bluuebunny/crossref_metadata_2025 huggingface dataset:
Mining EL Bases with Adaptable Role Depth
Authors: Guimarães Ricardo , Ozaki Ana , Persia Cosimo , Sertkaya Baris
Published: September 2022

In Formal Concept Analysis, a base for a finite structure is a set of implications that characterizes all valid implications of the structure. This notion can be adapted to the context of Description Logic, where the base consists of a set of concept inclusions instead of implications. In this setting, concept expressions can be arbitrarily large. Thus, it is not clear whether a finite base exists and, if so, how large concept expressions may need to be. We first revisit results in the literature for mining EL bases from finite interpretations. Those mainly focus on finding a finite base or on fixing the role depth but potentially losing some of the valid concept inclusions with higher role depth. We then present a new strategy for mining EL bases which is adaptable in the sense that it can bound the role depth of concepts depending on the local structure of the interpretation. Our strategy guarantees to capture all EL concept inclusions holding in the interpretation, not only the ones up to a fixed role depth.
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
Authors: Afshar Ardavan , Yin Kejing , Yan Sherry , Qian Cheng , Ho Joyce , Park Haesun , Sun Jimeng
Published: September 2022

Existing tensor factorization methods assume that the input tensor follows some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve the factorization by minimizing some empirical loss functions defined based on the corresponding distribution. However, it suffers from several drawbacks: 1) In reality, the underlying distributions are complicated and unknown, making it infeasible to be approximated by a simple distribution. 2) The correlation across dimensions of the input tensor is not well utilized, leading to sub-optimal performance. Although heuristics were proposed to incorporate such correlation as side information under Gaussian distribution, they can not easily be generalized to other distributions. Thus, a more principled way of utilizing the correlation in tensor factorization models is still an open challenge. Without assuming any explicit distribution, we formulate the tensor factorization as an optimal transport problem with Wasserstein distance, which can handle non-negative inputs.

We introduce SWIFT, which minimizes the Wasserstein distance that measures the distance between the input tensor and that of the reconstruction. In particular, we define the N-th order tensor Wasserstein loss for the widely used tensor CP factorization and derive the optimization algorithm that minimizes it. By leveraging sparsity structure and different equivalent formulations for optimizing computational efficiency, SWIFT is as scalable as other well-known CP algorithms. Using the factor matrices as features, SWIFT achieves up to 9.65% and 11.31% relative improvement over baselines for downstream prediction tasks. Under the noisy conditions, SWIFT achieves up to 15% and 17% relative improvements over the best competitors for the prediction tasks.
DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits
Authors: Agarwal Mridul , Aggarwal Vaneet , Umrawal Abhishek Kumar , Quinn Chris
Published: September 2022

We consider the bandit problem of selecting K out of N arms at each time step. The joint reward can be a non-linear function of the rewards of the selected individual arms. The direct use of a multi-armed bandit algorithm requires choosing among all possible combinations, making the action space large. To simplify the problem, existing works on combinatorial bandits typically assume feedback as a linear function of individual rewards. In this paper, we prove the lower bound for top-K subset selection with bandit feedback with possibly correlated rewards. We present a novel algorithm for the combinatorial setting without using individual arm feedback or requiring linearity of the reward function. Additionally, our algorithm works on correlated rewards of individual arms. Our algorithm, aDaptive Accept RejecT (DART), sequentially finds good arms and eliminates bad arms based on confidence bounds. DART is computationally efficient and uses storage linear in N. Further, DART achieves a regret bound of Õ(K√KNT) for a time horizon T, which matches the lower bound in bandit feedback up to a factor of √log 2NT. When applied to the problem of cross-selling optimization and maximizing the mean of individual rewards, the performance of the proposed algorithm surpasses that of state-of-the-art algorithms. We also show that DART significantly outperforms existing methods for both linear and non-linear joint reward environments.
The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits
Authors: Arenas Marcelo , Barceló Pablo , Bertossi Leopoldo , Monet Mikaël
Published: September 2022

Scores based on Shapley values are widely used for providing explanations to classification results over machine learning models. A prime example of this is the influential SHAP-score, a version of the Shapley value that can help explain the result of a learned model on a specific entity by assigning a score to every feature. While in general computing Shapley values is a computationally intractable problem, it has recently been claimed that the SHAP-score can be computed in polynomial time over the class of decision trees. In this paper, we provide a proof of a stronger result over Boolean models: the SHAP-score can be computed in polynomial time over deterministic and decomposable Boolean circuits. Such circuits, also known as tractable Boolean circuits, generalize a wide range of Boolean circuits and binary decision diagrams classes, including binary decision trees, Ordered Binary Decision Diagrams (OBDDs) and Free Binary Decision Diagrams (FBDDs). We also establish the computational limits of the notion of SHAP-score by observing that, under a mild condition, computing it over a class of Boolean models is always polynomially as hard as the model counting problem for that class. This implies that both determinism and decomposability are essential properties for the circuits that we consider, as removing one or the other renders the problem of computing the SHAP-score intractable (namely, #P-hard).
Robust Model Compression Using Deep Hypotheses
Authors: Armstrong Omri , Gilad-Bachrach Ran
Published: September 2022

Machine Learning models should ideally be compact and robust. Compactness provides efficiency and comprehensibility whereas robustness provides stability. Both topics have been studied in recent years but in isolation. Here we present a robust model compression scheme which is independent of model types: it can compress ensembles, neural networks and other types of models into diverse types of small models.
The main building block is the notion of depth derived from robust statistics.
Originally, depth was introduced as a measure of the centrality of a point in a sample such that the median is the deepest point.
This concept was extended to classification functions which makes it possible to define the depth of a hypothesis and the median hypothesis. Algorithms have been suggested to approximate the median but they have been limited to binary classification. In this study, we present a new algorithm, the Multiclass Empirical Median Optimization (MEMO) algorithm that finds a deep hypothesis in multi-class tasks, and prove its correctness.
This led to our Compact Robust Estimated Median Belief Optimization (CREMBO) algorithm for robust model compression. We demonstrate the success of this algorithm empirically by compressing neural networks and random forests into small decision trees, which are interpretable models, and show that they are more accurate and robust than other comparable methods. In addition, our empirical study shows that our method outperforms Knowledge Distillation on DNN to DNN compression.
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
Authors: Bai Haoyue , Sun Rui , Hong Lanqing , Zhou Fengwei , Ye Nanyang , Ye Han-Jia , Chan S.-H. Gary , Li Zhenguo
Published: September 2022

While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework for a wide range of applications is challenging, mainly due to different kinds of distribution shifts in the real world, such as the shift across domains or the extrapolation of correlation. Most of the previous approaches can only solve one specific distribution shift, leading to unsatisfactory performance when applied to various OoD benchmarks. In this work, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization. Specifically, DecAug disentangles the category-related and context-related features by orthogonalizing the two gradients (w.r.t. intermediate features) of losses for predicting category and context labels, where category-related features contain causal information of the target object, while context-related features cause distribution shifts between training and test data. Furthermore, we perform gradient-based augmentation on context-related features to improve the robustness of learned representations. Experimental results show that DecAug outperforms other state-of-the-art methods on various OoD datasets, which is among the very few methods that can deal with different types of OoD generalization challenges.
Communication-Aware Collaborative Learning
Authors: Blum Avrim , Heinecke Shelby , Reyzin Lev
Published: September 2022

Algorithms for noiseless collaborative PAC learning have been analyzed and optimized in recent years with respect to sample complexity. In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity. We develop communication efficient collaborative PAC learning algorithms using distributed boosting. We then consider the communication cost of collaborative learning in the presence of classification noise. As an intermediate step, we show how collaborative PAC learning algorithms can be adapted to handle classification noise. With this insight, we develop communication efficient algorithms for collaborative PAC learning robust to classification noise.
High-Confidence Off-Policy (or Counterfactual) Variance Estimation
Authors: Chandak Yash , Shankar Shiv , Thomas Philip S.
Published: September 2022

Many sequential decision-making systems leverage data collected using prior policies to propose a new policy. For critical applications, it is important that high-confidence guarantees on the new policy’s behavior are provided before deployment, to ensure that the policy will behave as desired. Prior works have studied high-confidence off-policy estimation of the expected return, however, high-confidence off-policy estimation of the variance of returns can be equally critical for high-risk applications. In this paper we tackle the previously open problem of estimating and bounding, with high confidence, the variance of returns from off-policy data.
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Authors: Cruceru Calin , Becigneul Gary , Ganea Octavian-Eugen
Published: September 2022

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases (e.g., hierarchical structures benefit from hyperbolic geometry). However, going beyond embedding spaces of constant sectional curvature, while potentially more representationally powerful, proves to be challenging as one can easily lose the appeal of computationally tractable tools such as geodesic distances or Riemannian gradients. Here, we explore two computationally efficient matrix manifolds, showcasing how to learn and optimize graph embeddings in these Riemannian spaces. Empirically, we demonstrate consistent improvements over Euclidean geometry while often outperforming hyperbolic and elliptical embeddings based on various metrics that capture different graph properties. Our results serve as new evidence for the benefits of non-Euclidean embeddings in machine learning pipelines.
Loop Estimator for Discounted Values in Markov Reward Processes
Authors: Dai Falcon Z. , Walter Matthew R.
Published: September 2022

At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process. We propose a simple and efficient estimator called loop estimator that exploits the regenerative structure of Markov reward processes without explicitly estimating a full model. Our method enjoys a space complexity of O(1) when estimating the value of a single positive recurrent state s unlike TD with O(S) or model-based methods with O(S^2). Moreover, the regenerative structure enables us to show, without relying on the generative model approach, that the estimator has an instance-dependent convergence rate of O~(\sqrt{\tau_s/T}) over steps T on a single sample path, where \tau_s is the maximal expected hitting time to state s. In preliminary numerical experiments, the loop estimator outperforms model-free methods, such as TD(k), and is competitive with the model-based estimator.
Knowledge Refactoring for Inductive Program Synthesis
Authors: Dumancic Sebastijan , Guns Tias , Cropper Andrew
Published: September 2022

Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the knowledge refactoring problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. A key feature of Knorf is that, rather than simply removing knowledge, it also introduces new knowledge through predicate invention. We evaluate our approach on two domains: building Lego structures and real-world string transformations. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Authors: Fiedler Christian , Scherer Carsten W. , Trimpe Sebastian
Published: September 2022

Gaussian Process regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required. Although such rigorous bounds are available for Gaussian Processes, they are too conservative to be useful in applications. This often leads practitioners to replacing these bounds by heuristics, thus breaking all theoretical guarantees. To address this problem, we introduce new uncertainty bounds that are rigorous, yet practically useful at the same time. In particular, the bounds can be explicitly evaluated and are much less conservative than state of the art results. Furthermore, we show that certain model misspecifications lead to only graceful degradation. We demonstrate these advantages and the usefulness of our results for learning-based control with numerical examples.
On the Convergence of Communication-Efficient Local SGD for Federated Learning
Authors: Gao Hongchang , Xu An , Huang Heng
Published: September 2022

Federated Learning (FL) has attracted increasing attention in recent years. A leading training algorithm in FL is local SGD, which updates the model parameter on each worker and averages model parameters across different workers only once in a while. Although it has fewer communication rounds than the classical parallel SGD, local SGD still has large communication overhead in each communication round for large machine learning models, such as deep neural networks. To address this issue, we propose a new communication-efficient distributed SGD method, which can significantly reduce the communication cost by the error-compensated double compression mechanism. Under the non-convex setting, our theoretical results show that our approach has better communication complexity than existing methods and enjoys the same linear speedup regarding the number of workers as the full-precision local SGD. Moreover, we propose a communication-efficient distributed SGD with momentum, which also has better communication complexity than existing methods and enjoys a linear speedup with respect to the number of workers. At last, extensive experiments are conducted to verify the performance of our proposed two methods. Moreover, we propose a communication-efficient distributed SGD with momentum to accelerate the convergence, which also has better communication complexity than existing methods and enjoys a linear speedup with respect to the number of workers. At last, extensive experiments are conducted to verify the performance of our proposed methods.
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations
Authors: Gokhale Tejas , Anirudh Rushil , Kailkhura Bhavya , J. Thiagarajan Jayaraman , Baral Chitta , Yang Yezhou
Published: September 2022

While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings.
In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known.
We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain.
While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes.
We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain.
Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations,
and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization.
We demonstrate the applicability of our approach on three types of naturally occurring perturbations --- object-related shifts, geometric transformations, and common image corruptions.
Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.
Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization
Authors: Guo Yinuo , Zhu Hualei , Lin Zeqi , Chen Bei , Lou Jian-Guang , Zhang Dongmei
Published: September 2022

Human intelligence exhibits compositional generalization (i.e., the capacity to
understand and produce unseen combinations of seen components), but current neural seq2seq models lack such ability. In this paper, we revisit iterative back-translation, a simple yet effective semi-supervised method, to investigate whether and how it can improve compositional generalization. In this work:
(1) We first empirically show that iterative back-translation substantially improves the performance on compositional generalization benchmarks (CFQ and SCAN).
(2) To understand why iterative back-translation is useful, we carefully examine the performance gains and find that iterative back-translation can increasingly correct errors in pseudo-parallel data.
(3) To further encourage this mechanism, we propose curriculum iterative back-translation, which better improves the quality of pseudo-parallel data, thus further improving the performance.
Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs
Authors: HasanzadeZonuzy Aria , Bura Archana , Kalathil Dileep , Shakkottai Srinivas
Published: September 2022

Many physical systems have underlying safety considerations that require that the policy employed ensures the satisfaction of a set of constraints. The analytical formulation usually takes the form of a Constrained Markov Decision Process (CMDP). We focus on the case where the CMDP is unknown, and RL algorithms obtain samples to discover the model and compute an optimal constrained policy. Our goal is to characterize the relationship between safety constraints and the number of samples needed to ensure a desired level of accuracy---both objective maximization and constraint satisfaction---in a PAC sense. We explore two classes of RL algorithms, namely, (i) a generative model based approach, wherein samples are taken initially to estimate a model, and (ii) an online approach, wherein the model is updated as samples are obtained. Our main finding is that compared to the best known bounds of the unconstrained regime, the sample complexity of constrained RL algorithms are increased by a factor that is logarithmic in the number of constraints, which suggests that the approach may be easily utilized in real systems.
Scaling-Up Robust Gradient Descent Techniques
Authors: Holland Matthew J.
Published: September 2022

We study a scalable alternative to robust gradient descent (RGD) techniques that can be used when losses and/or gradients can be heavy-tailed, though this will be unknown to the learner. The core technique is simple: instead of trying to robustly aggregate gradients at each step, which is costly and leads to sub-optimal dimension dependence in risk bounds, we choose a candidate which does not diverge too far from the majority of cheap stochastic sub-processes run over partitioned data. This lets us retain the formal strength of RGD methods at a fraction of the cost.
Topology Distance: A Topology-Based Approach for Evaluating Generative Adversarial Networks
Authors: Horak Danijela , Yu Simiao , Salimi-Khorshidi Gholamreza
Published: September 2022

Automatic evaluation of the goodness of Generative Adversarial Networks (GANs) has been a challenge for the field of machine learning. In this work, we propose a distance complementary to existing measures: Topology Distance (TD), the main idea behind which is to compare the geometric and topological features of the latent manifold of real data with those of generated data. More specifically, we build Vietoris-Rips complex on image features, and define TD based on the differences in persistent-homology groups of the two manifolds. We compare TD with the most commonly-used and relevant measures in the field, including Inception Score (IS), Fr\'echet Inception Distance (FID), Kernel Inception Distance (KID) and Geometry Score (GS), in a range of experiments on various datasets. We demonstrate the unique advantage and superiority of our proposed approach over the aforementioned metrics. A combination of our empirical results and the theoretical argument we propose in favour of TD, strongly supports the claim that TD is a powerful candidate metric that researchers can employ when aiming to automatically evaluate the goodness of GANs’ learning.
Continual Learning by Using Information of Each Class Holistically
Authors: Hu Wenpeng , Qin Qi , Wang Mengyu , Ma Jinwen , Liu Bing
Published: September 2022

Continual learning (CL) incrementally learns a sequence of tasks while solving
the catastrophic forgetting (CF) problem. Existing methods mainly try to deal with CF directly.
In this paper, we propose to avoid CF by considering the features of each class holistically rather than only the discriminative information for classifying the classes seen so far. This latter approach is prone to CF because the discriminative information for old classes may not be sufficiently discriminative for the new class to be learned. Consequently, in learning each new task, the network parameters for previous tasks have to be revised, which causes CF. With the holistic consideration, after adding new tasks, the system can still do well for previous tasks. The proposed technique is called Per-class Continual Learning (PCL).
PCL has two key novelties. (1) It proposes a one-class learning based technique for CL, which considers features of each class holistically and represents a new approach to solving the CL problem. (2) It proposes a method to extract discriminative information after training to further improve the accuracy. Empirical evaluation shows that PCL markedly outperforms the state-of-the-art baselines for one or more classes per task. More tasks also result in more gains.
Large Batch Optimization for Deep Learning Using New Complete Layer-Wise Adaptive Rate Scaling
Authors: Huo Zhouyuan , Gu Bin , Huang Heng
Published: September 2022

Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications. Warmup is one of nontrivial techniques to stabilize the convergence of large batch training. However, warmup is an empirical method and it is still unknown whether there is a better algorithm with theoretical underpinnings. In this paper, we propose a novel Complete Layer-wise Adaptive Rate Scaling (CLARS) algorithm for large-batch training. We prove the convergence of our algorithm by introducing a new fine-grained analysis of gradient-based methods. Furthermore, the new analysis also helps to understand two other empirical tricks, layer-wise adaptive rate scaling and linear learning rate scaling. We conduct extensive experiments and demonstrate that the proposed algorithm outperforms gradual warmup technique by a large margin and defeats the convergence of the state-of-the-art large-batch optimizer in training advanced deep neural networks (ResNet, DenseNet, MobileNet) on ImageNet dataset.
Active Bayesian Assessment of Black-Box Classifiers
Authors: Ji Disi , Logan Robert L. , Smyth Padhraic , Steyvers Mark
Published: September 2022

Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical need to both reliably assess the performance of these pre-trained models and to perform this assessment in a label-efficient manner (given that labels may be scarce and costly to collect). In this paper, we introduce an active Bayesian approach for assessment of classifier performance to satisfy the desiderata of both reliability and label-efficiency. We begin by developing inference strategies to quantify uncertainty for common assessment metrics such as accuracy, misclassification cost, and calibration error. We then propose a general framework for active Bayesian assessment using inferred uncertainty to guide efficient selection of instances for labeling, enabling better performance assessment with fewer labels. We demonstrate significant gains from our proposed active Bayesian approach via a series of systematic empirical experiments assessing the performance of modern neural classifiers (e.g., ResNet and BERT) on several standard image and text classification datasets.
Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks
Authors: Jia Jinyuan , Cao Xiaoyu , Gong Neil Zhenqiang
Published: September 2022

In a data poisoning attack, an attacker modifies, deletes, and/or inserts some training examples to corrupt the learnt machine learning model. Bootstrap Aggregating (bagging) is a well known ensemble learning method, which trains multiple base models on random subsamples of a training dataset using a base learning algorithm and uses majority vote to predict labels of testing examples. We prove the intrinsic certified robustness of bagging against data poisoning attacks. Specifically, we show that bagging with an arbitrary base learning algorithm provably predicts the same label for a testing example when the number of modified, deleted, and/or inserted training examples is bounded by a threshold. Moreover, we show that our derived threshold is tight if no assumptions on the base learning algorithm are made. We evaluate our method on MNIST and CIFAR10. For instance, our method achieves a certified accuracy of 91.1% on MNIST when arbitrarily modifying, deleting, and/or inserting 100 training examples. Code is available at: https://github.com/jjy1994/BaggingCertifyDataPoisoning.
A Recipe for Global Convergence Guarantee in Deep Neural Networks
Authors: Kawaguchi Kenji , Sun Qingyun
Published: September 2022

Existing global convergence guarantees of (stochastic) gradient descent do not apply to practical deep networks in the practical regime of deep learning beyond the neural tangent kernel (NTK) regime. This paper proposes an algorithm, which is ensured to have global convergence guarantees in the practical regime beyond the NTK regime, under a verifiable condition called the expressivity condition. The expressivity condition is defined to be both data-dependent and architecture-dependent, which is the key property that makes our results applicable for practical settings beyond the NTK regime. On the one hand, the expressivity condition is theoretically proven to hold data-independently for fully-connected deep neural networks with narrow hidden layers and a single wide layer. On the other hand, the expressivity condition is numerically shown to hold data-dependently for deep (convolutional) ResNet with batch normalization with various standard image datasets. We also show that the the proposed algorithm has generalization performances comparable with those of the heuristic algorithm, with the same hyper-parameters and total number of iterations. Therefore, the proposed algorithm can be viewed as a step towards providing theoretical guarantees for deep learning in the practical regime.
Token-Aware Virtual Adversarial Training in Natural Language Understanding
Authors: Li Linyang , Qiu Xipeng
Published: September 2022

Gradient-based adversarial training is widely used in improving the robustness of neural networks, while it cannot be easily adapted to natural language processing tasks since the embedding space is discrete.
In natural language processing fields, virtual adversarial training is introduced since texts are discrete and cannot be perturbed by gradients directly.
Alternatively, virtual adversarial training, which generates perturbations on the embedding space, is introduced in NLP tasks.
Despite its success, existing virtual adversarial training methods generate perturbations roughly constrained by Frobenius normalization balls.
To craft fine-grained perturbations, we propose a Token-Aware Virtual Adversarial Training method.
We introduce a token-level accumulated perturbation vocabulary to initialize the perturbations better and use a token-level normalization ball to constrain these perturbations pertinently.
Experiments show that our method improves the performance of pre-trained models such as BERT and ALBERT in various tasks by a considerable margin.
The proposed method improves the score of the GLUE benchmark from 78.3 to 80.9 using BERT model and it also enhances the performance of sequence labeling and text classification tasks.
Etapas del análisis de la información documental
Authors: Peña Vera Tania
Published: September 2022

El análisis de la información y los productos derivados de él representan una valiosa herramienta de trabajo cuando se desea obtener el máximo provecho de los amplios volúmenes de información a los que es posible acceder en la actualidad. En atención a esta notable importancia, el objetivo central de este documento fue mencionar las etapas para la obtención de un análisis de la información adecuado, como mecanismo para el acceso expedito a contenidos de interés y herramienta de investigación; en consecuencia, como medio para apalancar la producción de nuevos conocimientos. Para ello, se utilizó la metodología de investigación documental, con los aportes de algunos autores que se integraron en una construcción sintética que respondiera al mencionado objetivo.
EXPLORING EFFECTIVENESS OF PEER COUNSELLING IN MITIGATING DRUG AND SUBSTANCE ABUSE IN ZIMBABWEAN SECONDARY SCHOOLS: RURAL LEARNERS’ PERSPECTIVE
Authors: Chikuvadze Pinias , Chidarikire Munyraradzi , D. Mbawuya Kudzai
Published: September 2022

In rural secondary schools in Zimbabwe, there has been an increase in the number of learners involved in drug and substance abuse. In response, numerous measures among them guidance and counselling have been put in place to mitigate learners against abusing drugs and substances. It is in this context that this study sought to gain insight into the effectiveness of peer counselling strategy as a homegrown remedy to the reduction of drug and substance abuse by rural secondary school learners. It is in this context that the data generation was guided by the qualitative approach. Twenty rural secondary school learners and six stakeholders from different sectors communities who are involved in guidance and counselling initiatives were purposively sampled. Data generated through document analysis and in-depth interviews were analysed according to emerging themes. Findings revealed that in some cases both rural female and male learners are involved in drug and substance abuse as in their interactions they influence each other. Consequently, drug and substance abuse in these rural secondary schools cannot be regarded as a ‘boys’ hazard. Therefore, this called for the need to empower learners in these rural schools through the engagement of all stakeholders in the formulation of the peer counselling strategy. It is against this background that it can be concluded that to a larger extent peer counselling can be an effective strategy in alleviating drug and substance abuse amongst learners in Zimbabwean rural secondary schools. Therefore the following recommendation was made: in rural secondary schools coaching of all stakeholders on the implementation of peer counselling as an instrument to ease off learners' engagement in anti-social conducts should be strengthened.
STRUCTURAL ANALYSIS OF CODE-MIXING AMONG JORDANIANS ON FACEBOOK
Authors: Alamaren Eman , Adi Kasuma Shaidatul Akma
Published: September 2022

The main issue of the present aims at investigating the most frequent English categories that Jordanian students tend to use within Arabic discourse on Facebook. Furthermore, the study tries to examine the validity of Polack’s universal constraint ‘size of constituent' and whether Jordanian students tend to mix small constituents like nouns, verbs, adjectives, and adverbs, or they tend to mix large English constituents like clauses and phrases. To this end, a research design involves both quantitative and qualitative approaches was used. The quantitative analysis collected numeric information and used frequency distribution technique. On the other hand, the qualitative analysis collected qualitative data and used thematic analysis technique. The quantitative analysis revealed that Jordanian speakers generally insert both small and large English constituents in their speech; 70% of the inserted English constituents were small while large constituents constituted 29% of the constituents. That means, Jordanians tend to use small English constituents more than large ones. Particularly, nouns are the most frequently used mixed English constituents with (30 %), followed by adjectives which constituted 13% of the mixed English constituents. Accordingly, Poplack's universal constraint of size of constituent is violated. On the other side, the qualitative analysis of the data obtained showed that the size of the inserted English constituents has no effect on Arabic sentence structure and word order, that is, speakers form the sentence according to Arabic language and use constituents that have very limited grammatical restrictions. As a conclusion, Jordanian students use English nouns as the most frequently used English categories. Furthermore, the study provides a theoretical implication that is directed to the Arabic Language Academies in the Arab World. In fact, Arabic Academic are responsible to coin appropriate Arabic categories that meet the need of using English categories. That is, coining new Arabic categories will limit using English categories within Arabic discourse, save Arabic purity, and enrich Arabic language with new terms. Thus, Arabic language have to have new lexicon to develop and stay alive.
SPI ve SPEI ile Samsun İli Kuraklık Analizi
Authors: YÜCE Mehmet İshak , AKSOY Hafzullah , AYTEK Ali , EŞİT Musa , UĞUR Fetihhan , YAŞA İslam , ŞİMŞEK Abdulselam , DEGER İbrahim Halil
Published: September 2022

Su kaynakları planlaması ve yönetiminde kuraklığın tanımlanması, izlenmesi ve karakterizasyonu büyük önem taşımaktadır. Bu çalışmada, Samsun kent merkezinde bulunan 17030 no’lu meteoroloji istasyonundan 1938-2020 dönemi için aylık yağış verileri ve Standart Yağış İndeksi (SPI) ile Standartlaştırılmış Yağış Evapotranspirasyon İndeksi (SPEI) kullanılarak kuraklık analizi yapılmıştır. Çalışmanın ilk kısmında, iklim değişikliğinin yağış, sıcaklık, nispi nem ve buharlaşma verileri üzerindeki etkileri %95 güven aralığına göre incelenmiştir. Sıcaklık verisinde artan bir eğilim söz konusu iken, diğer verilerinde herhangi bir eğilim gözlemlenmemiştir. SPI ve SPEI arasında 1 aylık zaman serilerinde güçlü bir ilişki elde edilirken, 3-,6-, 9-, 12 ve 24 aylık zaman serilerinde bu ilişki güçlü olsa da R2 değerlerinde azalma olduğu gözlemlenmiştir. Samsun ili için kuraklık kategorileri arasında en fazla normal kuraklık görülmesine rağmen, çok şiddetli kuraklık kategorisi ise en az paya sahiptir. İki indeks arasında çok az farklılık gözlemlenmiştir. Ancak, SPI çok şiddetli kuraklık kategorisinde, SPEI indeksine göre daha hassas sonuçlar verdiği tespit edilmiştir. İki indeks ile elde edilen bulgulara göre, 1950 yılında hem kuraklık süresi bakımından hem de kuraklık şiddeti bakımından uzun süren bir kuraklık gözlemlenmiştir. Çalışmanın sonuçları, Samsun ili kuraklık risk değerlendirmesi açısından önem taşımaktadır.
BODRUM KATLARDA RİJİT BODRUM PERDE TASARIMININ YAPI DAVRANIŞI ÜZERİNDEKİ ETKİSİ
Authors: ÜNSAL İsmail , ŞAHAN M. Fatih
Published: September 2022

Bu çalışma, binaların bodrum katlarında sırasıyla 1, 2 ,3 ve 4 cepheye yerleştirilen bodrum perdelerinin ve hiç bodrum perdesi olmamasının yapı davranışına etkisini araştırmaktadır. Bu amaçla ele alınan 13 katlı betonarme bir konut binası için Türkiye Bina Deprem Yönetmeliğinde (TBDY-2018) verilen dört farklı taşıyıcı sistem tipi esas alınmıştır. Salt çerçeveli, salt perdeli ve plandaki perde oranı düşük ve yüksek olan iki adet çerçeveli-perdeli taşıyıcı sistem olmak üzere dört farklı taşıyıcı sistem tipi ve beş farklı bodrum perde yerleşim durumu için toplam yirmi adet model hazırlanmıştır. Modeller arasındaki davranış farkları; perde taban momentlerindeki, eşdeğer deprem kuvvetleri, yapı rijitliği için yapı periyotları ve deprem deplasmanları, burulma düzensizliği, hasar durumları, radye tasarımı ve maliyet gibi parametreler üzerinden değerlendirilmiştir. Dört cephede perde yerleştirilerek oluşturulan rijit bodrum katın kritik kattaki hasar miktarını artırdığı tüm taşıyıcı sistem tiplerinde gözlenmiştir. Tüm modeller için kritik katlardaki en az hasar bodrum perdesi olmayan modellerinde görülmüştür. Tüm modellerde en yüksek radye temel maliyeti bodrum perdesiz modeller için elde edilirken en düşük radye temel maliyeti ise 4 cepheden bodrum perdeli olan rijit bodrum kata sahip modeller için elde edilmiştir. Radye temel ve üstyapı maliyetleri birleştirilerek elde edilen toplam kaba yapı maliyetleri ise tüm modellerde oldukça yakın sonuçlar vermiştir.
Gömülü ve Sarıcı Öznitelik Seçim Yöntemleri Kullanılarak Akciğer Rahatsızlıklarının Tespiti
Authors: ENGİN Mustafa Alptekin , ARAS Selim
Published: September 2022

Son yıllarda biyomedikal sinyal işleme alanındaki gelişmelere rağmen, akciğer rahatsızlıklarının tespiti üzerine hızlı ve yüksek doğrulukta çalışan teşhis sistemlerine duyulan ihtiyaç artmaktadır. Yapılan çalışmada fiziki muayene ile 94 farklı kişiden, solunum döngülerinin otomatik olarak tespit edilmesiyle elde edilen 150 adet normal ve 444 adet normal olmayan akciğer sesleri veri tabanı olarak kullanılmıştır. Sınıflandırma işleminde öznitelik olarak frekans ve zaman bölgesinde 12 farklı yöntem uygulanmıştır. Tüm veriler %80 eğitim %20 test aşamasında kullanılacak şekilde ikiye bölünmüştür. Elde edilen öznitelikler gömülü ve sarıcı öznitelik seçim yöntemleri kullanılarak değerlendirilmiştir. Bu yöntemler; özyinelemeli öznitelik eliminasyonu, uyarlanabilir yapı öğrenimi ile öznitelik seçimi, bağımlılık kılavuzlu denetimsiz öznitelik seçimi, sıralı yerellik ile denetimsiz öznitelik seçimi, içbükey küçültme yoluyla öznitelik seçimi, en küçük mutlak büzülme ve seçim operatörü öznitelik seçim yöntemleri olarak isimlendirilmektedir. İncelenen bu öznitelikler doğrusal destek vektör makineleri, k en yakın komşuluk, karar ağaçları ve naive bayes yöntemleri ile sınıflandırılmıştır. Sonuç olarak öznitelik sayısının sınırlandırılmadığı durum için, özyinelemeli öznitelik eliminasyonu yönteminin k en yakın komşuluk sınıflandırma ile beraber kullanıldığı durum için %97,3 doğruluk değerindeki başarıma ulaşılmaktadır. Öznitelik sayısının üç ile sınırlandırıldığı durumda ise uyarlanabilir yapı öğrenimi ile öznitelik seçimi yönteminin karar ağaçları yöntemi ile beraber kullanılması ile %91,4 değerinde başarıma ulaşılmıştır.
NESNELERİN İNTERNETİ (IOT) UYGULAMALARI İÇİN POLARİZASYONDAN BAĞIMSIZ METAMALZEME EMİCİ TABANLI ENERJİ HASATLAMA
Authors: İNCESU DOKUMACI Ayşe , KARAASLAN Muharrem , ÖZKANER Vedat
Published: September 2022

Bu çalışmada Nesnelerin İnterneti (IoT) için metamalzeme emici tabanlı enerji hasatlayıcı önerilmiştir. Önerilen metamalzeme yapının ön yüzeyi bakır rezonatör ile dielektrik malzemeden ve arka yüzey de tamamen bakırdan oluşmaktadır. Önerilen metamalzeme tabanlı emici 2.44 GHz ve 4.33 GHz mikrodalga frekanslarında çalışmaktadır. Bu çalışmada enine elektrik (TE), enine manyetik (TM) ve enine elektromanyetik (TEM) modları sayısal olarak incelenmiş ve emilim değerleri karşılaştırılmıştır. Bu çalışmada Sonlu Entegrasyon Tekniği (FIT) tabanlı simülasyon programı kullanılmış ve FIT tabanlı simülasyon sonuçlarına göre 2.44 GHz'de emilim tepe noktası yaklaşık %90 ve 4.33 GHz frekansta ise emilim tepe noktası %98'dir.Sunulan yapının sinyal emici özelliği sayesinde hapsolan elektromanyetik enerji yerleştirilen dirençler üzerinden elektrik enerjisine dönüştürülmüştür. Böylece önerilen metamalzeme emici tabanlı enerji hasatlayıcı yapı ile elektromanyetik dalgaların enerji emilimi ve dönüşümü daha küçük yapılarla daha verimli sonuçlar sağlanmıştır.
The Development and Preliminary Evaluation of an Internet-Based Self-help Intervention for Social Anxiety Disorder with Videoconferencing Therapist Support
Authors:   , Gershkovich Marina , Herbert James D
Published: September 2022

Social anxiety disorder (SAD) is one of the most prevalent psychiatric disorders in the United States. Although evidenced-based behavioral treatments are available, less than 20% of those with SAD receive treatment (Grant et al., 2005). The disparity between the number of individuals affected and those obtaining treatment is due to a number of factors, including limited accessibility to therapists practicing evidence-based interventions such as cognitive behavior therapy, geographic factors, and financial cost. Internet-based interventions may be utilized to overcome some of these barriers. Guided Internet-based therapeutic interventions have been demonstrated to be effective for social anxiety (e.g., Andersson et al., 2006). The optimal role (if any) of the therapist in such programs, including the amount of therapist time necessary for effective treatment, remains unclear. The purpose of this pilot study was to develop a novel Internet self-help CBT intervention and to assess the preliminary efficacy and acceptability of the program with minimal therapist support delivered through a common videoconferencing platform, for the treatment of SAD in adults. The intervention program is derived from an acceptance-based CBT program that utilizes traditional behavioral interventions (e.g., exposure) within the context of a model emphasizing mindfulness and psychological acceptance. Thirteen participants received the Internet-based self-help intervention consisting of eight weekly modules, and a brief weekly videoconferencing therapist check-in. Participants were assessed at pre-treatment, mid-treatment, and post-treatment on both outcome and process measures. Participants rated the treatment program as highly acceptable. The results indicate that participants experienced a significant reduction in SAD symptoms and improvements in functioning and quality of life. Implications and future directions are discussed.
An evaluation of daily weight monitoring as a method of weight gain prevention
Authors:   , Katterman Shawn Nicole , Lowe Michael R
Published: September 2022

Objective: Despite significant efforts to reduce obesity, there remains a great need to address the problem on a population level. Thus, it is imperative that interventions be developed that are cost-effective and can be widely disseminated. One group of individuals who appear to be at high risk for weight gain is young adults. In the past, daily weight monitoring has shown a preventative effect in normal weight college students. The current study aimed to replicate and extend these findings by testing the effects of weight monitoring in normal and overweight young adults. Method: College females in the normal and overweight range were randomly assigned to an assessment-only control group, or to an intervention group who monitored their weight daily for 8 weeks and received simple online feedback about their weight trajectory. Results: No statistically significant effect of group assignment on weight change was observed, however, the control group gained more weight on average and a significant effect may have been detected with a larger sample. Of those who gained more than 1 kg, there were no significant differences in which group they had been assigned to. No significant changes in cognitive or behavioral variables were observed as a result of daily weighing. A measure of healthy eating behaviors at baseline was the only measure associated with less weight gain across conditions. Discussion: Though the current study did not reveal a statistically significant effect of daily weight monitoring on weight gain prevention, the intervention was also not found to be harmful in normal or overweight individuals. Individuals with the highest weight gains were equally distributed across conditions, suggesting that for individuals who are most susceptible, daily weight monitoring may not be sufficient to prevent weight gain. Future research with larger samples is needed to fully elucidate the effects of weight monitoring and how it might be combined with other interventions for those most at risk for future weight gain.