQ. The results suggested that the HQ was the most challenging exercise regarding CoP and BS, although CQ presented a higher symmetrical sEMG activity. Since QE are often prescribed in exercise programs, specific knowledge of the characteristics of each QE makes prescribing safer and more efficient.