Reviewerly/exHarmony huggingface dataset:
Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge

Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can fool another model with a non-trivial probability. This gave birth to the transfer-based attack where the adversarial examples generated by a ate model are used to conduct blackbox attacks. There are some work on generating the adversarial examples from a given surrogate model with better transferability. However, training a special surrogate model to generate adversarial examples with better transferability is relatively under-explored. This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model. This trained surrogate model is named dark surrogate model (DSM). The proposed method for training a DSM consists of two key components: a teacher model extracting dark knowledge, and the mixing augmentation skill enhancing dark knowledge of training data. We conducted extensive experiments to show that the proposed method can substantially improve the adversarial transferability of surrogate models across different architectures of surrogate models and optimizers for generating adversarial examples, and it can be applied to other scenarios of transfer-based attack that contain dark knowledge, like face verification. Our code is publicly available at https://github.com/ydc123/DarkSurrogate-Model.
Ensemble Learning for Mapper Parameter Optimization

The Mapper algorithm is a technique from TDA used to create low-dimensional graph-based representations of high-dimensional data, proven effective in numerous exploratory data analysis tasks. The Mapper algorithm’s output depends on several user-chosen parameters, and selecting their values is a non-trivial choice, significantly narrowing its potential application in real-world scenarios. Research attempting to assist in selection of the parameters has been very limited to date. This paper is the first one to address the selection of Mapper’s three parameters simultaneously. The proposed idea incorporates the concept of Ensemble Learning into the Mapper algorithm. Using several datasets with known labels, we show that our method outperforms two baselines in recovering the dataset structure.
A Formal Introduction to Batch-Integrated Gradients for Temporal Explanations

eXplainable Artificial Intelligence (XAI) is at the forefront of Artificial Intelligence research. Little attention, however, has been paid to the development of XAI methods for temporal data. Current state-of-the-art methods treat instances as independent and do not utilise the time dimension. Critical fields such as Healthcare and Finance often take a temporal form, leaving a prominent gap in XAI research. To this end, we propose the utilisation and optimisation of path based methods to use the temporal nature of data for explanations. In this work we (1) Extrapolate on a new technique for explainability forming a formal introduction, based on Integrated Gradients, a technique not designed for temporal data, (2) introduce new properties for time-based explainers and give an overview of the state-of-the-art methods and their adherence to these properties, (3) provide a theoretical and empirical analysis of path based methods, (4) demonstrate explanations on real world case studies. From this, we identify the proposed method best adheres to both the proposed properties and existing properties. Similarly, we demonstrate how the introduced method outperforms state-of the-art methods in the demonstrated areas
Model-based exploration strategy to accelerate deterministic strategy algorithm training

Exploitation and Exploration in Reinforcement Learning (RL) are often in conflict, and a persistent problem in RL is finding effective methods for state space exploration. For the deterministic strategy algorithm, the exploration strategy directly affects the search process of the agent in the state space, which in turn affects the training efficiency. Model-based RL algorithms consider the environment model to be completely black-box, but when RL is applied to real-world problems, a portion of many environment models is often known. In this paper, we utilize a portion of known environment models in our exploration strategy to achieve more efficient exploration and thus accelerate the training of the agent. This is also closer to the actual situation, and often part of the environmental model we can acquire knowledge by way of a priori knowledge or learning the model. Our exploration strategy is applicable in a continuous action space environment. We propose three exploration methods that utilize partial or full environment models to achieve more efficient exploration. We use two GYM continuous action space environments and a 7-joints robotic arm environment to validate our exploration methods.
Implementation of the STEAM method, through emerging technologies such as the metaverse, to motivate high school students to investigate and use technology to solve problems in their environment

This research project originates in the municipality of Sáchica -Boyacá, Colombia where the 30 participants were students at the school "Institucion Educativa Nueva Generacion". The vast majority of Sachica's people are dedicated to farming, mostly to the cultivation of onions and tomatoes, which is their main source of income. Due to the different climatic changes in this area, agricultural activity has decreased, causing shortages in these products.The majority of young people in the region hope to explore careers outside of the agriculture industry. Unfortunately, this reality reduces the workforce of people working the land. The purpose of this research was to give technological tools to students to help them seek solutions to farming problems and to reduce production losses, thus resulting in an increase in income. Several trainings were delivered implementing the STEAM method using microcontrollers to carry out intelligent control systems for greenhouses, weather stations, and automatic irrigation. The process of the different steps and systems, which captured the interest of the students, contributed to the technological and economic development of the municipality. This project used both quantitative and qualitative analysis with a questionnaire-based evaluation to assess student performance. The highest scores were in the areas of effort, tenacity, and skill acquisition. The highest score for project assessment was for its usefulness in solving problems.
Bird’s Eye View Map for End-to-end Autonomous Driving Using Reinforcement Learning

End-to-end autonomous driving is one of the hot research topics in the field of artificial intelligence. End-to-end autonomous driving system uses neural networks to take multi-sensor data as input and output relevant parameters such as acceleration, braking force, and steering angle to control the vehicle. With the increasing complexity of sensor configurations in most autonomous vehicles, integrating information from different sensors and representing features in a unified view has become crucially important. The bird’s-eye view(BEV) has the advantage of friendly fusion of multiple sensor information into a single image and providing a comprehensive and intuitive understanding of the surrounding scene without the issues of occlusion and scale. It is particularly suitable for training decision-making tasks in autonomous driving systems. The BEV map is derived using the BEVFusion method, which leverages camera images and other sensor data captured by the multiple sensors installed on an ego vehicle. In this paper, we utilize BEV map as the input for end-to-end autonomous driving system and employ reinforcement learning algorithms to investigate the impact of the bird’s-eye view on decision-making. The results demonstrate that instances of error frames from BEV perception can have a significant impact on the reinforcement learning in end-to-end autonomous driving system.
Transforming Teachers’ Professional Development with Technology Innovation Integration in Essuekyir, Ghana

This article provides an evaluation of teachers’ technological skills enhancement, after adapting teacher professional development training, while integrating Information and Communication Technologies (ICTs) innovations into teaching methodologies in Essuekyir, a rural community in Ghana. The availability of training for teachers in Essuekyir was made feasible by deploying the Internet Backpack, a secure mobile broadband Internet connectivity tool which can be used to connect to the Internet with or without Wi-Fi or cellular network access. The Internet Backpack provides an immediate connectivity solution that allows users to be able to communicate from almost any location. The online learning resources provided by the Ghanaian Ministry of Education, at no cost, now accessible in rural Essuekyir, previously were mainly accessible by schools and teachers in urban areas. Our research finds that in Essuekyir, as an Internet connectivity device, the Internet Backpack effectively facilitates community Internet access for teachers as they learn how to adapt and use online learning systems for their, and their students, digital inclusion.
Towards Trust-Based Data Weighting in Machine Learning

In distributed environments, data for Machine Learning (ML) applications may be generated from numerous sources and devices, and traverse a cloud-edge continuum via a variety of protocols, using multiple security schemes and equipment types. While ML models typically benefit from using large training sets, not all data can be equally trusted. In this work, we examine data trust as a factor in creating ML models, and explore an approach using annotated trust metadata to contribute to data weighting in generating ML models. We assess the feasibility of this approach using well-known datasets for both linear regression and classification problems, demonstrating the benefit of including trust as a factor when using heterogeneous datasets. We discuss the potential benefits of this approach, and the opportunity it presents for improved data utilisation and processing.
Attention-guided Feature Fusion for Small Object Detection

Due to the less feature in small objects and feature loss during feature transmission, the detection of small objects is not satisfactory and is regarded as a challenging task in computer vision. Typically, multi-scale feature fusion methods are used to compensate the small object information. However, because there is a certain degree of misalignment in different scale features, traditional feature fusion methods suffer from aliasing effects, resulting in feature inconsistent deterioration, which is harmful to small target detection. Therefore, we propose an attention-guided feature alignment fusion module that utilizes features from adjacent scales to achieve efficient fusion of spatial and contextual information, alleviating the feature aliasing problem. In addition, we propose a shallow feature supplement module that uses attention mechanisms to guide small object information in low-level feature to be compensated in the bottom of the Neck without additional detection head, significantly improving the detection capability for small objections of the detector. Experiments on the MS COCO 2017 and VisDrone2019 datasets have demonstrated the superiority of our method. Specifically, compared to the baseline, we dramatically increased the AP50 and APS by 1.4 and 0.9 on COCO dataset, and the AP, AP50, and AP75 by 4.6, 4.3, and 5.2 on Visdrone2019 dataset.
Combining Degree of Interest Functions and Progressive Visualization

When visualizing large datasets, an important goal is to emphasize data that is relevant to the task at hand. A common way of achieving this is to compute the relevance of the data using degree of interest (DOI) functions, which apply a scenario-specific metric to quantify the data items according to their relevance to the users and their tasks. These DOI values can then be used to adjust the visual encoding through mechanisms like focus+context or information hiding. For datasets too large to be visualized at once, an alternative approach is to visualize it progressively in chunks, allowing analysts to reason about partial results and concluding their analysis much earlier than had they waited for all data. Combining the advantages of both approaches to tailor the visualization seems synergistic, yet, in practice turns out to be challenging, as DOI functions require the context of all data to produce useful values, requiring lengthy computations that break analysts’ flow in progressive visualization. In this paper, we propose an approach for uniting DOI functions with progressive visualization. We first introduce a new model for quantifying the user interest in analysis scenarios where the data is only partially available, by computing the interest for available data and predicting it for the rest. We then propose regression trees for implementing this approach in practice and evaluate it in benchmarks. With DOI values now available for progressive visualization as well, our approach opens the door for tailoring the visualization of large datasets to the analysis task at interactive update rates.
First Steps towards Efficient Genome Assembly on ARM-Based HPC

Exponential advances in computational power have fueled advances in many disciplines, and biology is no exception. High-Performance Computing (HPC) is gaining traction as one of the essential tools in scientific research. Further advances to exascale capabilities will necessitate more energy-efficient hardware. In this article, we present our efforts to improve the efficiency of genome assembly on ARM-based HPC systems. We use vectorization to optimize the popular genome assembly pipeline of minimap2, miniasm, and Racon. We compare different implementations using the Scalable Vector Extension (SVE) instruction set architecture and evaluate their performance in different aspects. Additionally, we compare the performance of autovectorization to hand-tuned code with intrinsics. Lastly, we present the design of a CPU dispatcher included in the Racon consensus module that enables the automatic selection of the fastest instruction set supported by the utilized CPU. Our findings provide a promising direction for further optimization of genome assembly on ARM-based HPC systems.
CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning

Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities. We proposed a similarity reward to help teach an optimal action sequence to model the step-wise nature of the human processing process explicitly. In addition, We designed an action set capable of handling multiple types of noise to construct the action space, thereby achieving successful crossmodal denoising. Extensive experiments against state-of-the-art methods on publicly available RGB, infrared, and terahertz datasets demonstrate the superiority of our method in crossmodal image denoising.
A Non-Matrix-Matched Calibration Method for In Situ Major and Trace Element Analysis of Scheelite by Nanosecond LA-ICP-MS

In this work, a reliable and robust in situ non-matrix-matched calibration method is proposed for element composition determination in scheelite samples. With external calibration against the silicate glass standard reference material NIST SRM 610, the concentrations of both major elements (Ca and W) and trace elements (Si, Fe, Mo, Y, rare earth elements, etc.) in scheelite are determined using an ArF 193 nm excimer nanosecond laser ablation-inductively coupled plasma mass spectrometer (LA-ICP-MS). Here, the ablation was performed by hole drilling under a helium (He) environment using a laser spot size of 35 μm and a laser repetition of 5 Hz, and the aerosols were then transported to a quadrupole ICP-MS by a mixture of He and make-up gas argon (Ar) with a total gas flow rate of 1.6 L/min. Results showed that there was no apparent matrix effect between the NIST SRM 610 and scheelite by this proposed method. With internal standardization against W, the obtained concentrations of CaO and WO3 were found to yield an average matrix CaO/WO3 mass fraction ratio of 0.245 (2σ = 0.003, n = 19), which agreed well with the value of 0.243 (2σ = 0.002, n = 15) from electron probe microanalysis (EPMA). Furthermore, the accuracy of trace element analyses with this proposed non-matrix-matched calibration in situ method was evaluated by comparing the concentration results with those from bulk analysis by solution nebulizer ICP-MS (SN-ICP-MS). It was found that the quantification results from LA-ICP-MS and SN-ICP-MS were comparable, in particular showing a relative concentration bias of the total ∑REE+Y contents of less than 2%. This confirmed that scheelites can be accurately analyzed in situ by LA-ICP-MS without matrix-matched calibration standards. By using this developed in situ method, the element compositions in a series of scheelite samples from different W-associated deposits in China were successfully quantified, promising further genetic process investigation and associated geologic activities of the polymetallic resources.
Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects

Deep Learning (DL) models have rapidly advanced, focusing on achieving high performance through testing model accuracy and robustness. However, it is unclear whether DL projects, as software systems, are tested thoroughly or functionally correct when there is a need to treat and test them like other software systems. Therefore, we empirically study the unit tests in open-source DL projects, analyzing 9,129 projects from GitHub. We find that: 1) unit tested DL projects have positive correlation with the open-source project metrics and have a higher acceptance rate of pull requests, 2) 68% of the sampled DL projects are not unit tested at all, 3) the layer and utilities (utils) of DL models have the most unit tests. Based on these findings and previous research outcomes, we built a mapping taxonomy between unit tests and faults in DL projects. We discuss the implications of our findings for developers and researchers and highlight the need for unit testing in open-source DL projects to ensure their reliability and stability. The study contributes to this community by raising awareness of the importance of unit testing in DL projects and encouraging further research in this area.
Linear Active Disturbance Rejection Lateral Controller for Unmanned All-Terrain Vehicle

Abstract: To address the disturbance of model uncertainty, a linear active disturbance rejection controller (LADRC) was designed for robust lateral control of unmanned all-terrain vehicle. In terms of relative motion of target node and current state, first-order lateral tracking model is established. According to the developed model, linear tracking differentiator (LTD), linear extended state observer (LESO) and linear state error feedback (LSEF) are designed in turn. LESO could observe the uncertainty of system and LSEF could compensate the uncertainty to make system robust. In order to verify the effectiveness, two typical scenarios, circle and double lane tracking, were designed for test. And the uncertainties of wheelbase and steering ratio were considered. Results illustrate that the designed LADRC can stably control the unmanned all-terrain vehicle tracking reference trajectory under both scenarios and has the advantages of small tracking error and small overshoot compared with the conventional pure tracking methods.
FROM SCHOOL TO HOME: UNDERSTANDING THE EXPERIENCES OF PARENTS AND LEARNERS IN MODULAR LEARNING

The emergence of the COVID-19 pandemic brought a major shift in the delivery of instruction. Parents and learners were caught unprepared for this situation. As such, this study aimed to describe the experiences of parents and learners in modular learning. Following a phenomenological approach, 10 parents and 30 learners from the Schools Division of the City of Batac underwent unstructured interviews via home visits, phone calls, and messenger calls. It adopted the Hycner transcription analysis methodology for the data analysis. Findings revealed that parents’ experiences could be described along four themes: multiple roles, learning management challenges, home learning strategies, and support systems. Meanwhile, learners’ experiences were revealed by these four themes: learning preferences, learning struggles, learning strategies, and learning support. Interestingly, these experiences magnify the need for attention to shared responsibility, understanding, cooperation, commitment, and compassion for modular learning to succeed in the context of a pandemic. Furthermore, the findings could serve as a basis for providing comprehensive and inclusive educational policies that facilitate the adjustments of learners and parents in a new learning setup. As such, schools can create and implement more adoptive, relevant, and inclusive institutional plans, projects, activities, and programs for the attainment of optimum learning gains.&#x0D;
Energy-aware temporal scheduling strategy of tasks for green data centers

With the development of cloud service technology, the construction of essential data is showing a trend towards large-scale development. In addition, its energy consumption is increasing day by day. Using the renewable energy to supply the electricity demand of a data center has become a common energy-saving solution and been adopted by more and more data center operators. Therefore, a temporal scheduling method of tasks is designed in this paper, which can increase the utilization rate of the renewable energy and reduce carbon emissions. The method is validated by a case study and its effectiveness is further validated by a comparative study. The numerical results confirm that the proposed solution show a good effect in the economic and environmental indicators.
An Analytical Approach for Practice-based Research of the Compositional Approach

This paper presents a process of analysis that has been developed via a practice-based, autoethnographic study of the processes of popular electronic music composition. The mediating effects of the tools of the music-maker are examined from an Ecological Approach to Perception (EAP) perspective, with particular reference to the theory of affordances. Concepts from Actor-Network Theory, such as nonhuman agency and translation, are used to build a framework that conceptualises how these mediating effects impact the processes of composition.