Reviewerly/exHarmony huggingface dataset:
Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction

Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on the diversity of data categories, overlooking the diversity of domains within the same data category. In this paper, to bridge this gap, we propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets: the single-blade dataset and the video anomaly detection dataset of blades. Compared to existing datasets, AeBAD has the following two characteristics: 1.) The target samples are not aligned and at different scales. 2.) There is a domain shift between the distribution of normal samples in the test set and the training set, where the domain shifts are mainly caused by the changes in illumination and view. Based on this dataset, we observe that current state-of-the-art (SOTA) IAD methods exhibit limitations when the domain of normal samples in the test set undergoes a shift. To address this issue, we propose a novel method called masked multi-scale reconstruction (MMR), which enhances the model's capacity to deduce causality among patches in normal samples by a masked reconstruction task. MMR achieves superior performance compared to SOTA methods on the AeBAD dataset. Furthermore, MMR achieves competitive performance with SOTA methods to detect the anomalies of different types on the MVTec AD dataset. Code and dataset are available at https://github.com/zhangzilongc/MMR.
An Application for E-Certificate Verification and Validation using Blockchain

Abstract: According to statistics from the Indian Ministry of Education, there are roughly one million graduates each year. Some of them will continue their education at high schools or university institutions while others will be prepared to find employment.
Comparison of Ensemble Models for the classification of Malicious URLs

Abstract: The increasing number of malicious URLs on the internet poses a significant threat to online security and privacy of individuals and organizations. Machine learning algorithms have been proposed as a solution to this problem, but the high volume and diversity of URLs and the constant evolution of malicious URLs pose significant challenges for researchers in this field. Ensemble models, which combine multiple models to improve the overall performance, have shown great promise in addressing these challenges. In this paper, we compare four popular ensemble algorithms for the task of classifying URLs: Random Forest, Bagging, XGBoost and AdaBoost. We use a dataset of labeled URLs and pre-process them. The performance of the models is evaluated using accuracy and run time as the evaluation metric. Our results show that all the four algorithms are able to achieve good performance with accuracy scores above 95%. The Random Forest algorithm had the highest accuracy, followed by XGBoos, Bagging and then AdaBoost. XGBoost was the fasted algorithm with runtime of 1 minute. Our study provides insight into the relative strengths of these ensemble algorithms on the task of URL classification and highlights the importance of selecting an appropriate model depending on the specific characteristics of the data and the requirements of the application.
Multiple 3D Chaotic Keys to Protect Digital Images

A simple, multipurpose, efficient, sensitive and highly qualities method of image cryptography will be introduced in this research paper. The method will be easily used to encrypt-decrypt any type of image with any size (gray and color images). The proposed method will secure the image by providing a high degree of image protection based on using a complex private key, which contains eight values, each of the has a double data type. The private key will provide the required key space to resist any hacking attack. The private key components will be used to calculate the size of the generated 3D chaotic logistic key. The private key will also be used to run two chaotic logistic map models to get two different chaotic keys base on the chaotic parameters values included in the private key. Two rounds of XORing operations will be used to generate the encrypted-decrypted image. The generated outputs of the proposed method will be sensitive to the selected value of the private key. The proposed method will be very flexible, changing the source image or/and changing the private key will not require any changes in the method algorithm. The proposed method will be tested and implemented using various image, the obtained results will be discussed to prove the features provided by the method: quality, sensitivity, security and efficiency.
Causal inference is not just a statistics problem

This paper introduces a collection of four data sets, similar to Anscombe's Quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four data sets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. The paper includes a mathematical summary of each data set, as well as directed acyclic graphs that depict the relationships between the variables. Despite the fact that the statistical summaries and visualizations for each data set are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example data sets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone. The paper also includes R code for reproducing all figures and provides access to the data sets themselves through an R package named quartets.
Emission spectral non-Markovianity in qubit-cavity systems in the ultrastrong coupling regime

We study the emission spectra of dissipative Rabi and Jaynes-Cummings models in the non-Markovian and ultrastrong coupling regimes. We have derived a polaron-transformed Nakajima-Zwanzig master equation (PTNZE) to calculate the emission spectra, which eliminates the well known limitations of the Markovian approximation and the standard second-order perturbation. Using the time-dependent variational approach as benchmark, the PTNZE is found to yield accurate emission spectra in certain ultrastrong coupling regimes where the standard second-order Nakajima-Zwanzig master equation breaks down. It is shown that the emission spectra of the dissipative Rabi and Jaynes-Cummings models are in general asymmetric under various initial conditions. Direct comparisons of spectra for the two models illustrate the essential role of the qubit-cavity counter-rotating term and the spectra features under different qubit-cavity coupling strengths and system initial conditions.
A Certified Radius-Guided Attack Framework to Image Segmentation Models

Image segmentation is an important problem in many safety-critical applications. Recent studies show that modern image segmentation models are vulnerable to adversarial perturbations, while existing attack methods mainly follow the idea of attacking image classification models. We argue that image segmentation and classification have inherent differences, and design an attack framework specially for image segmentation models. Our attack framework is inspired by certified radius, which was originally used by defenders to defend against adversarial perturbations to classification models. We are the first, from the attacker perspective, to leverage the properties of certified radius and propose a certified radius guided attack framework against image segmentation models. Specifically, we first adapt randomized smoothing, the state-of-the-art certification method for classification models, to derive the pixel's certified radius. We then focus more on disrupting pixels with relatively smaller certified radii and design a pixel-wise certified radius guided loss, when plugged into any existing white-box attack, yields our certified radius-guided white-box attack. Next, we propose the first black-box attack to image segmentation models via bandit. We design a novel gradient estimator, based on bandit feedback, which is query-efficient and provably unbiased and stable. We use this gradient estimator to design a projected bandit gradient descent (PBGD) attack, as well as a certified radius-guided PBGD (CR-PBGD) attack. We prove our PBGD and CR-PBGD attacks can achieve asymptotically optimal attack performance with an optimal rate. We evaluate our certified-radius guided white-box and black-box attacks on multiple modern image segmentation models and datasets. Our results validate the effectiveness of our certified radius-guided attack framework.
Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures AutoML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoMLTrace, a visual interactive sketch showing both the context and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage scenario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work.
MiFL: Multi-Input Neural Networks in Federated Learning

&lt;p&gt;Driven by the Deep Learning (DL) revolution, Artificial intelligence (AI) has become a fundamental tool for many Bio-Medical tasks, including AI-assisted diagnosis. These include analysing and classifying images (2D and 3D), where, for some tasks, DL exhibits superhuman performance. Diagnostic imaging, however, is not the only diagnostic tool. Tabular data, such as personal data, vital signs, and genomic/blood tests, are commonly collected for every patient entering a clinical institution. However, it is rarely considered in DL pipelines, although it carries diagnostic information. The training of DL models requires large datasets, so large that every institution might need more data that should be pooled from different sites. Data pooling generates newfound concerns about data access and movement across other institutions spawning multiple dimensions, such as performance, energy efficiency, privacy, criticality, and security. Federated Learning (FL) is a cooperative learning paradigm aiming at addressing these concerns by moving models instead of data across different institutions. This paper proposes a Federated multi-input model that leverages images and tabular data, providing a proof of concept of the feasibility of multi-input FL architectures. The proposed model was evaluated on two showcases: the prognosis of CoViD-19 disease and the patients’ stratification for Alzheimer’s disease. Results show that enabling multi-input architectures in the FL framework allows for improving the performance regarding both accuracy and generalizability with respect to non-federated models while ensuring security and data protection peculiar to FL.&lt;/p&gt;
Translating computational innovations into reality: focus on the users!

Computer-aided/AI-driven tools are increasing being developed for use with digital pathology images. Whether a given scheme makes it into clinical use depends on a multitude of factors, perhaps most importantly whether it has an impact on a clinician’s decision-making process thus on patient care and outcomes. To have a positive impact, clinical decision support tools must be well-integrated into routine clinical workflows and thus require assessment from a human factors perspective that includes attention to ways these tools impact users’ perceptual and cognitive information processing mechanisms. Methods from implementation sciences (the scientific study of methods to promote systematic uptake of research findings and other evidence-based practices into routine practice to improve the quality and effectiveness of health services) can also be used to prepare users for and then assess the impact of introducing these tools into the clinical workflow. This paper will provide an overview of these perspectives, drawing on the history of medical image perception research in radiology and the growing application of these principles and methods in pathology.
To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency

Sequence-to-sequence language models can be used to produce abstractive summaries which are coherent, relevant, and concise. Still, model sizes can make deployment in latency-sensitive or web-scale implementations difficult. This paper studies the relationship between model size, structured pruning, inference efficiency, and summarization accuracy on widely used summarization datasets. We show that model accuracy is tied to the encoder size while inference efficiency is connected to the decoder. Using asymmetric pruning can lead to nearly 3x improvement in inference latency with ~1 point loss in Rouge-2. Moreover, we find both the average degradation and the role of asymmetry to be consistent across model sizes and variations in datasets.
Metaverse/Meta-Education Belief Scale

The "metaverse," which bridges augmented and virtual reality as mixed reality and includes technological phenomena such as artificial intelligence, continues to be an agenda topic. It is foreseen that the concept in question will accelerate the changes in education and teaching activities, as in many other fields. In this research, a measurement tool was developed to determine pre-service teachers' and teachers' beliefs about education in the metaverse (meta-education), especially considering the concept of the "metaverse" that entered our lives with the information age. During the development process of the measurement tool, an item pool consisting of 59 items was created by scanning the relevant literature. The opinions of five experts were received for the draft scale. Two items were added to the draft scale, and ten items were removed based on expert feedback. The scale was arranged as 41 items before the application. During the pilot application process, 203 were collected. Exploratory factor analysis (EFA) was used for the scale's construct validity. The EFA result shows that the scale consists of 22 items and a single factor, and 44% of the total variance is explained. The item-rest correlations on the scale ranged from .64 to .77. The Cronbach alpha coefficient of the scale was found to be .90. Based on these findings, the scale can be used to determine adults' beliefs about meta-education.
A Sparse Spatiotemporal Transformer for Detecting Driver Distracted Behaviors

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;At present, the development of autonomous driving technology is still immature, and there is still a long way until fully driverless vehicles. Therefore, the state of the driver is still an important factor affecting traffic safety, and it is of great significance to detect the driver’s distracted behavior. In the task of driver distracted behavior detection, some characteristics of driver behavior in the cockpit can be further utilized to improve the detection performance. Compared with general human behaviors, driving behaviors are confined to enclosed space and are far less diverse. With this in mind, we propose a sparse spatiotemporal transformer which extracts local spatiotemporal features by segmenting the video at the low level of the model, and filters out local key spatiotemporal information associated with larger attention values based on the attention map in the middle layer, so as to enhance the high-level global semantic features. Experiments are conducted on a public driver behavior detection dataset (Drive&amp;amp;Act), and the generalization ability of the proposal is evaluated with a dataset collected. Results show that the sparse spatiotemporal transformer devised in this study can obtain robust global semantic features via retaining key local spatiotemporal information while reducing the computational burden, and therefore achieves a high accuracy for the driver distracted behavior detection.&lt;/div&gt;&lt;/div&gt;
Thermal quantum correlations and Teleportation in a Graphene Sheet

The characterization of quantum resources in dynamical systems is one of the most important problems to be addressed in quantum information theory. In this article, we investigate the behaviors of quantum correlations and teleportation technique in a graphene sheet comprising of disordered electrons in a two-dimensional honeycomb lattice. We use three different measures of quantum correlations such as entanglement, measurement-induced nonlocality and uncertainty-induced nonlocality. We study the ground state properties of the graphene sheet from the perspective of quantum correlations. At thermal equilibrium, we show that the band parameter strengthens the quantum correlations whereas the scattering strength weakens the correlations. Finally, the impact of the system's parameters on the teleportation technique is also expounded.
When Technical Solutions aren’t Enough: Using Software Concepts to Analyse Challenges at Delivery Processes between Mixed-Signal IC Team Members

&lt;p&gt;Scientific works on integrated circuits usually focus on technical challenges and contribute to improving the solutions. They often neglect intertwined social aspects that highly impact project success, as shown in recent Software Engineering research. This work is a case study to identify challenges in delivery processes between digital and analog disciplines on mixed-signal projects at a specific design house, considering technical and social perspectives. We performed 17 semi-structured interviews and examined the available process documents, analyzing the data using Socio-Technical Grounded Theory. We mapped 14 constructs, 13 propositions, and two explanations to identify those challenges faced by mixed-signal teams. This study contributes to the discussion of social aspects of Semiconductor’s scientific works and presents a list of possible delivery challenges in the context of the focal company. The proposed root causes are the incipient specification processes and the lack of practices related to collaboration in cross-functional teams. Requirement Engineering and DevOps studies provide insights to define implications for improvement in the organization. &lt;/p&gt;
Improving Students' Speaking Skill Using Fable Pictures Cued And Storytelling Technique

This research used a storytelling technique with fable pictures to help students develop their speaking skills in learning English. Classroom Action Research is used in this study, which is applied in the classroom to see how the students' ability to speak English consists of 10 students from SMA Negeri 1 Sidamanik in the academic year of 2021/2022, North Sumatra. This research showed that the storytelling technique with fable pictures cued could be a helpful method for students in the classroom. Students seemed to have the confidence to speak without being nervous. Also, they enjoyed the fabled story with the picture. The pre-cycle results were 78.0, and cycle 1 increased by 2. 46. In cycle 2, the improvement score received was 5.04, and to combine all the improvements per cycle, which is 7.5. the researchers can conclude that telling stories with pictures cued can improve students' speaking ability.
A Proposed Heuristic for Guessing Distributions

How can a layman choose the least biased hypothetical distribution for approximating a real distribution? In this paper, we propose a simple rule of thumb for addressing this challenge. This rule of thumb involves choosing the distribution that maximizes the entropy under the constraint of rank order. For explaining this rule of thumb and its justification, we first show that “guesstimating” a real-world distribution is possible by using the “True Median”.
Image Stabilization for Hololens Camera in Remote Collaboration

With the advent of new technologies, Augmented Reality (AR) has become an effective tool in remote collaboration. Narrow field-of-view (FoV) and motion blur can offer an unpleasant experience with limited cognition for remote viewers of AR headsets. In this article, we propose a two-stage pipeline to tackle this issue and ensure a stable viewing experience with a larger FoV. The solution involves an offline 3D reconstruction of the indoor environment, followed by enhanced rendering using only the live poses of AR device. We experiment with and evaluate the two different 3D reconstruction methods, RGB-D geometric approach and Neural Radiance Fields (NeRF), based on their data requirements, reconstruction quality, rendering, and training times. The generated sequences from these methods had smoother transitions and provided a better perspective of the environment. The geometry-based enhanced FoV method had better renderings as it lacked blurry outputs making it better than the other attempted approaches. Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR) metrics were used to quantitatively show that the rendering quality using the geometry-based enhanced FoV method is better. Link to the code repository - https://github.com/MixedRealityETHZ/ImageStabilization.
Openmenot: A Google Chrome Extension for Detecting Phishing Websites Using Deep Learning

Abstract: With the advent of internet as a major medium right from inter-personal communication to how business transactions occur, the need to focus on research and development of various cyber security techniques rises. Phishing is one of the most common social engineering methods faced by public internet users, governments and businesses. There are several types of phishing by which users are directed to fake websites that resemble the legit ones. These fake websites collect the user credentials and use them for illegal purposes. In this project, a three-fold detection model using deep learning techniques like (Gradient Booster) for lexical and domain feature extraction ,(LSTM) for hyperlink extraction and (CNN) for content based extraction to detect phishing websites is proposed. This model is deployed by developing a google chrome extension that classifies and alerts the user when he/she is directed to a website that is not legit
On the Computation of Meaning, Language Models and Incomprehensible Horrors

&lt;p&gt;[Accepted to the 16th Annual Conference on Artificial General Intelligence]&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;We bring together foundational theories of meaning and a mathematical formalism of artificial general intelligence to provide a mechanistic explanation of meaning, communication and symbol emergence. We establish circumstances under which a machine might mean what we think it means by what it says, or comprehend what we mean by what we say. We conclude that a language model such as ChatGPT does not comprehend or engage in meaningful communication with humans, though it may exhibit complex behaviours such as theory of mind.&lt;/p&gt;