Reviewerly/exHarmony huggingface dataset:
Revisiting Hyperparameter Tuning with Differential Privacy

Hyperparameter tuning is a common practice in the application of machine learning but is a typically ignored aspect in the literature on privacy-preserving machine learning due to its negative effect on the overall privacy parameter. In this paper, we aim to tackle this fundamental yet challenging problem by providing an effective hyperparameter tuning framework with differential privacy. The proposed method allows us to adopt a broader hyperparameter search space and even to perform a grid search over the whole space, since its privacy loss parameter is independent of the number of hyperparameter candidates. Interestingly, it instead correlates with the utility gained from hyperparameter searching, revealing an explicit and mandatory trade-off between privacy and utility. Theoretically, we show that its additional privacy loss bound incurred by hyperparameter tuning is upper-bounded by the squared root of the gained utility. However, we note that the additional privacy loss bound would empirically scale like a squared root of the logarithm of the utility term, benefiting from the design of doubling step.
Circling Back to Recurrent Models of Language

Just because some purely recurrent models suffer from being hard to optimize and inefficient on today's hardware, they are not necessarily bad models of language. We demonstrate this by the extent to which these models can still be improved by a combination of a slightly better recurrent cell, architecture, objective, as well as optimization. In the process, we establish a new state of the art for language modelling on small datasets and on Enwik8 with dynamic evaluation.
A family of mixture models for beta valued DNA methylation data

As hypermethylation of promoter cytosine-guanine dinucleotide (CpG) islands has been shown to silence tumour suppressor genes, identifying differentially methylated CpG sites between different samples can assist in understanding disease. Differentially methylated CpG sites (DMCs) can be identified using moderated t-tests or nonparametric tests, but this typically requires the use of data transformations due to a lack of appropriate statistical methods able to adequately account for the bounded nature of DNA methylation data. We propose a family of beta mixture models (BMMs) which use a model-based approach to cluster CpG sites given their original beta-valued methylation data, with no need for transformations. The BMMs allow (i) objective inference of methylation state thresholds and (ii) identification of DMCs between different sample types. The BMMs employ different parameter constraints facilitating application to different study settings. Parameter estimation proceeds via an expectation-maximisation algorithm, with a novel approximation in the maximization step providing tractability and computational feasibility. Performance of BMMs is assessed through thorough simulation studies, and the BMMs are used to analyse a prostate cancer dataset. The BMMs objectively infer intuitive and biologically interpretable methylation state thresholds, and identify DMCs that are related to genes implicated in carcinogenesis and involved in cancer related pathways. An R package betaclust facilitates widespread use of BMMs.
Quantum Walker in Presence of a Moving Detector

In this work, we study the effect of a moving detector on a discrete time one dimensional Quantum Random Walk where the movement is realized in the form of hopping/shifts. The occupation probability $f(x,t;n,s)$ is estimated as the number of detection $n$ and amount of shift $s$ vary. It is seen that the occupation probability at the initial position $x_D$ of the detector is enhanced when $n$ is small which is a quantum mechanical effect but decreases when $n$ is large. The ratio of occupation probabilities of our walk to that of an Infinite walk shows a scaling behavior of $\frac{x_D^2}{n^2}$. It shows a definite scaling behavior with amount of shifts $s$ also. The limiting behaviors of the walk are observed when $x_D$ is large, $n$ is large and $s$ is large and the walker for these cases approach the Infinite Walk, The Semi Infinite Walk and the Quenched Quantum Walk respectively.
Probing Statistical Representations For End-To-End ASR

End-to-End automatic speech recognition (ASR) models aim to learn a generalised speech representation to perform recognition. In this domain there is little research to analyse internal representation dependencies and their relationship to modelling approaches. This paper investigates cross-domain language model dependencies within transformer architectures using SVCCA and uses these insights to exploit modelling approaches. It was found that specific neural representations within the transformer layers exhibit correlated behaviour which impacts recognition performance. Altogether, this work provides analysis of the modelling approaches affecting contextual dependencies and ASR performance, and can be used to create or adapt better performing End-to-End ASR models and also for downstream tasks.
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning

Graph representation learning is a fundamental research issue and benefits a wide range of applications on graph-structured data. Conventional artificial neural network-based methods such as graph neural networks (GNNs) and variational graph auto-encoders (VGAEs) have achieved promising results in learning on graphs, but they suffer from extremely high energy consumption during training and inference stages. Inspired by the bio-fidelity and energy-efficiency of spiking neural networks (SNNs), recent methods attempt to adapt GNNs to the SNN framework by substituting spiking neurons for the activation functions. However, existing SNN-based GNN methods cannot be applied to the more general multi-node representation learning problem represented by link prediction. Moreover, these methods did not fully exploit the bio-fidelity of SNNs, as they still require costly multiply-accumulate (MAC) operations, which severely harm the energy efficiency. To address the above issues and improve energy efficiency, in this paper, we propose an SNN-based deep generative method, namely the Spiking Variational Graph Auto-Encoders (S-VGAE) for efficient graph representation learning. To deal with the multi-node problem, we propose a probabilistic decoder that generates binary latent variables as spiking node representations and reconstructs graphs via the weighted inner product. To avoid the MAC operations for energy efficiency, we further decouple the propagation and transformation layers of conventional GNN aggregators. We conduct link prediction experiments on multiple benchmark graph datasets, and the results demonstrate that our model consumes significantly lower energy with the performances superior or comparable to other ANN- and SNN-based methods for graph representation learning.
Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing

This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints given in a scene graph. A typical natural scene contains several objects, often exhibiting visual relationships of varied complexities between them. These inter-object relationships provide strong contextual cues toward improving grounding performance compared to a traditional object query-only-based localization task. A scene graph is an efficient and structured way to represent all the objects and their semantic relationships in the image. In an attempt towards bridging these two modalities representing scenes and utilizing contextual information for improving object localization, we rigorously study the problem of grounding scene graphs on natural images. To this end, we propose a novel graph neural network-based approach referred to as Visio-Lingual Message PAssing Graph Neural Network (VL-MPAG Net). In VL-MPAG Net, we first construct a directed graph with object proposals as nodes and an edge between a pair of nodes representing a plausible relation between them. Then a three-step inter-graph and intra-graph message passing is performed to learn the context-dependent representation of the proposals and query objects. These object representations are used to score the proposals to generate object localization. The proposed method significantly outperforms the baselines on four public datasets.
Driving innovation through project based learning: A pre-university STEAM for Social Good initiative

The Covid pandemic is a clarion call for increased sensitivity to the interconnected nature of social problems facing our world today. A future-oriented education on critical issues, such as those outlined in the United Nations Sustainable Development Goals (UN SDGs) and designing potential solutions for such problems is an imperative skill that must be imparted to children to help them navigate their future in today's unpredictable world. Towards this goal, we have been conducting 3.5 month-long mentoring programs for pre-university students in India to participate in a STEAM for Social Good innovation challenge conducted annually by the Government of India. Using digital and physical computing skills, we helped children explore creative solutions for social problems through a constructionist approach to learning, wherein they ideated and reflected upon the problems in their communities. The children learnt the Engineering Design Thinking process and worked in online groups of two or three, from concept to completion. Despite the constraints posed by the pandemic, they explored creative ways to think about design and innovation. They completed a variety of tasks by making, tinkering, engineering, assembling, and programming to grasp the intricate relationship between software and hardware. Subsequently, the children showcased their creative abilities through video storytelling to a panel of domain experts. In this paper, we present the children's perspective of their experiences through this journey, the evaluation metrics based on IEEE design principles, and our learnings from conducting this initiative as a university-school partnership model for 84 middle and high school students. The aspirational intent of this initiative is to make the children better social problem solvers and help them perceive social problems as opportunities to enhance life for themselves and their communities.
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters

Adapter-tuning is a paradigm that transfers a pretrained language model to downstream tasks by adding and tuning a small number of new parameters. Previously proposed adapter architectures are all feed-forward neural networks. In this paper, we investigate the effectiveness of using tiny-attention -- i.e., attention with extremely small per-head dimensionality -- as adapters. Our tiny-attention adapter learns to modify the hidden states at each position directly conditioned on the hidden states at all the other positions, which is missed by the previously proposed adapters. Moreover, we view its multiple attention heads as a mixture of experts and propose to average their weights during deployment, which further reduces its inference computation cost. On the GLUE benchmark, our tiny-attention adapter outperforms the other parameter-efficient transfer learning methods as well as full fine-tuning while only updating 0.05% of the parameters. On the FewGLUE benchmark, its performance is comparable to that of GPT-3 and PET.
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS

Deep learning models for semantic segmentation are prone to poor performance in real-world applications due to the highly challenging nature of the task. Model uncertainty quantification (UQ) is one way to address this issue of lack of model trustworthiness by enabling the practitioner to know how much to trust a segmentation output. Current UQ methods in this application domain are mainly restricted to Bayesian based methods which are computationally expensive and are only able to extract central moments of uncertainty thereby limiting the quality of their uncertainty estimates. We present a simple framework for high-resolution predictive uncertainty quantification of semantic segmentation models that leverages a multi-moment functional definition of uncertainty associated with the model's feature space in the reproducing kernel Hilbert space (RKHS). The multiple uncertainty functionals extracted from this framework are defined by the local density dynamics of the model's feature space and hence automatically align themselves at the tail-regions of the intrinsic probability density function of the feature space (where uncertainty is the highest) in such a way that the successively higher order moments quantify the more uncertain regions. This leads to a significantly more accurate view of model uncertainty than conventional Bayesian methods. Moreover, the extraction of such moments is done in a single-shot computation making it much faster than Bayesian and ensemble approaches (that involve a high number of forward stochastic passes of the model to quantify its uncertainty). We demonstrate these advantages through experimental evaluations of our framework implemented over four different state-of-the-art model architectures that are trained and evaluated on two benchmark road-scene segmentation datasets (Camvid and Cityscapes).
Average Mixing in Quantum Walks of Reversible Markov Chains

The Szegedy quantum walk is a discrete time quantum walk model which defines a quantum analogue of any Markov chain. The long-term behavior of the quantum walk can be encoded in a matrix called the average mixing matrix, whose columns give the limiting probability distribution of the walk given an initial state. We define a version of the average mixing matrix of the Szegedy quantum walk which allows us to more readily compare the limiting behavior to that of the chain it quantizes. We prove a formula for our mixing matrix in terms of the spectral decomposition of the Markov chain and show a relationship with the mixing matrix of a continuous quantum walk on the chain. In particular, we prove that average uniform mixing in the continuous walk implies average uniform mixing in the Szegedy walk. We conclude by giving examples of Markov chains of arbitrarily large size which admit average uniform mixing in both the continuous and Szegedy quantum walk.
Along Similar Lines: Local Obstacle Avoidance for Long-term Autonomous Path Following

Visual Teach and Repeat 3 (VT&R3), a generalization of stereo VT&R, achieves long-term autonomous path-following using topometric mapping and localization from a single rich sensor stream. In this paper, we improve the capabilities of a LiDAR implementation of VT&R3 to reliably detect and avoid obstacles in changing environments. Our architecture simplifies the obstacle-perception problem to that of place-dependent change detection. We then extend the behaviour of generic sample-based motion planners to better suit the teach-and-repeat problem structure by introducing a new edge-cost metric paired with a curvilinear planning space. The resulting planner generates naturally smooth paths that avoid local obstacles while minimizing lateral path deviation to best exploit prior terrain knowledge. While we use the method with VT&R, it can be generalized to suit arbitrary path-following applications. Experimental results from online run-time analysis, unit testing, and qualitative experiments on a differential drive robot show the promise of the technique for reliable long-term autonomous operation in complex unstructured environments.
AN ANALYSIS OF SENTENCE STRUCTURE ERRORS IN ESSAY WRITINGS COMMITTED BY SOPHOMORES AT CAN THO UNIVERSITY, VIETNAM

This study investigates the common sentence structure errors and the source of those errors, focusing on the four types of sentences such as simple, compound, complex, and compound-complex. The data source for this study was collected from 80 writings composed by EFL second-year students. Simultaneously, the researchers designed an interview to explore the causes of errors, from which some resolutions were proposed. The findings revealed that there were seven (7) types of errors in terms of sentence structure: (1) Dangling Modifier, (2) Squinting Modifier, (3) Illogical Sentence, (4) Fragmented Sentence, (5) Run-on Sentence, (6) Inappropriate Coordinating Conjunction, and (7) Inversion of Subject-Verb. Those errors were caused by mother tongue influence, teaching approaches and materials, lack of grammatical knowledge and others. This research contributes to the discipline of linguistics by giving out several possible solutions that can help learners to prevent in their compositions.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0954/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
The Implementation of Project Based Learning in Designing Ecotourism Learning Media

The purpose of this study is to describe the implementation of project-based learning in designing Ecotourism learning media and to asses students perception in project based learning application. The study involves 24 respondents of the 6th semester students of English Education Study Program, Khairun University. This study administered questionnaires and evaluation test of writing and speaking as the instrument to collect the data. To analyze the data, this research employed mixed method of quantitative and qualitative descriptive analysis. The result of the analysis of students’ perceptions was described with a likert scale, while students’ achievement from the implementation of Project Based Learning was evaluated by their presentation test. The result of the study showed that 24 respondents obtained 84.72 in their evaluation of writing and speaking test. It is in the category of very good; while from students’ understanding on learning process of a project were 95.14. The suitability of the content in the English for ecotourism course is also in line with project-based learning where there is conformity in the teaching and learning process and was followed by a value of 82.92; while 64.58 score by participants that acknowledge the project based learning increased their skills, motivation and mastery of the material and fostered their learning. In terms of the implementation, students obtained 83.00 of their overall score from presentation test.
The Design and Implementation of a Libyan Salary Mobile Application using Flutter

Mobile applications have become widespread in many aspects of our daily lives. Many people do most of their work via their phones, such as online banking, shopping, etc. Due to many individuals being unaware of their salary details, this paper presents a design and implementation of a Libyan salary application” الراتب” using Flutter (cross-platform). The application allows users to know details of their salaries, including bonuses and deductions according to the salary schedule of each ministry or sector. The application currently contains salary schedules for ten ministries. The implementation consists of the following four steps: data collection of laws and payroll tables for each ministry to use as a local database for the salary application, design of friendly-use application screens using Flutter, integration of the mobile application with Firebase) backend platform by Google), and finally, preparing and deploying an Android and web version of the salary application. The results that were obtained from reviewers in the Google Play Store were satisfactory. There is an increasing local demand to add other ministries to the application. The citizens of the local community encourage providing such a service, which is considered the first of its kind. The application reached more than 100k downloads in less than a year, and the average rating is 4.5 out of 5 in the Google Play Store at the time of writing this paper.
The Impact of Communication Skills on Understanding Learning Outcomes for Nursing Students

Communication is an important key in everyday life. By communicating we can interact with the people around us. Communication is needed in daily activities even it is a form of social and interpersonal interaction process in human life. Communication skills are a soft skill that can determine a person's success, so they need to be taught at all levels of education, because communication skills are vital skills in the development of human resources in forming superior and characterized humans. As a nurse, they need communication skill to communicate with their patients to make it clear about their health. This study is quasi-experimental research. Research subjects are about 68 nursing students. The results showed that there was no influence between student communication skills on learning outcomes for nursing students.
Quantum-classical software for drug prescription simulation in aging people

Purpose Aging often leads to the presence of comorbidities and chronic diseases.As a result, older people are often polymedicated, taking 5 or more drugs on a regular basis.This means that, when a new illness or physical problem appears, it is very difficult to prescribe the most appropriate medicine to treat them due to possible reactions, adverse effects, contraindications, etc. Personalized medicine tries to address this situation by analyzing the best possible combination of medication to treat each person's conditions.Method Pharmacogenetics is particularly relevant in the field of personalized medicine and medication.This field searches to prescribe drugs according to the effects of these drugs, the patient's history, and the genetic and physiological factors of the patient.Current software is useful to identify potential problems and facilitate the individualisation of the administration of certain drugs.However, providing safe and effective drug therapy with this software is a rather computationally complex problem for several reasons.Aging people take multiple medications and treatments throughout their lives.Therefore, there is a large amount of historical data to consider that is currently unmanageable with current tools, and linking this data to genetic information is computationally very complex (Dash, S. et al., 2019).The new discipline of quantum computing can be used to address this complexity.The arrival of quantum computing has brought a new paradigm in computing and software engineering, unlocking new horizons in the applications of computation to solve problems (Zhao, J., 2020).Quantum computing not only makes it possible to solve problems in much less time than classical computers but also to use its computational power to tackle problems beyond the scope of classical computing as we know it (MacQuarrie, E. R. et al., 2020).Like conventional software, quantum software will be a fundamental part of the evolutionary process of this technology.However, there is still a lot of research to be done in the development of this new emerging technology (J.Rojo et al., 2020).Results and Discussion Hybrid architectures allow the combination of classical and quantum systems.In this work we present a system that allows healthcare professionals to use a classical system to analyze a patient's situation.This is transferred to a quantum system in which the patient's genetic information, medication history, etc. is analyzed and represented by a data model (Figure 1a).Therefore, the obtained results can be interpreted by healthcare professionals, and simulations can be performed using quantum computing in a transparent way (Figure 1b).