Reviewerly/exHarmony huggingface dataset:
Learning Where to Look – Generative NAS is Surprisingly Efficient

The efficient, automated search for well-performing neural architectures (NAS) has drawn increasing attention in the recent past. Thereby, the predominant research objective is to reduce the necessity of costly evaluations of neural architectures while efficiently exploring large search spaces. To this aim, surrogate models embed architectures in a latent space and predict their performance, while generative models for neural architectures enable optimization-based search within the latent space the generator draws from. Both, surrogate and generative models, have the aim of facilitating query-efficient search in a well-structured latent space. In this paper, we further improve the trade-off between query-efficiency and promising architecture generation by leveraging advantages from both, efficient surrogate models and generative design. To this end, we propose a generative model, paired with a surrogate predictor, that iteratively learns to generate samples from increasingly promising latent subspaces. This approach leads to very effective and efficient architecture search, while keeping the query amount low. In addition, our approach allows in a straightforward manner to jointly optimize for multiple objectives such as accuracy and hardware latency. We show the benefit of this approach not only w.r.t. the optimization of architectures for highest classification accuracy but also in the context of hardware constraints and outperform state-of-the-art methods on several NAS benchmarks for single and multiple objectives. We also achieve state-of-the-art performance on ImageNet. The code is available at https://github.com/jovitalukasik/AG-Net .
Spiking Neural Network Equalization for IM/DD Optical Communication

A spiking neural network (SNN) equalizer model suitable for electronic neuromorphic hardware is designed for an IM/DD link. The SNN achieves the same bit-error-rate as an artificial neural network, outperforming linear equalization.
Explaining Ontology-Mediated Query Answers Using Proofs over Universal Models

In ontology-mediated query answering, access to incomplete data sources is mediated by a conceptual layer constituted by an ontology, which can be formulated in a description logic (DL) or using existential rules. In the literature, there exists a multitude of complex techniques for incorporating ontological knowledge into queries. However, few of these approaches were designed for explainability of the query answers. We tackle this challenge by adapting an existing proof framework toward conjunctive query answering, based on the notion of universal models. We investigate the data and combined complexity of determining the existence of a proof below a given quality threshold, which can be measured in different ways. By distinguishing various parameters such as the shape of the query, we obtain an overview of the complexity of this problem for several Horn DLs.
Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds

Most models tasked to ground referential utterances in 2D and 3D scenes learn to select the referred object from a pool of object proposals provided by a pre-trained detector. This is limiting because an utterance may refer to visual entities at various levels of granularity, such as the chair, the leg of the chair, or the tip of the front leg of the chair, which may be missed by the detector. We propose a language grounding model that attends on the referential utterance and on the object proposal pool computed from a pre-trained detector to decode referenced objects with a detection head, without selecting them from the pool. In this way, it is helped by powerful pre-trained object detectors without being restricted by their misses. We call our model Bottom Up Top Down DEtection TRansformers (BUTD-DETR) because it uses both language guidance (top down) and objectness guidance (bottom-up) to ground referential utterances in images and point clouds. Moreover, BUTD-DETR casts object detection as referential grounding and uses object labels as language prompts to be grounded in the visual scene, augmenting supervision for the referential grounding task in this way. The proposed model sets a new state-of-the-art across popular 3D language grounding benchmarks with significant performance gains over previous 3D approaches (12.6% on SR3D, 11.6% on NR3D and 6.3% on ScanRefer). When applied in 2D images, it performs on par with the previous state of the art. We ablate the design choices of our model and quantify their contribution to performance. Our code and checkpoints can be found at the project website https://butd-detr.github.io .
Advances in Behavioral Economics and Responsible Competition Leadership: Tackling Searchplace Discrimination

The digital millennium leveraged the World Wide Web into a powerful information source. Online internet searchplaces guide human everyday decisions. The strategic placement of information in search engine results has become increasingly important in corporate and political settings. Virtual competition derails in negative search engine de-optimization and unethical strategic searchplace manipulation that degrades the perception of a search term by pushing out competitors’ quality content from search engine results. This article discusses technicalities of searchplace discrimination in erasing useful information about competition for negative, unrelated, spamming, or harmful contents. In light of the negative implications of searchplace discrimination, cyberbullying and online inequalities, behavioral economics and responsible competition leadership can aid in creating inclusive digital worlds. Behavioral insights should draw attention to self-determined internet user empowerment to correct abuse of algorithmic loopholes. Legal advancements, regulatory oversight, economic incentives, technical support and industry rescue funds work towards discrimination-free online searchplaces in favor for quality content over unethical competition. Ethics of online inclusion, law and economics analyses of searchplaces and interdisciplinary dialogue building on searchplace ethics but also human-artificial intelligence algorithm compatibility and cyber-checks-and-balances to tackle searchplace discrimination are expected to become key advancements in behavioral e-ethics and competition leadership of the future.
Semi-supervised Object Detection via VC Learning

Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exacerbate the confirmation bias issue caused by inevitable mislabelling. To solve this problem, this paper proposes to use confusing samples proactively without label correction. Specifically, a virtual category (VC) is assigned to each confusing sample such that they can safely contribute to the model optimisation even without a concrete label. It is attributed to specifying the embedding distance between the training sample and the virtual category as the lower bound of the inter-class distance. Moreover, we also modify the localisation loss to allow high-quality boundaries for location regression. Extensive experiments demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially with small amounts of available labels.
Semi-Supervised Anomaly Detection with Contrastive Regularization

Deep anomaly detection has recently seen significant developments to provide robust and efficient classifiers using only a few anomalous samples. Many of those models consist in a first isolated step of representation learning. However, in its current form the learned representation does not encode the semantics of normal sample and anomalies. Indeed during the first step these models will not utilize the available normal/anomaly labels, harming the downstream anomaly detection classifier performances.In the light of this limitation, we introduce a new deep anomaly detector enforcing an anomaly distance constraint on the norm of the representations while using contrastive learning on the direction of the features. This allows it to learn representations well-suited to anomaly detection while avoiding any representation collapse. Moreover, we introduce two strategies of anomaly enriching to improve the robustness of any distance-based anomaly detector. Our model highly improves the state-of-the-art performances on a wide array of anomaly types with up to 74% error relative improvement on object anomalies.
THE CREATIVE POTENTIAL OF FUTURE TEACHERS: DIAGNOSTICS AND OVERCOMING BARRIERS TO DEVELOPMENT

The article deals with the concepts of pedagogical talent and creative potential of future teachers. The article presents the description of the course "Diagnostics and technologies for the researcher’s creative potential development", within the framework of which a diagnostic study was carried out. The results obtained made it possible to determine the individual characteristics of the creative potential profile, as well as to identify barriers to the development. The novelty of the research is more scientific and applied. The practical significance of the results obtained consists in the proposed version of creating pedagogical conditions, within the framework of which the convergence of the theoretical methodological concepts and technologies of creativity development with the results of its diagnostics. The results of the study are also important for expanding the understanding of students' creative potential and predictors of possible barriers to creativity development.
Classification Performance Evaluation from Multilevel Logistic and Support Vector Machine Algorithms through Simulated Data in Python

This paper analyzes the performance of multilevel logistic and support vector machine algorithms when the objective is the stratification of the sample into two groups for binary classification. Under the data simulation in Python, we show that multilevel logistic models cannot correctly classify observations under certain non-linear conditions, even when defined contextual hierarchical groups and support vector classifiers generate better predictions. Python codes are provided for replication purposes.
SpanMTL: a span-based multi-table labeling for aspect-oriented fine-grained opinion extraction

Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract the aspect terms, corresponding opinion terms and sentiment polarity in a target sentence. Most previous methods treat AFOE as word-level or span-level task, which ignore the complementarity of these two tasks. To integrate the merits of word-level and span-level information, we construct an end-to-end Span-based Multi-Table Labeling (SpanMTL) framework. SpanMTL combines word-based and span-based table labeling to tackle AFOE task. Specifically, in the proposed model, we use two separate BiLSTMs to encode the information of aspect and opinion terms into a word-based 2D representation table. Based on the table, we construct span-based table with CNN by associating the word-pair representations. At last, we integrate the table label distributions of word- and span-based table labeling to generate a multi-table labeling. The proposed method improves the performances of Opinion Pair Extraction (OPE) and Opinion Triplet Extraction (OTE) tasks by introducing span information, especially on the datasets with lots of spans. We have conducted various experiments on AFOE datasets to validate our method. The experimental results show that our method outperforms other baselines when the sentences having lots of span information.
Exploiting Deep Learning-Based LSTM Classification for Improving Hand Gesture Recognition to Enhance Visitors’ Museum Experiences

Hand gesture recognition (HGR) is one of the main axes of the Human-Computer Interaction (HCI) research field. And computer vision is a very active dedicated research area. However, traditional vision-based methods, like using a fixed camera to record video sequences of sign language, have some serious drawbacks inherent to the fixed camera location, complex lighting conditions, and cluttered backgrounds. Motivated by these potential limitations, the present paper addresses the detection and classification of hand gestures based rather on wearable video monitoring data. A new feature extraction strategy based on five hand's partial occupancy areas in images is provided. And a deep learning formalism, using the Long Short-Term Memory (LSTM) algorithm, has been implemented to ensure an effectual separation between classes. To analyze the performances of the classification, available data have been used for experiments, and both virtual and real museum scenarios are considered. The obtained results demonstrated that the combined five area ratios and LSTM classification were not only able to recognize different hand gestures but it was also able to distinguish between actions with a high degree of similarity (like slide left and slide right classes). The use of deep learning-based LSTM algorithm in the classification phase helped in reducing significantly the number of misclassifications, and achieving an outstanding recognition performance when challenged with real-world data.
LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Abstract Medical image is an essential tool used in quantitative and qualitative evaluation of different diseases. Medical imaging methods such as fluorescein angiography (FA), optical coherence tomography angiography (OCTA), computed tomography (CT), optical coherence tomography (OCT), and X-ray are used for diagnosis. These imaging modalities suffer from low contrast, which leads to deterioration in the image quality. Consequently, this causes limitation in the usage of medical images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision-making. To overcome this limitation, we propose a novel local transfer function for medical image enhancement algorithm using the pixel neighborhood constraint. The proposed algorithm uses block-wise intensity distribution to generate the regional similarity index. The regional similarity index transformed each centered pixel in the block, to generate a new similarity image. An intuitive optimization algorithm is utilized to optimize the proposed algorithm parameters. Experimentation results show that the proposed LTF-NSI performs better than the state-of-the-art methods and improves the interpretability and perception of the medical images, which can provide clinicians and computer vision program with good quantitative and qualitative information.
A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition

Computer Vision (CV) has become an essential field in Artificial Intelligence applications. Object detection and recognition (ODR) is one of the fundamental tasks of computer vision implementations. However, developing an efficient ODR model is still a significant problem. The model’s execution time and speed are the most critical features during the inference or detection and recognition process, which need to be improved using the latest object detection architectures. In this paper, the handwritten detection and recognition (HDR) model is developed based on previously known algorithms with their efficiency, such as Faster R-CNN and YOLOv4 in the first hand. On the other hand, two new models capable of detecting and recognizing handwritten digits using the latest ODR algorithm are proposed, one based on the latest YOLO family architecture (YOLOv5-HDR) with high speed and accuracy and the other using the transformers architecture (DETR). To the best of our knowledge, this is the first study to achieve a details comparison between YOLOv5 and transformers-based models in handwritten digit detection. Finally, the detailed performance analysis achieved by the paper proves that the YOLOv4-based model achieved the testing inference 13% faster than Faster R-CNN. However, the proposed YOLOv5-based model outperformed the YOLOv4 and the transformers-based one as it increased the testing execution time 25% faster than the YOLOv4, three times faster than the DETR model. A further adversarial attack test has been conducted to ensure the robust performance of the proposed model. Furthermore, numerical experiment results and their analyses demonstrate the robustness and effectiveness of the proposed YOLOv5-based model being the most stable for handwritten digit detection and recognition tasks.
Genuine quadripartite steering in three-photon spontaneous parametric down-conversion

The authors show that genuine quadripartite quantum steering can be produced in the case of three-photon spontaneous parametric down-conversion when the quantum property of the pump is considered. They also find that the injected signal can improve the stability of quadripartite quantum steering.
Amazon's Artificial Intelligence in Retail Novelty - Case Study

Purpose: The provision of a method for thoughtful decision-making is the core purpose of artificial intelligence research and development. The primary goal of artificial intelligence (AI) is to give computers the ability to do intellectual tasks such as making decisions, solving problems, seeing their surroundings, and understanding human communication. Amazon is famous for using robots—roughly 30,000 of them—within its distribution centres. The company has gained mechanical autonomous organisation, which allows the robots to function independently. Kiva in 2012. Retailers can improve their demand estimates, make better pricing decisions, and optimise product placement with the aid of AI. The end result is that customers are connected with the proper products at the suitable time, in the appropriate place, and at the appropriate price. Utilizing predictive analytics may assist in determining the amount of a product that should be ordered to ensure that shops do not end up with either an excess or a shortage of inventory. Design/Methodology/Approach: The efficiency of our workplaces may be substantially improved by the use of artificial intelligence. When AI is utilised to do tasks that are boring or dangerous, human workers are given more time to concentrate on endeavours that need capabilities such as creativity and empathy, amongst other abilities. Artificial intelligence (AI) may be of assistance to a corporation in three different areas: the automation of corporate processes; the acquisition of insight through data analysis; and the interaction with consumers and staff. Findings/Result: As a result of AI, individuals will be freed up to focus on the 20% of non-routine jobs that account for 80% of the value they create. In the future, "intelligent automation of process change" (IAPC) will be used by smart machines to constantly examine and improve the whole process of a business's response to artificial intelligence. Automated and optimised everyday chores save your time and money and improves operational efficiency and productivity. The outputs of cognitive technology may help you make quicker business judgments and benefits the AI in the Workplace. Doing the same thing over and over again might take a toll on your workers' productivity. Automating and optimising these procedures will save you money. Employees will be able to concentrate on multiple areas at once, resulting in increased production. As AI processes data more quickly than humans, this leads to increased productivity. Originality/Value: It is essential to have a deeper comprehension of the differences between AI and human intelligence if we are to be adequately prepared for a society in which AI will play a much more pervasive role in our everyday lives in the foreseeable future. The process of reproduction is intricately connected to both biological and human intelligence. The advancement of artificial intelligence (AI) is significant for the reason that it paves the way for software to perform human activities and cost-effective than it was previously possible. Paper Type: Company Analysis.
Sentiment Analysis using Latent Dirichlet Allocation for Aspect Term Extraction

This work proposes a sentiment analysis approach for decision-making in product design, analysis, and market share. The approach incorporates user-generated text data in the form of consumer reviews to extract product features using topic-based modeling methods. Latent Dirichlet Allocation (LDA) is employed to extract aspect categories from the data and identify the sentiment of each review using the VADER sentiment analyzer. The performance of the proposed method is evaluated in terms of accuracy, with an achieved result of 80%. The extracted topics are also summarized to provide leads for product design and quality assurance. The approach can be used by manufacturers, retailers, and suppliers to understand customers' opinions about their products better and make better decisions. LDA is a powerful unsupervised method that can extract latent topics from a collection of documents; this method has been widely used in text mining, information retrieval, and natural language processing. The accuracy can be improved by using more sophisticated models or more data.
$\mathcal {S}^{2}$Net: Accurate Panorama Depth Estimation on Spherical Surface

Monocular depth estimation is an ambiguous problem, thus global structural cues play an important role in current data-driven single-view depth estimation methods. Panorama images capture the complete spatial information of their surroundings utilizing the equirectangular projection which introduces large distortion. This requires the depth estimation method to be able to handle the distortion and extract global context information from the image. In this paper, we propose an end-to-end deep network for monocular panorama depth estimation on a unit spherical surface. Specifically, we project the feature maps extracted from equirectangular images onto unit spherical surface sampled by uniformly distributed grids, where the decoder network can aggregate the information from the distortion-reduced feature maps. Meanwhile, we propose a global cross-attention-based fusion module to fuse the feature maps from skip connection and enhance the ability to obtain global context. Experiments are conducted on five panorama depth estimation datasets, and the results demonstrate that the proposed method substantially outperforms previous state-of-the-art methods. All related codes will be open-sourced in the upcoming days.
Classification of quantum correlation using deep learning

Quantum correlation, as an intrinsic property of quantum mechanics, has been widely employed to test the fundamental physical principles and explore the quantum-enhanced technologies. However, such correlation would be drowned and even destroyed in the conditions of high levels of loss and noise, which drops into the classical realm and renders quantum advantage ineffective. Especially in low light conditions, conventional linear classifiers are unable to extract and distinguish quantum and classical correlations with high accuracy. Here we experimentally demonstrate the classification of quantum correlation using deep learning to meet the challenge in the quantum imaging scheme. We design the convolutional neural network to learn and classify the correlated photons efficiently with only 0.1 signal photons per pixel. We show that decreasing signal intensity further weakens the correlation and makes an accurate linear classification impossible, while the deep learning method has a strong robustness of such task with the accuracy of 99.99%. These results open up a new perspective to optimize the quantum correlation in low light conditions, representing a step towards diverse applications in quantum-enhanced measurement scenarios, such as super-resolution microscope, quantum illumination, etc.