Reviewerly/exHarmony huggingface dataset:
AU-Aware 3D Face Reconstruction through Personalized AU-Specific Blendshape Learning

3D face reconstruction and facial action unit (AU) detection have emerged as interesting and challenging tasks in recent years, but are rarely performed in tandem. Image-based 3D face reconstruction, which can represent a dense space of facial motions, is typically accomplished by estimating identity, expression, texture, head pose, and illumination separately via pre-constructed 3D morphable models (3DMMs). Recent 3D reconstruction models can recover high-quality geometric facial details like wrinkles and pores, but are still limited in their ability to recover 3D subtle motions caused by the activation of AUs. We present a multi-stage learning framework that recovers AU-interpretable 3D facial details by learning personalized AU-specific blendshapes from images. Our model explicitly learns 3D expression basis by using AU labels and generic AU relationship prior and then constrains the basis coefficients such that they are semantically mapped to each AU. Our AU-aware 3D reconstruction model generates accurate 3D expressions composed by semantically meaningful AU motion components. Furthermore, the output of the model can be directly applied to generate 3D AU occurrence predictions, which have not been fully explored by prior 3D reconstruction models. We demonstrate the effectiveness of our approach via qualitative and quantitative evaluations.
ARTIFICIAL INTELLIGENCE IN THE ADMINISTRATION OF JUSTICE: NEW OPPORTUNITIES AND NEW CHALLENGES

The implementation of AI in the judicial system has been discussed for quite a long time. However, as lawyers note, the use of AI judges can lead not only to procedural, but also to value changes in the legal system. The review examines new opportunities and new risks that AI may entail in the field of justice.
US Accent Recognition Using Machine Learning Methods

Speech is considered the most important instrument for individuals to express their ideas and thoughts, and language is its most basic part. Even among speakers of the same language, there may be differences in language pronunciation. These differences are called accents. English is considered the most spoken language in the world. For this reason, there are many English speech accents. This study aims to recognize the American accent among six accents: American, British, French, German, Italian, and Spanish. Two different machine learning-based algorithms are employed for the accent recognition task: Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). In both methods, the user-defined hyperparameters are optimized to obtain high-accuracy results. Furthermore, the k-fold cross validation technique is applied to ensure the reliability of the results. Experimental results show that SVM with the Radial Basis Function (RBF) kernel yields the highest accuracy (89 %). As a result, SVM with RBF kernel is a viable option for speaker accent recognition applications.
Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offline Handwritten Mathematical Expression Recognition

Offline Handwritten Mathematical Expression Recognition (HMER) has been dramatically advanced recently by employing tree decoders as part of the encoder-decoder method. Despite the tree decoder-based methods regard the expressions as a tree and parse 2D spatial structure to the tree nodes sequence, the performance of existing works is still poor due to the inevitable tree nodes prediction errors. Besides, they lack syntax rules to regulate the output of expressions. In this paper, we propose a novel model called Spatial Attention and Syntax Rule Enhanced Tree Decoder (SS-TD), which is equipped with spatial attention mechanism to alleviate the prediction error of tree structure and use syntax masks (obtained from the transformation of syntax rules) to constrain the occurrence of ungrammatical mathematical expression. In this way, our model can effectively describe tree structure and increase the accuracy of output expression. Experiments show that SS-TD achieves better recognition performance than prior models on CROHME 14/16/19 datasets, demonstrating the effectiveness of our model.
Hybrid Pre-training Based on Masked Autoencoders for Medical Image Segmentation

The application of deep learning in the field of medical images has been gaining attention in recent years. However, due to the small number of medical images, the inability to generate them manually, and the high cost of annotation, it is difficult to train them. Masked Autoencoders (MAE), a recent hot new approach in the field of self-supervision, is based on Vision Transformer (ViT) architecture coding to improve the learning difficulty by adding masking to the cut non-overlapping image patches to obtain deeper image representations. In this paper, we employ three different small-scale pre-training datasets using MAE’s pre-training method to pre-train and select the medical segmentation task as its performance test task. We experimentally find that the new hybrid dataset pre-trained with the training set of the hybrid downstream task and other datasets has good performance, and the results are improved compared to those without pre-training, and better than those obtained with a single dataset, showing the potential of the self-supervised method hybrid pre-training for medical segmentation tasks.
Multi Sensor Gesture Recognition System Based on MCU

MCU embedded AI application has the characteristics of reducing power consumption, reducing cost, fast listing, real-time response, wide industry, huge volume, pre-processing and so on. This paper develops a multi-sensor gesture recognition system based on MCU, By collecting sensor data, Deep learning framework training AI model, Cube AI converts and generates C code in embedded microcontroller, Finally, the EMG gesture recognition function is realized.
Reservoir-computing and neural-network-based equalization for short reach communication

We review the use of neural-network-based equalizers for mitigating the power fading effect in intensity-modulated directly detected links, and compare the different approaches reported in the literature.
Enriching Vision Videos with Text: An Eye Tracking Study

One main goal of requirements engineering is ensuring that all stakeholders share the same vision of the future system. Vision Videos can be used for this purpose. They visualize the product vision at an early stage of the project. However, some functional aspects, such as an access code being valid only once, are difficult to visualize. Short texts could be a low-effort solution to complement videos with additional information. Thus, represented visions and requirements could be taken to the next level of clarification. Our question is two-fold: What effects do texts have in vision videos? What are suitable ways to add textual information to vision videos? We conducted an eye tracking study to investigate the effects of adding short texts to vision videos, either below the scene or as text overlays. We evaluated these two methods regarding video comprehension, reading time, cognitive load and subjective preference. Our study shows that texts in vision videos improve vision comprehension and thus support the achievement of one requirements engineering goal. Both methods increase the cognitive load of viewers only by a reasonable margin. No clear-cut preference for a single variant was found. We derive recommendations on how to add texts to vision videos.
Challenges and Best Practices in Corporate AI Governance: Lessons from the Biopharmaceutical Industry

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management.
Learning to Restore 3D Face from In-the-Wild Degraded Images

In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded. To address such a problem, in this paper we propose a novel Learning to Restore (L2R) 3D face framework for unsupervised high-quality face reconstruction from low-resolution images. Rather than directly refining 2D image appearance, L2R learns to recover fine-grained 3D details on the proxy against degradation via extracting generative facial priors. Concretely, L2R proposes a novel albedo restoration network to model high-quality 3D facial texture, in which the diverse guidance from the pre-trained Generative Adversarial Networks (GANs) is leveraged to complement the lack of input facial clues. With the finer details of the restored 3D texture, L2R then learns displacement maps from scratch to enhance the significant facial structure and geometry. Both of the procedures are mutually optimized with a novel 3D-aware adversarial loss, which further improves the modelling performance and suppresses the potential uncertainty. Extensive experiments on benchmarks show that L2R outperforms state-of-the-art methods under the condition of low-quality inputs, and obtains superior performances than 2D pre-processed modelling approaches with limited 3D proxy.
The Use of Virtual Reality Technologies in the Specialists’ Training in the Field of Information Security

The paper presents a study of ready-made solutions and ways to use virtual reality (VR) technologies in training specialists. Due to the lack of tools that meet the needs of such preparation in the field of information security (IS), when students study network attacks and ways of protection against them, a training and laboratory complex using the Unity development environment for educational purposes were developed. It is intended for a detailed study of current network attacks implementation and development of protection measures against them. The educational and laboratory bench includes a VR testbed and its algorithmic support successfully tested in the educational process of the the National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) within the framework of the discipline "Secure Information Systems".
NEAR: Non-supervised Explainability Architecture for Accurate Review-Based Collaborative Filtering

There is a critical issue in explainable recommender systems that compounds the challenges of explainability yet is rarely tackled: the lack of ground-truth explanation texts for training. It is unrealistic to expect every user-item pair in a dataset to have a corresponding target explanation. Hence, we pioneer the first non-supervised explainability architecture for review-based collaborative filtering (called NEAR) as our novel contribution to the theory of explanation construction in recommender systems. While maintaining excellent recommendation performance, our approach reformulates explainability as a non-supervised (i.e., unsupervised and self-supervised) explanation generation task. We formally define two explanation types, both of which NEAR can produce. An invariant explanation, fixed for all users, is based on the unsupervised extractive summary of an item's reviews via embedding clustering. Meanwhile, a variant explanation, personalized for a specific user, is a sentence-level text generated by our customized Transformer conditioned on every user-item-rating tuple and artificial ground-truth (self-supervised label) from one of the invariant explanation's sentences. Our empirical evaluation illustrates that NEAR's rating prediction accuracy is better than the other state-of-the-art baselines. Moreover, experiments and assessments show that NEAR-generated variant explanations are more personalized and distinct than those from other Transformer-based models, and our invariant explanations are preferred over those from other contemporary models in real life.
An Evaluation of the Multi-platform Efficiency of Lightweight Cryptographic Permutations

Permutation-based symmetric cryptography has become increasingly popular over the past ten years, especially in the lightweight domain. More than half of the 32 second-round candidates of NIST’s lightweight cryptography standardization project are permutation-based designs or can be instantiated with a permutation. The performance of a permutation-based construction depends, among other aspects, on the rate (i.e. the number of bytes processed per call of the permutation function) and the execution time of the permutation. In this paper we analyze the execution time and code size of assembler implementations of the permutation of Ascon, Gimli, Schwaemm, and Xoodyak on an 8-bit AVR and a 32-bit ARM Cortex-M3 microcontroller. Our aim is to ascertain how well these four permutations perform on microcontrollers with very different architectural and micro-architectural characteristics such as the available register capacity or the latency of multi-bit shifts and rotations. We also determine the impact of flash wait states on the execution time of the permutations on Cortex-M3 development boards with 0, 2, and 4 wait states. Our results show that the throughput (in terms of permutation time divided by rate when the capacity is fixed to 256 bits) of the permutation of Ascon, Schwaemm, and Xoodyak is similar on ARM Cortex-M3 and lies in the range of 41.1 to 48.6 cycles per rate-byte. However, on an 8-bit AVR ATmega128, the permutation of Schwaemm outperforms its counterparts of Ascon and Xoodyak by a factor of 1.20 and 1.59, respectively.
Improved arithmetic optimization algorithm based on sigmoid function and Cauchy Gaussian fusion mutation

To solve the problems of arithmetic optimization algorithm (AOA) ,such as slow convergence speed, low convergence accuracy and easy to fall into local optimization, this dissertation put forward an improved arithmetic optimization algorithm (IAOA) which is improved by many methods. Firstly, the AOA population is initialized by tent chaotic map; Then, the mathematical optimizer acceleration function (MOA) is improved by using sigmoid function; Finally, the Cauchy Gauss fusion mutation strategy is used to mutate the current optimal individual, and the performance of IAOA is verified by the benchmark function. According to the experimental data, a prediction model (IAOA -SVM) for the quality index of finished pellets was established to predict the quality of finished pellets. The simulation results show that the prediction results of IAOA -SVM model are more accurate, which proves the effectiveness of the proposed IAOA in practical application.
Application and Practice of Project-based Teaching in Marine Auxiliary Machinery Course

Project-based teaching mode is an important teaching method in the current college teaching process.It has obvious effect on the cultivation of creative quality of talents.Project-based teaching emphasizes taking students as the main body and learning as the center.Based on a course in marine auxiliary machinery, centrifugal pump cavitation was selected to explore and practice project-based teaching.The application results show that the project-based teaching method can improve the classroom teaching effect to a certain extent.
Self-supervised deep visual servoing for high precision peg-in-hole insertion

Many industrial assembly tasks involve peg-in-hole like insertions with sub-millimeter tolerances which are challenging, even in highly calibrated robot cells. Visual servoing can be employed to increase the robustness towards uncertainties in the system, however, state of the art methods either rely on accurate 3D models for synthetic renderings or manual involvement in acquisition of training data. We present a novel self-supervised visual servoing method for high precision peg-in-hole insertion, which is fully automated and does not rely on synthetic data. We demonstrate its applicability for insertion of electronic components into a printed circuit board with tight tolerances. We show that peg-in-hole insertion can be drastically sped up by preceding a robust but slow force-based insertion strategy with our proposed visual servoing method, the configuration of which is fully autonomous.
University student population: perception of class modality types in the context of COVID-19

Abstract The effects on different human groups caused by social isolation due to the SARS-CoV-2 pandemic are currently being known. In this context, one of the least favored sectors and that even until the end of 2021 had not resumed activities to a great extent of Latin America was the education sector and, in the face of a new variant of the virus, it is possible that these activities will continue virtually in 2022. In this regard, the present study aims to show the appreciation of the students, mainly university students, in order to know the perception and assessment regarding the class modality types in the context of COVID-19. For this, a quantitative and descriptive study was designed based on the interpretation of information obtained from surveys carried out on 198 students from the National University of San Cristóbal de Huamanga in Peru. The results regarding the qualification of learning in the virtual modality of the theoretical classes show that 58.1% of students mention that it was regular and 31.3% that it was good, regarding learning in the virtual modality of the experimental classes, 43.4% mention that it was regular and 38.9% deficient. Their preferences in most cases is to have theoretical classes virtually and/or blended; and experimental laboratory classes in face to face and/or blended; Likewise, under the current context of COVID-19, 85.4% of students state that they prefer their health over their professional training.
Method for Planning a Coverage Trajectory for a Group of UAVs Marking Out Zones for Installing Seismic Modules

Today, the task of planning the trajectory of covering the area with a group of unmanned aerial vehicles (UAVs) remains relevant. This paper presents a method for planning the coverage trajectory when a group of UAVs performs aerial monitoring of the terrain in order to mark out the zones for installing seismic modules. The developed method allows solving the problem of constructing high-precision three-dimensional maps of vast territories by a group of UAVs due to the effective distribution of group agents over various parts of the global trajectory of terrain coverage. The proposed solution not only takes into account the current parameters of the UAV, the distance to the segments of the covering trajectories, but also ensures the minimization of the total time of the aerial monitoring mission. According to the results of experiments, using the proposed method, the average error in reconstructing maps of simulated areas using the ADNN metric was 13.01 cm. In the future, the proposed solution can be modified by introducing new algorithms for the decomposition of the global coverage trajectory, as well as methods for smoothing the covering trajectories.
On the Impact of Model Tolerance in Power Grid Anomaly Detection Systems

Rapid development in deep learning-based detection systems for numerous industrial applications has opened opportunities to apply them in power grids. A consumer’s power consumption can be monitored to recognize any anomalous behavior in their household. When building such detection systems, evaluating their robustness to adversarial samples is critical. It has been shown that when we provide adversarial samples to deep learning models, they falsely classify instances, even when the perturbation or noise added to the original data is very small. On the other hand, these models should be able to detect attack instances correctly and raise few to no false alarms. While this expectation can be difficult to attain, we are allowed to choose a threshold that decides the extent to which the detection and false alarm rates are compromised. To this end, we explore the threshold selection problem for state-of-the-art deep learning-based detection models such that it can recognize attack instances. We show that selecting a threshold is challenging, and even if an appropriate threshold is chosen, the tolerance of a model to adversarial samples can still leave avenues for an attack to be successful.
Implement the Data Conversion System by Using $$\alpha $$-Lightweight Coreset for Validation Process

In recent years, data have been created and stored in different places with various formats and types. This causes a lot of difficulties for data analysis and data mining which can make profits for every aspect of social applications based on these valuable data. In order to overcome this problem, data conversion is a crucial step that we have to build for linking and merging different data resources into a unified data store. This paper implements a complete data conversion system based on the elastic data conversion framework. In addition, we also apply the $$\alpha $$ -Lightweight Coreset for the data validation process of this system.
Hoax Classification in Imbalanced Datasets Based on Indonesian News Title using RoBERTa

Hoaxes are something that can not be avoided, especially in Indonesia, where the literacy rate in Indonesia is quite low, they are easy to believe in news without doing fact check. The worst thing is that news that is trusted by the public may not be read completely through the entire content. They believe, from the title alone could already covers the entire content of the news. Media in the other side, are also competing to make controversial titles so that their traffic is improved. The research that will be carried out by us is where we can classify a news whether it is a fact or a hoax from the title alone. The RoBERTa (A Robustly Optimized BERT Pretraining Approach) model will be used in this study, because in several previous studies RoBERTa has proven to be good for classification. The accuracy achieved in this study also reached 99.52% with an accuracy validation of 93.84% which shows that even with an imbalanced dataset the classification shows a promising result by using the RoBERTa model which data is balanced using the undersampling method.