Reviewerly/exHarmony huggingface dataset:
Improving Knowledge Distillation Using Data Augmentation

Knowledge Distillation (KD) is a type of transfer learning where a large, pre-trained model (Teacher Model) transfers knowledge to a smaller, simpler model (Student Model) to make the latter more efficiently trainable.Knowledge distillation (KD) can effectively transfer the knowledge of a teacher model to a student model in image classification tasks.However, the results of KD can vary depending on the specific method and approach used, thus a standardized improvement method is necessary.Data Augmentation techniques are powerful performance improvement methods for image classification.In this paper, we propose a standardized approach to improving knowledge distillation using various Data Augmentation techniques based on the α value.The results showed that Data Augmentation techniques provided a significant performance improvement in the Knowledge Distillation model.We demonstrated that as the strength of data augmentation increases, the performance of knowledge distillation (KD) also increases proportionally.And It seems that the α value does not have a significant impact on the performance improvement of knowledge distillation (KD).
Accelerating Finite-Field and Torus FHE via Compute-Enabled (S)RAM

Fully Homomorphic Encryption (FHE) allows outsourced computation on clients’ encrypted data while preserving data privacy. FHE’s high computational intensity incurs high overhead from data transfer with hardware such as CPU, GPU, and FPGA, due to the inherent separation between computing and data. To overcome this limitation, Compute-Enabled RAM (CE-RAM) has been explored; however, prior work using CE-RAM to accelerate FHE only explores a simple implementation of a finite-field FHE scheme and did not explore algorithmic optimizations. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this paper, we investigate CE-RAM acceleration FHE more deeply, implementing both the finite-field B/FV and torus-based TFHE cryptosystems in CE-RAM with common FHE optimizations. This is the first work to explore using CE-RAM to accelerate TFHE. For B/FV, we explore parameter-specific algorithmic optimizations specifically designed for CE-RAM friendliness. We evaluate our implementation as compared to prior work in CE-RAM FHE acceleration and other hardware acceleration strategies. We demonstrate speedups of up to 784x for B/FV homomorphic multiplication and 38x for TFHE bootstrapping as compared to CPU implementations. We also discuss the overhead of CE-RAM for FHE on energy and area consumption, showing comparable or improved performance as compared to other work or hypothetical near-memory accelerators.
teex: A toolbox for the evaluation of explanations

We present teex, a Python toolbox for the evaluation of explanations. teex focuses on the evaluation of local explanations of the predictions of machine learning models by comparing them to ground-truth explanations. It supports several types of explanations: feature importance vectors, saliency maps, decision rules, and word importance maps. A collection of evaluation metrics is provided for each type. Real-world datasets and generators of synthetic data with ground-truth explanations are also contained within the library. teex contributes to research on explainable AI by providing tested, streamlined, user-friendly tools to compute quality metrics for the evaluation of explanation methods. Source code and a basic overview can be found at github.com/chus-chus/teex, and tutorials and full API documentation are at teex.readthedocs.io.
Photoswitchable quantum electrodynamics in a hybrid plasmonic quantum emitter

The design and preparation of quantum states free from environmental decohering effects is critically important in the development of on-chip quantum systems with robustness. One promising strategy is to harness quantum state superposition to construct decoherence-free subspace (DFS), which is termed dark state. Typically, the excitation of dark states relies on anti-phase-matching on two qubits and the inter-qubit distance is of wavelength scale, which limits the development of compact quantum chips. In this work, we propose a hybrid plasmonic quantum emitter composed of strongly correlated quantum emitters intermediated by a plasmonic nanocavity. Through turning the plasmonic loss from drawback into advantage, we break the anti-phase-matching rule by rapidly decaying the superposed bright state and preparing a sub-100 nm dark state with decay rate reduced by 3 orders of magnitudes. More interestingly, the dark state can be optically switched to a single-photon emitter with enhanced brightness through photon-blockade, with the quantum second order correlation function at zero delay showing a wide range of tunability down to 0.02.
Retracted: Learning Enhanced Feature Responses for Visual Object Tracking

[This retracts the article DOI: 10.1155/2022/1241687.].
Survey of Electrical Ambient Intelligence (AmI) Devices Built for Environmental Monitoring

This paper presents a thorough survey of the evolution of electrical devices used for environmental monitoring. The technologies that make up these equipments are carefully investigated, several efficient solutions of devices built for this type of measurements are proposed and compared. The purpose of this study is to describe the characteristics of devices for environmental monitoring and to highlight further challenges, especially regarding the field of software engineering.
Retracted: Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

[This retracts the article DOI: 10.1155/2021/2794588.].
Retracted: Study on Correlation between Body Cell Mass Index and Cognitive Impairment in Hemodialysis Maintaining Patients: A Cross-Sectional Study

[This retracts the article DOI: 10.1155/2022/9023562.].
Formation of Specific Competences in the Engineering Career by Educational Software - methodological landmarks applied to the “Initiation in research” discipline

Since a curricular concept oriented towards the formation of competences is very topical, the informational and educational technologies are characterized as a transdisciplinary and interdisciplinary field, which provides tools for the elaboration of educational applications, which can eventually be implemented in technical disciplines. Since both teachers and students often see only a part of the TIC (Information and Communication Technologies) possibilities, we believe it is necessary to research and bring to the attention of the pedagogical opinion the formative valences of modern technologies, which are no longer just an element of impact, but tend to already become everyday tools with perspective in the formation of transversal competences in most technical disciplines. In the context of formative-productive education, in this paper, we are concerned with the study of engineering disciplines through traditional strategies and methods, the formation of positive attitudes of students towards the immanent values of engineering and their correlation with the techniques and strategies for the development of informational and communication skills. Thus, there is a need to use the computer as a tool for learning, taking into account the fact that it is precisely this type of study that is of great value from a functional point of view. Specifically, the aim of this research is to define the methodological configuration of the development of educational software (SE) in the modern educational system through the prism of the transdisciplinary and interdisciplinary character of informatics and TIC, on the example of the discipline Initiation in research in the University of Pitesti, through the act of implementing software products, profiling the contribution on the formation of transversal competences to the aforementioned discipline.
Chaotic American zebra search optimization algorithm for benchmark challenges

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.
Databases restoration processes in intellectual property organizations

This document represents the original research and analysis of the impact of the use of advanced technologies for data backup and restore for Informix type databases in cases of disasters in intellectual property organizations. Along with the economic growth, a permanent increase in the volume of computerized data was noted, thus showing the need to implement high-performance backup systems in different storage environments. In this context, it was concluded that it is necessary to ensure an improvement of the systems and applications management through which the processes of restoring these volumes of data are ensured. This research is argued on the basis of experiments carried out and is a personal point of view of the authors. The experimental research was carried out in the available computer systems with the aim of streamlining the restoration processes and also to test the availability of permanent data on the computer.
CycleSAR: SAR Image Despeckling as Unpaired Image-to-Image Translation

Synthetic Aperture Radar (SAR) is a cutting-edge remote sensing technology that offers a unique perspective on the Earth's surface through its advanced microwave imaging, providing valuable insights into various aspects of the environment. However, SAR images are impacted by the speckle phenomenon, which acts as noise, hindering accurate interpretation of the scene and presenting a major challenge for SAR image analysis and understanding. Deep Learning has emerged as a powerful solution for despeckling SAR images, but acquiring large amounts of labeled data for training is a significant obstacle as obtaining ground truth in the SAR domain is not feasible. The proposed method overcomes this limitation with a novel unsupervised approach to single-look SAR image despeckling. Our method, CycleSAR, leverages the power of cycle-consistent generative adversarial networks (CycleGANs) to formulate the despeckling problem as an unpaired image-to-image translation, by effectively bypassing the need for ground truth data. Additionally, our method not only effectively reduces the speckle in single-look SAR images, but also enables, by construction, the simultaneous learning of a generative model to generate realistic speckled realizations of multi-look SAR images. The addition of a conditional variational autoencoder (CVAE) further enhances the method, enabling the one-to-many generation of speckled images and leading to an overall improvement in despeckling performance. The experimental results demonstrate the remarkable capabilities of the proposed method, CycleSAR, in providing high-quality despeckling and realistic speckle realizations. CycleSAR stands apart from existing state-of-the-art methods as it does not rely on data simulation or make any assumptions about the speckle distribution.
Deformable Fractional Filters

This paper introduces Deformable Fractional Filters (DFFs) for Convolutional Neural Networks (CNNs). DFFs enhance the efficiency of conventional Deformable Convolutional Filters by introducing a compression mechanism rooted in techniques from fractional calculus. Concretely, our method reduces the parameter overhead requirement of convolutional filters by replacing the kernel with a fractional approximation, which can be trained using only three parameters – regardless of the kernel size. DFFs present a compelling use case for the compression of networks that require large kernel sizes. To demonstrate the benefits of DFFs, we report experimental results across a diverse set of computer vision problem domains, including classification, semantic segmentation, and medical imaging. Our experiments illustrate the favorable performance and regularization properties presented by DFFs in comparison with other baseline CNNs.
Adaptive Linear Regression for Data Stream

The approaches that currently constitute the state-of-the-art for the task of regression on continuous data streams usually involve ensembles, regression trees, and regression rules. They have been found to work very well for certain situations but generally consume computational resources to a prohibitive extent. In this paper, we propose a new method based on an ensemble of linear regressions for the regression task adapted to handle continuous data streams. The technique has been named Adaptive Linear Regression (ALR). The algorithm combines strategies that contribute to high prediction accuracy using (i) distinct sliding window sizes for training each ensemble element, and (ii) a dynamic regressor selection method for final ensemble voting. After an extensive experimental study, ALR was found to exhibit high predictive performance and outperform state-of-the-art ensemble regressors on data streams for real and synthetic datasets. Moreover, it exhibits low processing time in its parallel version and is faster than ARF-Reg in its serial version. The paper also presents an analysis of how the choice of sliding window size for training favors accuracy.
HQProtoPNet: An Evidence-Based Model for Interpretable Image Recognition

In image recognition, improving the interpretability of the recognition model can help people understand the model better and increase the trust of human beings for model prediction. The prototype-based interpretable model is a self-explanatory image recognition model that simulates the evidence reasoning used in human recognition. Each prototype is evidence that contains category features, which can help in determining the image category. Based on the prototype-based model, this paper introduces a deep interpretable network architecture called the high-quality prototypical part network (HQProtoPNet). Compared to existing work, this paper adds random erasing to enhance the picture, helping to improve prototype generation and increase model prediction. The multiple scale conversion operation is also introduced and the similarity calculation is improved to make the prototype have multiscale information and matching ability. Furthermore, the accuracy of HQProtoPNet can reach or even exceed the accuracy of several black-box models. Additionally, due to the improvement in the quality of the prototype, the model's prediction accuracy is improved by stacking without reducing the interpretability of the stacked model, which gives the model real stackability.
Compact Cross-Reparam Convolution Network for Efficient Image Super-resolution

With increasing demand for deployment on edge platforms, there is a need for efficient super-resolution methods. Linear overparameterization is widely used to enhance lightweight model performance. However, the general usage of the reparameterization technique is merging multi-branch into standard 3 × 3 convolutions, no one explores its usage in 1D-convolution, which is energy efficiency and adepts at capturing structural edge features. We propose a compact cross-reparam convolution super-resolution (CRCSR) network in a vgg-style plain architecture. We design Cross-Reparam convolution (CRC) to enhance feature representation for cross-convolution. We leverage a sparse large kernel to capture long-range information while limiting the computational costs and number of parameters. Besides, when training deeper type CRCSR, reserving and merging operations are used to build Rmparam-Cross Block (RCB) to improve the convergence of deeper networks. Experiments on super-resolution datasets of different sizes demonstrate that CRCSR has competitive restoration performance compared to state-of-the-art lightweight SR models
Domain generalization via feature disentanglement with reconstruction for pathology image segmentation

In pathology, the learned model may suffer from performance degradation due to stain variations between the training and testing datasets. To address this challenge, this paper proposes a feature disentanglement approach to learn the domain-invariant features to achieve domain generalization. The adaptive instance normalization (AdaIN)-based reconstruction is introduced to preserve the important semantic information. The generalization ability of the proposed method is further improved by using a contrastive loss function based on color augmentation to attract the domain-invariant features and repel the domain-specific features in the feature disentanglement process. The proposed method is evaluated on liver tumor and liver lipid droplet segmentation tasks. The results demonstrate that the proposed method can be applied to unseen datasets scanned by different scanners without significant performance degradation.
Application Of 3-Dimensional Modeling In Android-Based Adventure Game Applications

Role Playing Game (RPG) is a game that has complex story elements and acting that makes a person feel like a character in the game. One of the RPG editing engines is RPG Maker MV, where a 2D RPG can be a standalone program that can be played directly without the help of this program or other programs. Quests are assigned to players by non-player characters (NPCs) that the player cannot control and interact with them through dialogue. In the game mode each player can collect things into an inventory which contains the things needed to solve certain tasks during gameplay. In addition, game objects that represent learning objects have been assigned locations which can be NPCs or other objects. In addition, game objects that represent learning objects have been assigned locations which can be NPCs or other objects. Can apply 3-dimensional modeling to role-playing game applications by placing objects on characters that can walk up, down, left and right. NPC objects can be applied to weapon objects, namely axes, if gold points are collected then the NPC object will appear. It is hoped that this implementation can be a reference not only for 3-dimensional modeling in complex role-playing game applications but also for all circles as the development of Android and IOS-based RPG games.
What's Wrong With Low-Code Development Platforms? An Empirical Study of Low-Code Development Platform Bugs

Low-code development platforms (LCDPs) are increasingly being introduced and leveraged by major IT enterprises to lower the threshold and promote the efficiency of software development. Like other software systems, LCDPs are also inevitable to have bugs. The bugs in LCDPs may cause unpredictable consequences as they pose risks to all the downstream software products. However, to the best of our knowledge, there exist no studies that ever consider the bugs caused by LCDPs. To handle the LCDP bugs better, in this article, we conduct an empirical study of the characteristics of LCDP bugs by examining 974 confirmed bugs of four dominant LCDPs (i.e., OutSystems, Mendix, Appsmith, and Budibase) from both commercial and open-source domains. These bugs are analyzed from three perspectives, including bug root causes, bug symptoms, and the affected stages of LCDPs. Based on the analysis, we obtain a series of valuable findings. For example, around 60% of the bugs reside in the stage of designing and specifying the developed applications. Over 37% of the bugs lead LCDPs to behave unexpectedly but without showing explicit signs. Moreover, the bugs relevant to the incorrect graphics of user interfaces are significant due to the characteristics of LCDPs. These findings point out the guidelines, challenges, and future directions to address LCDP bugs.
Analysis of Mineral Water Distribution Using Apriori Algorithm Method

Normalization is a process used to prepare data so that it can be used to meet user needs and support subsequent processes to get better results. The image normalization application system with Resizing Image is implemented using the matlab programming language. The mechanism for making an application starts from making a GUI (Graphic User Interface) to making coding. This thesis presents a system model of image normalization using k-space transformation to get the result in the form of an image normalization display using image resizing. The creation of an image normalization system using k-space transformation was carried out using the Matlab programming language version 2021a. The results of the system functionality test show that the image normalization system using the built resizing image can be used properly.
Tracking Sediment Provenance Applying a Linear Mixing Model Approach Using R’s FingerPro Package, in the Mining-Influenced Ocoña Watershed, Southern Peru

Stream sediments record water–rock interactions in tributaries followed by fluid mixing in larger downstream catchments, but it can be difficult to determine the relative contributions of each tributary. A good way to analyze this problem is sediment fingerprinting, which allows researchers to identify the source of sediments within a basin and to estimate the contribution of each source to the watershed. Herein, we developed a workflow using the frequentist model FingerPro v1.3 to quantify the sediment source contribution in a semiarid watershed. We applied an unmixing model algorithm to an ICP-MS geochemical database containing information on 32 elements in 362 stream sediment samples. By modeling the source contributions to these mixed samples, we infer that the main sediment contribution comes from the upper portion of the catchment (61–70%), followed by the middle (21–29%) and lower (8–10%) parts, with geochemical anomalies (As and Cu) being closely related to mining sites. Results from this study can be helpful for future management decisions to ensure a better environment in this semiarid watershed.
Meet our Spring Visiting Scholars | Interview with Iara Vidal, Isabelle Dorsch, and Lisa Matthias

Iara Vidal, Isabelle Dorsch, and Lisa Matthias are the ScholCommLab’s spring Visiting Scholars. The ScholCommLab is excited to welcome three new faces to the lab this spring. Iara Vidal, Isabelle Dorsch, and Lisa Matthias will join us as Visiting Scholars in each of our two locations—Matthias and Vidal in Vancouver, Dorsch in Ottawa—to collaborate on a research project of their choosing. Vidal has a background in library science and is currently finishing her PhD in Information...