Reviewerly/exHarmony huggingface dataset:
A 2D Convolutional Neural Network Approach for Human Action Recognition

Nowadays, deep neural networks are widely used for human action recognition (HAR) due to their ability to operate directly on the raw video inputs by extracting both the spatial and temporal information. Although the 3D convolutional neural networks as deep models have achieved superior performance, they remain computational expensive. In this paper we propose a 2D-CNN approach that learns robust feature representation from temporal information embedded into the motion history images of action videos. The proposed approach is simple and reduces the computational complexity imposed by the 3D-CNN approaches. The KTH database is used to validate our approach and the achieved results are compared favorably against the handcrafted state-of-the-art methods.
Groupwise Multimodal Image Registration Using Joint Total Variation

In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv.
DeepSinger: Singing Voice Synthesis with Data Mined From the Web

In this paper, we develop DeepSinger, a multi-lingual multi-singer singing voice synthesis (SVS) system, which is built from scratch using singing training data mined from music websites. The pipeline of DeepSinger consists of several steps, including data crawling, singing and accompaniment separation, lyrics-to-singing alignment, data filtration, and singing modeling. Specifically, we design a lyrics-to-singing alignment model to automatically extract the duration of each phoneme in lyrics starting from coarse-grained sentence level to fine-grained phoneme level, and further design a multi-lingual multi-singer singing model based on a feed-forward Transformer to directly generate linear-spectrograms from lyrics, and synthesize voices using Griffin-Lim. DeepSinger has several advantages over previous SVS systems: 1) to the best of our knowledge, it is the first SVS system that directly mines training data from music websites, 2) the lyrics-to-singing alignment model further avoids any human efforts for alignment labeling and greatly reduces labeling cost, 3) the singing model based on a feed-forward Transformer is simple and efficient, by removing the complicated acoustic feature modeling in parametric synthesis and leveraging a reference encoder to capture the timbre of a singer from noisy singing data, and 4) it can synthesize singing voices in multiple languages and multiple singers. We evaluate DeepSinger on our mined singing dataset that consists of about 92 hours data from 89 singers on three languages (Chinese, Cantonese and English). The results demonstrate that with the singing data purely mined from the Web, DeepSinger can synthesize high-quality singing voices in terms of both pitch accuracy and voice naturalness (footnote: Our audio samples are shown in https://speechresearch.github.io/deepsinger/.)
Semantically Plausible and Diverse 3D Human Motion Prediction

We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using Conditional Variational Autoencoders (CVAEs). Existing approaches to generate diverse motions either fail to capture the diversity in human motion or fail to generate diverse but semantically plausible continuations of an observed motion. In this paper, we address both of these problems by developing a new variational framework that accounts for both diversity and semantic of the generated future motion. Unlike existing approaches that sampling the latent variable is independent of the conditioning, our approach conditions the sampling process of the latent variable on the representation of the past observation, thus encouraging it to carry relevant information. Our experiments demonstrate that our approach not only yields motions of higher quality while retaining diversity, but also generates motions that preserve semantic information contained in the observed 3D pose sequence.
Fuzzy Metaheuristics: A State-of-the-Art Review

A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies for developing heuristic optimization algorithms, reaching the optimum solution in the solution space more quickly by using efficient searches in a high-level work environment. There are various metaheuristics algorithms in the literature and the use of these algorithms for many problems in different areas is increasing rapidly. However, because the problems addressed are complex and uncertain, more effective and reliable results are needed for practitioners. In this context, fuzzy sets that better express uncertainties and reduce complexity can be successfully used with metaheuristic algorithms to achieve more concrete and realistic results. However, there isn’t a guiding source in the literature for those who want to research this topic. Therefore, this study aims to guide researchers on fuzzy-based metaheuristic algorithm applications by presenting literature research. For this aim, a large number of papers implementing fuzzy-based metaheuristic algorithms have been summarized with graphical figures by analyzing with respect to some characteristics such as subject area, published journal, publication year, and source country.
Prevalence and Success of Article Purchasing

Librarians from three Kentucky public universities conducted a survey on the prevalence and success of article purchasing programs. They found that a majority of academic libraries offer article purchasing to reduce costs and expand access to resources, and most of these institutions believe their article purchasing program is successful. Libraries that spent more on article purchasing and libraries that were motivated by a desire to increase access to articles reported higher levels of program success.
Learning to plan with uncertain topological maps

We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure. Whereas classical symbolic algorithms achieve optimal results on noise-less topologies, or optimal results in a probabilistic sense on graphs with probabilistic structure, we aim to show that machine learning can overcome missing information in the graph by taking into account rich high-dimensional node features, for instance visual information available at each location of the map. Compared to purely learned neural white box algorithms, we structure our neural model with an inductive bias for dynamic programming based shortest path algorithms, and we show that a particular parameterization of our neural model corresponds to the Bellman-Ford algorithm. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning.
Emojis or Not? How Emotional Vividness Influences Brand Attitude in Social Media

Social media has become a major platform for brand-customer interactions, and nonverbal cues such as emojis are widely used in text-based communication. Previous research suggests that emojis are perceived inappropriate and childish in work-related contexts, but it remains unknown whether these nonverbal cues would have a similarly negative effect when brands interact with customers in social media, or what the mechanisms underlying their possible effect are. Through a laboratory experiments this paper finds that more vivid emotional expressions in the form of emojis enhance consumers' brand attitude, and this positive effect is mediated by processing fluency and perceived persuasive intent. The findings have both theoretical contributions and practical implications for brand managers.
Marginal loss and exclusion loss for partially supervised multi-organ segmentation

Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs. In this paper, we investigate how to learn a single multi-organ segmentation network from a union of such datasets. To this end, we propose two types of novel loss function, particularly designed for this scenario: (i) marginal loss and (ii) exclusion loss. Because the background label for a partially labeled image is, in fact, a `merged' label of all unlabelled organs and `true' background (in the sense of full labels), the probability of this `merged' background label is a marginal probability, summing the relevant probabilities before merging. This marginal probability can be plugged into any existing loss function (such as cross entropy loss, Dice loss, etc.) to form a marginal loss. Leveraging the fact that the organs are non-overlapping, we propose the exclusion loss to gauge the dissimilarity between labeled organs and the estimated segmentation of unlabelled organs. Experiments on a union of five benchmark datasets in multi-organ segmentation of liver, spleen, left and right kidneys, and pancreas demonstrate that using our newly proposed loss functions brings a conspicuous performance improvement for state-of-the-art methods without introducing any extra computation.
Learning Sparse Neural Networks for Better Generalization

Deep neural networks perform well on test data when they are highly overparameterized, which, however, also leads to large cost to train and deploy them. As a leading approach to address this problem, sparse neural networks have been widely used to significantly reduce the size of networks, making them more efficient during training and deployment, without compromising performance. Recently, sparse neural networks, either compressed from a pre-trained model or obtained by training from scratch, have been observed to be able to generalize as well as or even better than their dense counterparts. However, conventional techniques to find well fitted sparse sub-networks are expensive and the mechanisms underlying this phenomenon are far from clear. To tackle these problems, this Ph.D. research aims to study the generalization of sparse neural networks, and to propose more efficient approaches that can yield sparse neural networks with generalization bounds.
The Improvement of V-Shaped Transfer Function of Binary Particle Swarm Optimization

Binary Particle Swarm Optimization (BPSO) is a swarm intelligence to optimize discrete space problems by extending the Particle Swarm Optimization. Its transfer function is the key element of BPSO. In this paper, a new V-shaped transfer function with a parameter k has been proposed. The parameter k was used to control the opening size of the transfer function. At first, the setting of the parameter k has been obtained by the experiments, and then the new V-shaped transfer with the optimal k value is compared with the other kinds of the V-shaped transfer functions by the experiment of feature selection. The results have indicated that the new V-shaped transfer function improved the performance of Binary Particle Swarm Optimization.
Human-Autonomy Teaming and Explainable AI Capabilities in RTS Games

Real-time strategy games often times mimic the appearance and feel of military-like command and control systems. Artificial intelligence and machine learning research enjoys utilizing the environments produced by these games and are often focused on creating intelligent agents to beat them or accomplish high scores. Instead of creating these agents, or bots, to beat real-time strategy games, this work instead focuses on creating machine learning-driven agents to work with human players. However, due to the advancements in the deep learning field, machine learning models have become harder to understand, and are called ‘black-box models’ due to the inability to see the inner workings of such algorithms. To remedy this, we describe how we take human-autonomy teaming and explainable artificial intelligence techniques to shed light and provide inside for players to understand recommendations by the system.
The Program Generation in the Software Package QextNewEdition

This paper is devoted to the program Generation which is a self-containing console application for classification of linear codes. It can be used for codes over fields with $$q<8$$ elements and with wide-range parameters. The base of the implemented algorithm is the concept of canonical augmentation.
Prediction of Photovoltaic Power Using Nature-Inspired Computing

Prediction of photovoltaic (PV) energy is an important task. It allows grid operators to plan production of energy in order to secure stability of electrical grid. In this work we focus on improving prediction of PV energy using nature-inspired algorithms for optimization of Support Vector Regression (SVR) models. We propose method, which uses different models optimized for various types of weather in order to achieve higher overall accuracy compared to single optimized model. Each sample is classified by Multi-Layer Perceptron (MLP) into some weather class and then model is trained for each weather class. Our method achieved slightly better results compared to single optimized model.
Auxiliary Tasks Speed Up Learning PointGoal Navigation

PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment. Wijmans et al. showed that this task is solvable but their method is computationally prohibitive, requiring 2.5 billion frames and 180 GPU-days. In this work, we develop a method to significantly increase sample and time efficiency in learning PointNav using self-supervised auxiliary tasks (e.g. predicting the action taken between two egocentric observations, predicting the distance between two observations from a trajectory,etc.).We find that naively combining multiple auxiliary tasks improves sample efficiency,but only provides marginal gains beyond a point. To overcome this, we use attention to combine representations learnt from individual auxiliary tasks. Our best agent is 5.5x faster to reach the performance of the previous state-of-the-art, DD-PPO, at 40M frames, and improves on DD-PPO's performance at 40M frames by 0.16 SPL. Our code is publicly available at this https URL.
Traffic accident prediction based on Markov chain cloud model

Abstract A dynamic prediction model based on Markov chain and cloud model is established to predict the volume of road traffic accidents which is under the guidance of stochastic process and cloud theory referring to the characters of the road traffic accidents and time series data. First of all, we establish the evaluation model based on cloud model, and get the relative error range of the observed value and the predicted value. Then we can use Markov chain to correct the relative error. The result shows that this method balances both randomicity and fuzziness and has higher accuracy. Therefore, it can be used for analysing the trend of road traffic accidents in different traffic conditions and provide evidence for safety early warning and corresponding accident prevention countermeasures formulation in relative road segments.
Knowledge redundancy approach to reduce size in association rules

Association Rules Mining is one of the most studied and widely applied fields in Data Mining. However, the discovered models usually result in a very large set of rules; so the analysis capability, from the user point of view, is diminishing. Hence, it is difficult to use the found model in order to assist decision-making process. The previous handicap is heightened in presence of redundant rules in the final set. In this work a new definition of redundancy in association rules is proposed, based on user prior knowledge. A post-processing method is developed to eliminate this kind of redundancy, using association rules known by the user. Our proposal allows to find more compact models of association rules to ease its use in the decision-making process. The developed experiments have shown reduction levels that exceed 90 percent of all generated rules, using prior knowledge always below ten percent. So, our method improves the efficiency of association rules mining and the exploitation of discovered association rules.
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations

A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate and adapt Cross-View Training (CVT) as a semi-supervised learning technique, allowing us to utilize unlabeled data to improve the model representations. In the Thai sentence segmentation experiments, our model reduced the relative error by 7.4% and 10.5% compared with the baseline models on the Orchid and UGWC datasets, respectively. We also applied our model to the task of pronunciation recovery on the IWSLT English dataset. Our model outperformed the prior sequence tagging models, achieving a relative error reduction of 2.5%. Ablation studies revealed that utilizing n-gram presentations was the main contributing factor for Thai, while the semi-supervised training helped the most for English.
Stakeholder perceptions of public good provision from agriculture and implications for governance mechanism design

Agriculture provides many public goods; however the costs and benefits of these are rarely well distributed. Maintaining public good provision often relies on external governance mechanisms, in turn reliant on the existing socio-ecological system. With two groups of stakeholders (practitioners and academics) we created cognitive maps of socio-ecological systems linking agriculture, public goods, and governance mechanisms in north-east Scotland. Fuzzy cognitive mapping was used to explore stakeholders’ perceptions and experiences, and to assess alternative governance options for the local socio-ecological context. We find agreement for perceptions of the system between stakeholders, but differences in each group’s focus. Models predicted little change in the provision of public goods from agriculture in relation to different governance mechanisms. We find that stakeholder participation can aid understanding of the impacts of proposed governance changes at the local level, improving comprehension of stakeholder perception of impacts and understanding of stakeholders’ reactions to particular governance mechanisms.
Noise-adaptive test of quantum correlations with quasiprobability functions

We introduce a method for testing quantum correlations in terms of quasiprobability functions in the presence of noise. We analyze the effects of measurement imperfection and thermal environment on quantum correlations and show that their noise effects can be well encapsulated into the change of the order parameter of the generalized quasiprobability function. We then formulate a noise-adaptive entanglement witness in the form of a Bell-type inequality by using the generalized quasiprobability function. Remarkably, it allows us to observe quantum correlations under severe noise. Our method provides a useful tool to test quantum correlations in near-term noisy quantum processors with continuous-variable systems.
On the Behavior of Fuzzy Grey Cognitive Maps

Fuzzy Cognitive Maps (FCMs) are recurrent neural networks made up of well-defined neurons and causal relations. Fuzzy Grey Cognitive Maps (FGCMs) are an extension of FCMs, intended to surpass the intrinsic uncertainties modeling real-world problems by means of Grey theory. Despite the rising number of studies about FGCM-based models, little has been investigated with regard to the convergence of such networks. In this paper, we build a mathematical basis to uncover the behavior FGCM-based models equipped with transfer F-functions. To do so, we propose sufficient conditions for the existence and unicity of fixed-point attractors. Also, the results reported in the literature on the convergence of FGCMs, are compared with ours. Furthermore, we elucidate the reach and depth of our findings, especially and not exclusive to the prediction of FCMs' behavior.
A Refuse Management System and Blockchain: A Practical View

The refuse management in Malaysia had experienced high degradation, and this had caused a massive loss of ecosystem services due to significant pollution.Added with low community engagement to clean the environment, a lot of refuse management system being developed through apps is left unattended by them.One of the significant drawbacks of today refuses management system are still lacking in terms of the identity protocol for identifying individual especially those who have signed up for the app's usage.Apart from that, leakage of user identity has become a critical issue affecting users' certain level to use the app as main tools under a refuse management system .As a result, most of the service providers need to build and maintain their own databases of user information for all kind of system they developed.Therefore, the objective of the study is to identify major security issue and inadequacy of the current blockchain mechanism in the refuse management system and to explore why blockchain mechanism should be used as a mechanism for higher authentication security in refuse management system .Thus, this project will demonstrate the potential of why blockchain should be used as a mechanism for in cleaning up the environment.The ultimate outcome looks at the potential blockchain application in refuse management system for the utilizing of the surrounding committee to clean the environment.