Miracle-dz/UniFAR-FTUs huggingface dataset:
Association Between Malocclusion and Academic Performance Among Mongolian Adolescents

Objective: Malocclusion has been reported to affect the daily lives of schoolchildren adversely, but little is known regarding the association between malocclusion and academic performance. We aimed to investigate the association between malocclusion and academic performance among adolescents in Mongolia. Methods: We conducted a cross-sectional study of 767 students aged 7–16 years from two public schools in Ulaanbaatar, Mongolia. Three orthodontists evaluated the need for malocclusion treatment in the participants and determined the type of malocclusion using the Index of Orthodontic Treatment Need and dental casts. The academic scores of study participants in 20 subjects were provided by their schools. Z-scores within subjects were calculated and aggregated into both overall and in six groups of subject categories comprised of mathematics, science, social science, language, arts, and physical education. A multiple linear regression analysis was performed to determine the association between malocclusion, malocclusion type, and academic score adjusted for gender, age, school, and family income. Results: Of the 767 students, 32.6% had malocclusion, and dental crowding was the most prevalent type (162 cases, 21.1%). Malocclusion was not significantly associated with the z-score of overall academic score [coefficient: 0.04, 95% confidence interval (CI): −0.11 to 0.19]; however, dental crowding was significantly associated with the overall academic score (coefficient: −0.19, 95% CI: −0.35 to −0.03), after adjusting for covariates. Other types of malocclusion were not associated with academic scores. Among the six subject categories, arts (coefficient: −0.20, 95% CI: −0.36 to −0.04) and physical education (coefficient: −0.24, 95% CI: −0.42 to −0.07) were significantly associated with dental crowding. Conclusions: Schoolchildren in Mongolia with dental crowding may be prone to poor academic performance, particularly in arts and physical education classes. Further randomized controlled trials are needed to determine whether the treatment of crowding boosts academic performance.
Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent

A fundamental concept in statistics is the concept of Fr´echet sample mean. While its computation is a simple task in Euclidian space, the same does not hold for less structured spaces such as the space of graphs, where concepts of distance or mid-point can be hard to compute. We present some work in progress regarding new distance measures and new algorithms to compute the Fr´echet mean in the space of Graphs.
Human Body Multiple Parts Parsing for Person Reidentification Based on Xception

A mass of information grows explosively in socially networked industries, as extensive data, such as images and texts, is captured by vast sensors. Pedestrians are the main initiators of various activities in socially networked industries, hence, it is very important to quickly obtain relevant information of pedestrians from a large number of images. Person reidentification is an image retrieval technology, which can immediately retrieve target person in abundant images. However, due to the complexity of many important factors especially of changeful poses, occlusion and background clutter, person reidentification still faces extensive challenges. Considering these challenges, robust and distinguishing person representations are hard to be extracted well to identify different people. In this paper, to obtain more discriminative representations, we propose a human body multiple parts parsing (BMPP) architecture, which captures local pixel-level representations from body parts and global representations from whole body simultaneously. Additionally, a straightforward preprocessing method is adopted in this paper to improve the resolution of images in person reidentification benchmarks. To eliminate the negative effects of changeful poses, a simple yet effective representation fusion strategy is used for the original and horizontally flipped images to get final representations. Experimental results indicate that the method proposed in this article attains superior performance to most of state-of-the-art methods on CUHK03 and Market-1501. © 2021 The Authors . Published by Atlantis Press B
Learning Step Size Adaptation in Evolution Strategies

Step size adaptation is an essential part of successful evolution strategies in continuous solution spaces as they moderate between exploration and exploitation. We propose to learn step sizes evolved with a σ -self-adaptive (1 + λ )-ES using LSTMs. Based on input sequences of multi-variate distances between best solutions of successive generations and their step sizes a long short-term memory network (LSTM) is trained. The learned distances-step size pairs guide the search of the LSTM-ES, which is a (1 + λ )-ES with LSTM step size predictions. An experimental analysis illustrates the behavior of the LSTM-ES on the Sphere function with diﬀerent parameter settings and problem dimensionalities.
An quantum algorithm for feedforward neural networks tested on existing quantum hardware

We present a memory-eﬃcient quantum algorithm implementing the action of an artiﬁcial neuron according to a binary-valued model of the classical perceptron. The algorithm, tested on noisy IBM-Q superconducting real quantum processors, succeeds in elementary classi-ﬁcation and image-recognition tasks through a hybrid quantum-classical training procedure. Here we also show that this model is amenable to be extended to a multilayered artiﬁcial neural network, which is able to solve a task that would be impossible to a single one of its constituent artiﬁcial neurons, thus laying the basis for a fully quantum artiﬁcial intelligence algorithm run on noisy intermediate-scale quantum hardware.
Simplifying Deep Reservoir Architectures

We study the impact of architectural simpliﬁcations to the design of deep Reservoir Computing (RC) models. To do so, we analyze the eﬀects of shaping the structure of reservoir matrices, reducing the complexity of the deep recurrent network to a minimal setup. Experimental results point out the beneﬁts of a particularly simple deep RC architecture with ring topology in each reservoir layer and deterministically constructed input and inter-reservoir connections.
STAR

Timed association rules (TARs) generalize classical association rules (ARs) so that we can express temporal dependencies of the form “If X is true at time t, then Y will likely be true at time (t+τ). ” As with ARs, solving the TAR mining problem can generate huge numbers of rules. We show that methods to summarize ARs cannot work directly with TARs, and we develop two notions—strong and weak summaries—to summarize a set of TARs. We show that the problems of finding strong/weak summaries are NP-hard, and we provide polynomial-time approximation algorithms. We show experimentally that the coverage provided by our summarization methods is very high. Both technical measures based on coverage and human experiments on six World Bank datasets using 100 subjects from Mechanical Turk and a separate experiment with terrorism experts on a terrorism dataset show that while both summarization methods perform well, weak summaries are preferred, despite their taking more time to compute than strong summaries.
SGXL

Intel’s SGX architecture offers clients of public cloud computing platforms the ability to create hardware-protected enclaves whose contents are protected from privileged system software. However, SGX relies on system software for enclave memory management. In a sequence of recent papers, researchers have demonstrated that this reliance allows a malicious OS/hypervisor to snoop on the page addresses being accessed from within an enclave via various channels. This page address stream can then be used to infer secrets if the enclave’s page access pattern depends upon the secret and this constitutes an important class of side-channels. We propose SGXL, a hardware-software co-designed system that significantly increases the difficulty of any page address-based side-channels through the use of large pages. A large page maps address ranges at a much larger granularity than the default page size (at least 512× larger). SGXL thus significantly lowers resolution of the leaked page address stream and could practically throttle all flavors of page-address based side-channels. We detail the modifications needed to SGX’s software stack and the (minor) hardware enhancements required for SGXL to guarantee the use of large pages in the presence of adversarial system software. We empirically show that SGXL could be one of those rare systems that enhances security with the potential of improving performance as well.
Augmented reality in history education: an immersive storytelling of American colonisation period in the Philippines

: History education ordinarily faces a relativist slant, if not by the monotonous nature of the course. Hence, educators are continuously in pursuit for a better teaching strategy to keep the class interesting. The main goal of this study was to ‘bring history to life’ through a mobile application powered by augmented reality that can provide an immersive storytelling experience on the American Colonisation Period in the Philippines. Outlined as a second quarter lesson in K to 12 Basic Education Curriculum for Social Studies by the Department of Education, the Historic Augmented Reality Application (HARA) depicts the: 1) Battle of Manila Bay; 2) Mock Battle of Manila; 3) First Shot in Philippine-American War. Through a participatory approach, co-designers evaluated HARA in terms of pedagogical value (knowledge acquisition, acceptability, motivation, and attitude), and quality standards (effectiveness, satisfaction, and efficiency) through a usability inspection method. The iterative nature of the project development via a co-design approach with end users revealed early bugs, shortcomings, and possible improvements on the app. The rest is history! paper research interests include topics that attempt to renovate the landscape of education by challenging the boundaries of
Finite-time formation control for first-order multi-agent systems with region constraints

In this study, the finite-time formation control of multi-agent systems with region constraints is studied. Multiple agents have first-order dynamics and a common target area. A novel control algorithm is proposed using local information and interaction. If the communication graph is undirected and connected and the desired framework is rigid, it is proved that the controller can be used to solve the formation problem with a target area. That is, all agents can enter the desired region in finite time while reaching and maintaining the desired formation shapes. Finally, a numerical example is given to illustrate the results.
Molecular dynamics simulation of configurational properties of complexes between β-cyclodextrin and 2,6-diaziadamantane in water

Supramolecular complexes between β-cyclodextrin and 2,6-diaziadamantane in water are prepared by molecular dynamics simulations. A large number of arrangements taken from trajectories in picosecond intervals are analysed with respect to relative orientation and compared to results obtained for 2-aziadamantane containing a single diazirine functionality only. While for the latter two stable orientations have been observed, 2,6-diaziadamantane exhibits a single favourite orientation which is similar to the energetically more preferred orientation of 2-aziadamantane. Furthermore, the influence of the guest on the shape of the host is studied in some detail.
Explorations in Quantum Neural Networks with Intermediate Measurements

In this short note we explore a few quantum circuits with the particular goal of basic image recognition. The models we study are inspired by recent progress in Quantum Convolution Neural Networks (QCNN) [12]. We present a few experimental results, where we attempt to learn basic image patterns motivated by scaling down the MNIST dataset.
The failure mode of adhesively bonded aluminium following aqueous exposure

Adhesively bonded aluminium sheets pretreated by phosphoric acid anodizing (PAA) or chromic acid anodizing (CAA) were fabricated into 180° T-peel test pieces. These were then exposed to cycles of aqueous exposure at elevated temperature, followed by a drying procedure at ambient or elevated temperatures. In the case of the CAA-pretreated substrate, the aqueous exposure brings about a drastic reduction in joint strength and the locus of failure (as assessed by XPS) moves from the centre of the glue line to very close to the oxide/adhesive interface. The drying procedure increases the peel strength, and the failure path once again moves towards the bulk of the adhesive. This behaviour is ascribed to the agregation and to the subsequent removal of water molecules close to the interface. The behaviour of the PAA-pretreated joints is completely different. The short water immersion times used in this work have little effect on peel strength but subsequent drying at elevated temperature does reduce the joint strength considerably. It is postulated that this may be the result of leaching and subsequent deposition of a component of the adhesive, perhaps some unreacted curing agent. This behaviour is also reversible and the peel strength can be increased by re-exposing the joint to water. Thus, for both CAA and PAA pretreatments the strength can be affected greatly by environmental exposure, but both phenomena show a degree of reversibility.
The Oxidation of Cellulose by Chromium Trioxide— A New Method for Determining Accessibility

The secondary network consists of bundles of chains bound together by secondary bonds. An explanation is offered for the effects of D.P. and cellulose concentration on the shape of the loadelongation curve and on the magnitude of these quantities at rupture. The proposed structure permits a more satisfactory explanation of the variations in internal energy accompanying all deformations of cellulose gels.
Image completion via nonnegative matrix factorization using B-splines

When performing image completion, it is common to assume that images are smooth and low-rank, when viewed as matrices of pixel intensities. In this work, we use nonnegative matrix factorization to successively reﬁne the image by representing alternatively rows and columns as smooth signals using splines. Previous work solved this model using an alternating direction method of multipliers. Instead, we propose to use a version of the hierarchical alternating least squares algorithm adapted to handle splines, and show in numerical experiments that it outperforms the existing method. Performance can be further improved by increasing progressively the size of used splines. We also introduce a non iterative algorithm using the same NMF approach, where factorization is computed in a fast and accurate way but for which convergence is harder to achieve.
Dual Neural Network Fusion Model for Chinese Named Entity Recognition

Chinese named entity recognition (NER) has important effect on natural language processing (NLP) applications. This recognition task is complicated in its strong dependent-relation, missing delimiters in the text and insufficient feature representation in a single model. This paper thus proposes a dual neural network fusion model (DFM) to improve Chinese NER performance. We integrate the traditional bi-directional long-short-term memory (BiLSTM) structure and self-attention mechanism (ATT) with dilated convolutional neural network (DCNN) to better capture context information. Additionally, we exploit the Google’s pre-trained model named bi-directional encoder representations from transformers (BERT) as the embedding layer. The proposed model has the following merits: (1) a dual neural network architecture is proposed to enhance the robustness of extracted features. (2) An attention mechanism is fused into the dual neural network to extract implicit context representation information in Chinese NER. (3) Dilated convolutions are used to make a tradeoff between performance and executing speed. Experiments show that our proposed model exceeds the state-of-the-art Chinese NER methods. © 2021 The Authors . Published by Atlantis Press
Resilient Scheduling Heuristics for Rigid Parallel Jobs

This paper focuses on the resilient scheduling of parallel jobs on high-performance computing (HPC) platforms to minimize the overall completion time, or the makespan. We revisit the classical problem while assuming that jobs are subject to failures caused by transient or silent errors, and hence may need to be re-executed each time they fail to complete successfully. This work generalizes the classical framework where jobs are known offline and do not fail: in this framework, list scheduling that gives priority to the longest jobs is known to be a 3-approximation when imposing to use shelves, and a 2-approximation without this restriction. We show that when jobs can fail, using shelves can be arbitrarily bad, but unrestricted list scheduling remains a 2-approximation. The paper focuses on the design of several heuristics, some list-based and some shelf-based, along with different priority rules and backfilling strategies. We assess and compare their performance through an extensive set of simulations using both synthetic jobs and log traces from the Mira supercomputer.
A Survey of Machine Learning applied to Computer Networks

We review the current state of the art in the domain of machine learning applied to computer networks. First of all, we describe recent developments in computer networking and outline the potential ﬁelds for machine learning that arise from these developments. We discuss challenges for machine learning in this particular ﬁeld, namely the inherent big data aspect of computer networks, and the fact that learning very often needs to be conducted in a streaming setting with non-stationary data distributions. We discuss practical issues like privacy protection and computing resources before ﬁnally outlining potential technological beneﬁts of this emerging scientiﬁc ﬁeld.
Neurological Measurement of Human Trust in Automation Using Electroencephalogram

In modern society, automation is sufﬁciently complex to conduct advanced tasks. The role of the human operator in controlling a complex automation is crucial for avoiding failures, reducing risk, and preventing unpredictable situations. Measuring the level of trust of human operators is vital in predicting their acceptance and reliance on automation. In this study, an electroencephalogram (EEG) is used to identify speciﬁc brainwaves under trusted and mistrusted cases of automation. A power spectrum analysis was used for a brainwave analysis. The results indicate that the power of the alpha and beta waves is stronger for a trusted situation, whereas the power of gamma waves was stronger for a mistrusted situation. When the level of human trust in automation increases, the use of automatic control increases. Therefore, the ﬁndings of this research will contribute to utilizing a neurological technology to measure the level of trust of the human operator, which can affect the decision-making and the overall performance of automation used in industries.
Exploring the feature space of character-level embeddings

Recently, character-level embeddings have become popular in the Natural Language Processing community. These representations provide a description of a word which depends solely on its inner structure, i.e. the sequence of characters. Convolutional and recurrent neural networks are the undisputed protagonists in this context, and they represent the state of the art for many character-level applications. In this work, we ﬁrstly compare different neural architectures against adaptive string kernels in simpliﬁed scenarios. Then, we propose a hybrid ensemble that injects structural kernel-based features into a neural architecture, providing an efﬁcient and scalable solution. An all-around experimental assessment has been carried out on several string datasets, including biomedical entity recognition and sentiment analysis.
Linear Graph Convolutional Networks

Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative deﬁnitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a linear graph convolution operator. Despite its simplicity, we show that our convolution operator is more theoretically grounded than many proposals in literature, and shows improved predictive performance
Improving Light-weight Convolutional Neural Networks for Face Recognition Targeting Resource Constrained Platforms

A thorough investigation of the possibility to optimize deep convolutional neural network architectures for face recognition problems is considered, from the perspective of training very compact models to be further deployed on resource-constrained systems. Latencies in recognition phase and memory usage are minimized while recognition accuracies are maintained close to state of the art performance of more complicated deep neural networks. Using two widely used datasets, namely VGG-Face and YouTube Faces, several modifications of a recent light-weight CNN model are proposed, and for a reasonable accuracy the most compact solutions were identified. Experiments on VGG-Face show that our proposed models achieves 95.5% accuracy, with 5.6 times less memory storage when compared to the reference slim model.
Biochemical Pathway Robustness Prediction with Graph Neural Networks

The robustness property of a biochemical pathway refers to maintaining stable levels of molecular concentration against the perturbation of parameters governing the underlying chemical reactions. Its computation requires an expensive integration in parameter space. We present a novel application of Graph Neural Networks (GNN) to predict robustness indicators on pathways represented as Petri nets, without the need of performing costly simulations. Our assumption is that pathway structure alone is suﬃcient to be eﬀective in this task. We show experimentally for the ﬁrst time that this is indeed possible to a good extent, and investigate how diﬀerent architectural choices inﬂuence performances.
Fast Deep Neural Networks Convergence using a Weightless Neural Model

Deep Neural Networks (DNNs) have surged as a promising technique for AI applications combining a huge parametric space with ef-ﬁcient learning algorithms. The eﬃciency of the training procedure relies on some optimization algorithms which adjust the initial weights to minimize the loss of the model. Such strategies are essential to speed up the convergence of the optimization steps. Nonetheless, a general initialization procedure is still an open problem since the proposed techniques either require a long processing time or take a considerable number of iterations to ﬁgure out an acceptable model. This work presents a weight initialization strategy using transfer learning via Weightless Neural Network (WNN). This WNN initialization strategy reaches up to 5 . 5 × accuracy and 15 × loss reduction at the ﬁrst iterations when compared against well-known techniques such as Xavier and He.
Self-Organizing Kernel-based Convolutional Echo State Network for Human Actions Recognition

We propose a deterministic initialization of the Echo State Network reservoirs to ensure that the activation of its internal echo state representations reﬂects similar topological qualities of the input signal which should lead to a self-organizing reservoir. Human actions encoded as a multivariate time series signal are clustered before using the clustered nodes and interconnectivity matrices for initializing the S-ConvESN reservoirs. The capability of S-ConvESN is evaluated using several 3D-skeleton-based action recognition datasets.