Reviewerly/exHarmony huggingface dataset:
Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and the Explanations

Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural language processing tasks. Theoretical results show that both are Turing-complete and can represent any context-free language (CFL).In practice, it is often observed that Transformer models have better representation power than LSTM. But the reason is barely understood. We study such practical differences between LSTM and Transformer and propose an explanation based on their latent space decomposition patterns. To achieve this goal, we introduce an oracle training paradigm, which forces the decomposition of the latent representation of LSTMand the Transformer and supervises with the transitions of the Pushdown Automaton (PDA) of the corresponding CFL. With the forced decomposition, we show that the performance upper bounds of LSTM and Transformer in learning CFL are close: both of them can simulate a stack and perform stack operation along with state transitions. However, the absence of forced decomposition leads to the failure of LSTM models to capture the stack and stack operations, while having a marginal impact on the Transformer model. Lastly, we connect the experiment on the prototypical PDA to a real-world parsing task to re-verify the conclusions
Hidden in Plain Sight

We present digital documentation of the Cockroach Key archaeological site in Tampa Bay on the western coast of Florida, USA. The site consists of a mound and midden complex constructed by Native Americans between around 100 and 900 CE. Although well known to antiquarians of the 1800s and archaeologists of the early 1900s, the site has slowly become “hidden in plain sight” to both archaeologists (owing to the lack of contemporary investigations) and the public (owing to the density of vegetation). We use LiDAR-based mapping and ground-penetrating radar to document the site’s surface and subsurface features.
Fast and Accurate Background Reconstruction Using Background Bootstrapping

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using acceleration with a graphics processing unit (GPU) and a Python implementation.
Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs)

Companies are increasingly facing the challenges of a persistent cyber threat landscape.By means of AI, cyber attacks can be efficiently conducted more successful through offensive AI.As for cyber defense, AI can be also utilized against cyber threats (defensive AI).Due to limited resources, especially in small and medium-sized companies (SMEs), there is a need to deploy more effective defensive cyber security solutions.Precisely, the adaptation of AI-based resilient defenses must be driven forward.Therefore, the aim of this paper is to identify and evaluate AI-related use cases with a high impact potential on the cyber security level, while being applicable to SMEs at the same time.In order to reach the research goal, an extensive literature review of several online catalogs, surveys and online platforms was conducted.In conclusion, seven crucial AI-based security features were outlined that are providing a high impact potential to the security level for SMEs.Afterwards, the results are discussed and set into a broader context.Even though AI-based security solutions are providing a large range of advantages, certain challenges and barriers using AI-related security applications are addressed in the paper as well.A high need for usable state of the art AI based cyber security solution for SMEs was identified.
Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images

Purpose: Accurate segmentation of the pancreas using abdominal computed tomography (CT) scans is a prerequisite for a computer-aided diagnosis system to detect pathologies and perform quantitative assessment of pancreatic disorders. Manual outlining of the pancreas is tedious, time-consuming, and prone to subjective errors, and thus clearly not a viable solution for large datasets. Approach: We introduce a multiphase morphology-guided deep learning framework for efficient three-dimensional segmentation of the pancreas in CT images. The methodology works by localizing the pancreas using a modified visual geometry group-19 architecture, which is a 19-layer convolutional neural network model that helped reduce the region of interest for more efficient computation and removed most of the peripheral structures from consideration during the segmentation process. Subsequently, soft labels for segmentation of the pancreas in the localized region were generated using the U-net model. Finally, the model integrates the morphology prior of the pancreas to update soft labels and perform segmentation. The morphology prior is a single three-dimensional matrix, defined over the general shape and size of the pancreases from multiple CT abdominal images, that helps improve segmentation of the pancreas. Results: The system was trained and tested on the National Institutes of Health dataset (82 CT scans of the healthy pancreas). In fourfold cross-validation, the system produced an average Dice-SØrensen coefficient of 88.53% and outperformed state-of-the-art techniques. Conclusions: Localizing the pancreas assists in reducing segmentation errors and eliminating peripheral structures from consideration. Additionally, the morphology-guided model efficiently improves the overall segmentation of the pancreas.
Transfer learning approach in deep neural networks for uterine fibroid detection

Convolutional neural network (CNN), a class of deep neural network, takes images as input and automatically extracts features for effective class prediction.The performance of CNN architecture is a major concern while dealing with fewer data.Traditional CNN architectures like ImageNet, AlexNet, GoogleNet are trained with a large quantity of data.Transfer learning is applied in order to combine pre-trained models such as Inception-V4 and support vector machine (SVM) for better performance with fewer data.The goal of the proposed approach is to efficiently detect fibroid presence in uterus MRI images.Features of these images are extracted using initial layers of Inception-V4 and are transferred to SVM during training.Several combinations on various network classifiers are tested and the performance metrics are evaluated.The proposed model attained an accuracy of 81.05% with a U-Kappa score of 0.402 on predicting fibroid images and 0.25% accuracy improvement compared with ResNet based model.
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation

Automatic diagnosis has attracted increasing attention but remains challenging due to multi-step reasoning. Recent works usually address it by reinforcement learning methods. However, these methods show low efficiency and require task-specific reward functions. Considering the conversation between doctor and patient allows doctors to probe for symptoms and make diagnoses, the diagnosis process can be naturally seen as the generation of a sequence including symptoms and diagnoses. Inspired by this, we reformulate automatic diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but effective automatic Diagnosis model based on Transformer (Diaformer). We firstly design the symptom attention framework to learn the generation of symptom inquiry and the disease diagnosis. To alleviate the discrepancy between sequential generation and disorder of implicit symptoms, we further design three orderless training mechanisms. Experiments on three public datasets show that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5% with the highest training efficiency. Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.
Development of teaching digital competence in initial teacher training: A systematic review.

This article presents the results of a systematic review of different studies published in high-impact journals on the development of teaching digital competence (TDC) in initial teacher training. The methodology of the study is based on the documentary research approach, with the application of a systematic literature review covering the period between 2009 and 2019 in different databases. The sample is made up of 102 articles that are registered in a multidimensional analysis and classification matrix. One of the main findings is that in the last ten years there has been a significant increase in the literature related to teaching digital competence with the prevailing use of the quantitative methodological approach. Likewise, 50.98% of the articles are the result of the application of instruments to assess TDC. It can be concluded that TDC is a line of research with multiple dimensions of analysis and in constant evolution.&#x0D; Keywords: teaching digital competence, initial teacher training, educational technology, information, communication technology, systematic review
MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.
Updated Camera Spatial Frequency Response for ISO 12233

The edge-based Spatial Frequency Response (e-SFR) method is well established and has been included in the ISO 12233 standard since the first version in 2000.A new 4 th edition of the standard is proceeding, with additions and changes that are intended to broaden its application and improve reliability.We report on results for advanced edge-fitting which, although reported before, was not previously included in the standard.The application of the e-SFR method to a range of edge-feature angles is enhanced by the inclusion of an angle-based correction, and use of a new test chart.We present examples of the testing completed for a wider range of edge test features than previously addressed by ISO 12233, for nearzero-and -45-degree orientations.Various smoothing windows were compared, including the Hamming and Tukey forms.We also describe a correction for image non-uniformity, and the computing of an image sharpness measure (acutance) that will be included in the updated standard.
Bayesian Hyperparameter Optimization and Ensemble Learning for Machine Learning Models on Software Effort Estimation

In recent decades, various software effort estimation (SEE) algorithms have been suggested.Unfortunately, generating high-precision accuracy is still a major challenge in the context of SEE.The use of traditional techniques and parametric approaches is largely inaccurate because they produce biased and subjective accuracy.Meanwhile, none of the machine learning methods performed well.This study applies the AdaBoost ensemble learning method and random forest (RF), on the other hand the Bayesian optimization method is applied to determine the hyperparameters of this model.The PROMISE repository and the ISBSG dataset were used to build the SEE model.The developed model was comprehensively compared with four machine learning methods (classification and regression tree, k-nearest neighbor, multilayer perceptron, and support vector regression) under 3-fold cross validation (CV).It can be seen that the RF method based on AdaBoost ensemble learning and bayesian optimization outperforms this approach.In addition, the AdaBoost-based model assigns a feature importance rating, which makes it a promising tool in software effort prediction.
A low-cost AR training system for manual assembly operations

This research work proposes an AR training system adapted to industry, designed by considering key challenges identified during a long-term case study conducted in a boiler-manufacturing factory. The proposed system relies on lowcost visual assets (i.e., text, image, video, and predefined auxiliary content) and requires solely a head-mounted display (HMD) device (i.e., Hololens 2) for both authoring and training. We evaluate our proposal in a real-world use case by conducting a field study and two field experiments, involving 5 assembly workstations and 30 participants divided into 2 groups: (i) low-cost group (G-LA) and (ii) computeraided design (CAD)-based group (G-CAD). The most significant findings are as follows. The error rate of 2.2% reported by G-LA during the first assembly cycle (WEC) suggests that low-cost visual assets are sufficient for effectively delivering manual assembly expertise via AR to novice workers. Our comparative evaluation shows that CAD-based AR instructions lead to faster assembly (-7%, -18% and -24% over 3 assembly cycles) but persuade lower user attentiveness, eventually leading to higher error rates (+38% during the WEC). The overall decrease of the instructions reading time by 47% and by 35% in the 2nd and 3rd assembly cycles, respectively, suggest that participants become less dependent on the AR work instructions rapidly. By considering these findings, we question the worthiness of authoring CAD-based AR work instructions in similar industrial use cases.
Scaling Up Sign Spotting Through Sign Language Dictionaries

The focus of this work is $\textit{sign spotting}$ - given a video of an isolated sign, our task is to identify $\textit{whether}$ and $\textit{where}$ it has been signed in a continuous, co-articulated sign language video. To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1) $\textit{watching}$ existing footage which is sparsely labelled using mouthing cues; (2) $\textit{reading}$ associated subtitles (readily available translations of the signed content) which provide additional $\textit{weak-supervision}$; (3) $\textit{looking up}$ words (for which no co-articulated labelled examples are available) in visual sign language dictionaries to enable novel sign spotting. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning. We validate the effectiveness of our approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, BSLDict, to facilitate study of this task. The dataset, models and code are available at our project page.
State transformations within entanglement classes containing permutation-symmetric states

The study of state transformations under local operations and classical communication (LOCC) plays a crucial role in entanglement theory. While this has been long ago characterized for pure bipartite states, the situation is drastically different for systems of more parties: generic pure qudit states cannot be obtained from nor transformed to any state (i.e., they are isolated), which contains a different amount of entanglement. We consider here the question of LOCC convertibility for permutation-symmetric pure states of an arbitrary number of parties and local dimension, a class of clear interest both for physical and mathematical reasons and for which the aforementioned result does not apply given that it is a zero-measure subset in the state space. While it turns out that generic $n$-qubit symmetric states are also isolated, we consider particular families for which we can determine to be, on the contrary, endowed with a rich local stabilizer, a necessary requirement for LOCC convertibility to be possible. This allows us to identify classes in which LOCC transformations among permutation-symmetric states are possible. Notwithstanding, we provide several results that indicate severe obstructions to LOCC convertibility in general even within these highly symmetrical classes. In the course of the study of LOCC transformations, we also characterize the local symmetries of symmetric states.
Does a Face Mask Protect My Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images

Contactless and efficient systems are implemented rapidly to advocate preventive methods in the fight against the COVID-19 pandemic. Despite the positive benefits of such systems, there is potential for exploitation by invading user privacy. In this work, we analyse the privacy invasiveness of face biometric systems by predicting privacy-sensitive soft-biometrics using masked face images. We train and apply a CNN based on the ResNet-50 architecture with 20,003 synthetic masked images and measure the privacy invasiveness. Despite the popular belief of the privacy benefits of wearing a mask among people, we show that there is no significant difference to privacy invasiveness when a mask is worn. In our experiments we were able to accurately predict sex (94.7%), race (83.1%) and age (MAE 6.21 and RMSE 8.33) from masked face images. Our proposed approach can serve as a baseline utility to evaluate the privacy-invasiveness of artificial intelligence systems that make use of privacy-sensitive information. We open-source all contributions for reproducibility and broader use by the research community.
Blockchain technology in corporate governance and future potential solution for agency problems in Indonesia

The aim of this study is to determine stakeholder acceptance of the blockchain and to investigate a suitable model using a Technology Acceptance Model with specific reference to corporate governance through cryptography in solving decades of financial record-keeping problems. Stakeholders in corporate governance, namely customers, creditors, suppliers, communities, employees, owners, investors, trade unions and social activists, can benefit in different ways. Investors can benefit from buying equity at a lower price and selling it on a market with greater liquidity, but they will find it difficult to disguise their trades. This study argues that almost all aspects of corporate governance can be improved through the application of this technology, which results in greater transparency, increases liquidity, and lowers costs. Corporate governance will also be better because blockchain technology uses the concept of a distributed ledger which allows data to be distributed at every connected point in an efficient and accountable manner, so that all parties in the blockchain can exchange data in real time.
MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding

Recently, there has been an increasing interest in building question answering (QA) models that reason across multiple modalities, such as text and images. However, QA using images is often limited to just picking the answer from a pre-defined set of options. In addition, images in the real world, especially in news, have objects that are co-referential to the text, with complementary information from both modalities. In this paper, we present a new QA evaluation benchmark with 1,384 questions over news articles that require cross-media grounding of objects in images onto text. Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question. In addition, we introduce a novel multimedia data augmentation framework, based on cross-media knowledge extraction and synthetic question-answer generation, to automatically augment data that can provide weak supervision for this task. We evaluate both pipeline-based and end-to-end pretraining-based multimedia QA models on our benchmark, and show that they achieve promising performance, while considerably lagging behind human performance hence leaving large room for future work on this challenging new task.
Moderating the role of the perceived security and endorsement on the relationship between per-ceived risk and intention to use the artificial intelligence in financial services

Advancement of banking and financial investment has led to the rapid expansion of services automation. The consistent increase of Artificial Intelligence (AI) usage in investment management implies the impending popularity of technology-based service. This study examined influencer endorsement and perceived security benefits as moderators to the relationship between perceived risk and financial AI services. Questionnaires were disseminated to 300 respondents who were customers with experience of using financial AI services in Jordan, and they were chosen through purposive sampling method. Structural equation modeling run using Smart-partial least squares (PLS 3.3.6) was employed in analyzing the data obtained from 220 completed questionnaires. The results show that perceived risk negatively affects financial AI services, while influencer endorsement and perceived security moderate the relationship between perceived risk and financial AI services. This study provides insight to companies on how to reduce perceived risk to encourage people to use business intelligence applications, as in the use of financial technology services.