Reviewerly/exHarmony huggingface dataset:
FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration

Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the recognition decision and eventually lead to improved results in challenging scenarios. While much progress has been made in face image quality assessment in recent years, computing reliable quality scores for diverse facial images and FR models remains challenging. In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent. As such, the proposed approach is the first to link image quality to adversarial attacks. Comprehensive (cross-model as well as model-specific) experiments are conducted with four benchmark datasets, i.e., LFW, CFP–FP, XQLFW and IJB–C, four FR models, i.e., CosFace, ArcFace, CurricularFace and ElasticFace, and in comparison to seven state-of-the-art FIQA methods to demonstrate the performance of FaceQAN. Experimental results show that FaceQAN achieves competitive results, while exhibiting several desirable characteristics. The source code for FaceQAN is available at https://github.com/LSIbabnikz/FaceQAN.
R(Det)<sup>2</sup>: Randomized Decision Routing for Object Detection

In the paradigm of object detection, the decision head is an important part, which affects detection performance significantly. Yet how to design a high-performance decision head remains to be an open issue. In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection. First, we disentangle the decision choices and prediction values by plugging soft decision trees into neural networks. To facilitate effective learning, we propose randomized decision routing with node selective and associative losses, which can boost the feature representative learning and network decision simultaneously. Second, we develop the decision head for object detection with narrow branches to generate the routing probabilities and masks, for the purpose of obtaining divergent decisions from different nodes. We name this approach as the randomized decision routing for object detection, abbreviated as R(Det)2. Experiments on MS-COCO dataset demonstrate that R(Det)2 is effective to improve the detection performance. Equipped with existing detectors, it achieves 1.4 ~ 3.6% AP improvement.
Utilizing Software Architecture Recovery to Explore Large-Scale Software Systems in Virtual Reality

Exploring an unfamiliar large-scale software system is challenging, especially when based solely on source code. While software visualizations help in gaining an overview of a system, they generally neglect architecture knowledge in their representations, e.g., by arranging elements along package structures rather than functional components or locking users in a specific abstraction only slightly above the source code. In this paper, we introduce an automated approach for software architecture recovery and use its results in an immersive 3D virtual reality software visualization to aid accessing and relating architecture knowledge. We further provide a semantic zoom that allows a user to access and relate information both horizontally on the same abstraction level, e.g., by following method calls, and vertically across different abstraction levels, e.g., from a class to its containing component. We evaluate our contribution in a controlled experiment contrasting the usefulness regarding software exploration and comprehension of our concepts with those of the established CityVR visualization and the Eclipse IDE.
A Meta-Analysis Survey on the Usage of Meta-Heuristic Algorithms for Feature Selection on High-Dimensional Datasets

Feature selection (FS) using meta-heuristic algorithms on high-dimensional datasets (HDD) is becoming more prevalent due to the continuous advancement in data mining. However, the difficulty in identifying the threshold of features in a dataset to be categorised as HDD remains an issue due to the different schools of thought on this matter. Therefore, this survey intended to determine the threshold for a number of features to be HDD, and subsequently identify the trend or potential FS method for HDD and the most preferred meta-heuristic algorithms and classifiers for both wrapper-based and filter-based FS methods to analyse HDD. This study performed an extensive systematic literature review by implementing the PRISMA guidelines on 62 research articles that were published between 2016 to 2021. This survey proposed a novel grouping technique called literal grouping and data grouping (LGDG) to accurately group the chosen articles based on HDD. The LGDG method serves as a guide for other researchers who intend to perform FS research related to HDD. Literal grouping refers to searching for selected papers using specific keywords, like HDD in this case. While data grouping compares the number of features in datasets towards the threshold, which is set at 2,000 features by the majority. Based on the analyses of all the LGDG groupings, the filter-based FS method gained more attention in recent years with competent results no less than wrapper-based, especially on HDD. Besides that, Moth Flame Optimisation works well in filter-based methods, whereas Cuckoo Optimisation Algorithm works well in wrapper-based, while Whale Optimisation Algorithm works well in both FS methods. As for the classifier’s preferences, SVM, DT, and NB are preferred by the filter-based, while KNN is preferred by the wrapper-based method. It can be recommended that reviewing other aspects such as multi-objective FS on HDD and including more FS methods could be included in future studies as an extension to this survey.
Improving Extreme Low-Bit Quantization With Soft Threshold

Deep neural networks executing with low precision at inference time can gain acceleration and compression advantages over their high-precision counterparts, but need to overcome the challenge of accuracy degeneration as the bit-width decreases. This work focuses on under 4-bit quantization that has a significant accuracy degeneration. We start with ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values. We find that the hard threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> introduced in previous ternary networks for determining quantization intervals and the suboptimal solution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> limit the performance of the ternary model. To alleviate it, we present Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine ternarized values instead of depending on a hard threshold. Based on it, we further generalize the idea of soft threshold from ternarization to arbitrary bit-width, named Soft Threshold Quantized Networks (STQN). We observe that previous quantization relies on the rounding-to-nearest function, constraining the quantization solution space and leading to a significant accuracy degradation, especially in low-bit ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leq3$ </tex-math></inline-formula> -bits) quantization. Instead of relying on the traditional rounding-to-nearest function, STQN is able to determine quantization intervals by itself adaptively. Accuracy experiments on image classification, object detection and instance segmentation, as well as efficiency experiments on field-programmable gate array (FPGA) demonstrate that the proposed framework can achieve a prominent tradeoff between accuracy and efficiency. Code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WeixiangXu/STTN</uri> .
Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

This article presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full</i> posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-Gaussian</i> setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association).
Metaverse for Developing Engineering Competency

The objective of this research is to develop a metaverse framework for engineering competency development. This research is divided into two phases. In the first phase, it will study and synthesize the elements of a metaverse framework for engineering competency development. The researcher has studied, analyzed, and synthesized data on various issues from related documents and research. It was then developed into a metaverse framework for engineering competency development. We found that a metaverse framework for engineering competency development comprises three components, namely: metaverse, metaverse platform, and engineering competency for future ready. Using the metaverse in engineering will help improve and develop the engineering management system. It is also useful for workforce production or engineering personnel to have appropriate performance and meet the needs of the world workforce market. So, using the metaverse to train engineers who can meet international standards and play a full role in engineering is a good way to get ready for the digital transformation that will happen in the future.
Social Networking Applications: A Comparative Analysis for a Collaborative Learning through Google Classroom and Zoom

Recently, social network applications were developed intensively due to the increasing compaction and user demands. These applications provide different services to their users like learning, awareness, chatting with friends, sharing global news, etc. Simply, this work introduces the advantages of these software applications, specifically in the field of education during the COVID 19 spread. Google Classroom and Zoom meetings had gained the attention of many educational institutes for using them as a learning platform for students and educators. This research used two methodologies SWOT analysis and the information system success model of DeLone and McLean's updated to evaluate the effectiveness of these applications. SWOT analysis was performed for the Zoom meetings and google classroom, then evaluated their effectiveness. Likewise, DeLone and McLean's model was deployed for evaluation, an empirical survey was used and distributed in our college. The results were collected, analyzed, and studied using various statistical parameters. Practically, each application has its pros and cons. However, google classroom showed more functionality for the learning process than the Zoom application.
L2E: Learning to Exploit Your Opponent

Opponent modeling is essential to exploit sub-optimal opponents in strategic interactions. Most previous works focus on building explicit models to predict the opponents' styles or strategies, which require a large amount of data to train the model and lack adaptability to unknown opponents. In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling. L2E acquires the ability to exploit opponents through a few interactions with different opponents during training of a neural network and can quickly adapt to new opponents with unknown styles during testing. To automatically produce challenging and diverse opponents for training, we further present a novel opponent strategy generation algorithm. We evaluate L2E on two poker games and one grid soccer game, which are the commonly used benchmarks for opponent modeling. Comprehensive experimental results indicate that L2E rapidly adapts to diverse styles of unknown opponents.
Modular Action Concept Grounding in Semantic Video Prediction

Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional pre-diction, which sidesteps the learning of interaction between agents and objects. We introduce the task of semantic action-conditional video prediction, which uses semantic action labels to describe those interactions and can be regarded as an inverse problem of action recognition. The challenge of this new task primarily lies in how to effectively inform the model of semantic action information. Inspired by the idea of Mixture of Experts, we embody each abstract label by a structured combination of various visual concept learn-ers and propose a novel video prediction model, Modular Action Concept Network (MAC). Our method is evaluated on two newly designed synthetic datasets, CLEVR-Building- Blocks and Sapien-Kitchen, and one real-world dataset called Tower-Creation. Extensive experiments demonstrate that MAC can correctly condition on given instructions and generate corresponding future frames without need of bounding boxes. We further show that the trained model can make out-of-distribution generalization, be quickly adapted to new object categories and exploit its learnt features for object detection, showing the progression towards higher-level cognitive abilities. More visualizations can be found at http://www.pair.toronto.edu/mac/.
Sequence Labeling Algorithms for Punctuation Restoration in Brazilian Portuguese Texts

Punctuation Restoration is an essential post-processing task of text generation methods, such as Speech-to-Text (STT) and Machine Translation (MT). Usually, the generation models employed in those tasks produce unpunctuated text, which is difficult for human readers and might degrade the performance of many downstream text processing tasks. Thus, many techniques exist to restore the text’s punctuation. For instance, approaches based on Conditional Random Fields (CRF) and pre-trained models, such as the Bidirectional Encoder Representations from Transformers (BERT), have been widely applied. In the last few years, however, one approach has gained significant attention: casting the Punctuation Restoration problem into a sequence labeling task. In Sequence Labeling, each punctuation symbol becomes a label (e.g., COMMA, QUESTION, and PERIOD) that sequence tagging models can predict. This approach has achieved competitive results against state-of-the-art punctuation restoration algorithms. However, most research focuses on English, lacking discussion in other languages, such as Brazilian Portuguese. Therefore, this paper conducts an experimental analysis comparing the Bi-Long Short-Term Memory (BI-LSTM) + CRF model and BERT to predict punctuation in Brazilian Portuguese. We evaluate those approaches in the IWSLT2 2012-03 and OBRAS dataset in terms of precision, recall, and $$F_1$$ -score. The results showed that BERT achieved competitive results in terms of punctuation prediction, but it requires much more GPU resources for training than the BI-LSTM + CRF algorithm.
A Survey on Sentimental Analysis of Student Reviews Using Natural Language Processing (NLP) and Text Mining

Sentiment Analysis (SA) is a Natural Language Processing (NLP) application. It's sometimes referred to as "opinion mining" or "emotion extraction". Sentiment Analysis is a growing area of study in text mining and a popular research subject for opinion mining in education, which analyses and comprehends students' attitudes toward their learning in order to improve the quality of decision-making. More innovative approaches have been developed in online education, which provide an efficient way to learn irrespective of Gender, geographical discrimination, age, etc. As more and more reviews or feedback are posted during online classes, these messages should be correctly classified to identify the theme behind them. Machine Learning (ML) and Deep Learning (DL) based classification models are used to solve sentiment classification problems. Machine Learning (ML) algorithms Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and Decision Tree (DT) and Deep Learning (DL) algorithms especially Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are most frequently used algorithms for Natural Language processing (NLP). It allows us to have a deeper understanding of the student's emotions. It may also be used to recognize and analyse feelings in user comments or reviews on various blogs, websites, social media, online communities, and other sites in order to better understand their perspectives. Thus, the most addressed domain was the use of sentiment analysis in the context of higher education and the evaluation of teaching quality. It would be very helpful for instructors to improve learning strategies by assessing both the instructors and students' progress.
Learning Deep Non-blind Image Deconvolution Without Ground Truths

Non-blind image deconvolution (NBID) is about restoring a latent sharp image from a blurred one, given an associated blur kernel. Most existing deep neural networks for NBID are trained over many ground truth (GT) images, which limits their applicability in practical applications such as microscopic imaging and medical imaging. This paper proposes an unsupervised deep learning approach for NBID which avoids accessing GT images. The challenge raised from the absence of GT images is tackled by a self-supervised reconstruction loss that approximates its supervised counterpart well. The possible errors of blur kernels are addressed by a self-supervised prediction loss based on intermediate samples as well as an ensemble inference scheme based on kernel perturbation. The experiments show that the proposed approach provides very competitive performance to existing supervised learning-based methods, no matter under accurate kernels or erroneous kernels.
Gated2Gated: Self-Supervised Depth Estimation from Gated Images

Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of sequentially scanning a scene and directly recording depth via the photon time-of-flight, as in pulsed LiDAR sensors, gated imagers encode depth in the relative intensity of a handful of gated slices, captured at megapixel resolution. Although existing methods have shown that it is possible to decode high-resolution depth from such measurements, these methods require synchronized and calibrated LiDAR to supervise the gated depth decoder - prohibiting fast adoption across geographies, training on large unpaired datasets, and exploring alternative applications outside of automotive use cases. In this work, propose an entirely self-supervised depth estimation method that uses gated in-tensity profiles and temporal consistency as a training signal. The proposed model is trained end-to-end from gated video sequences, does not require LiDAR or RGB data, and learns to estimate absolute depth values. We take gated slices as input and disentangle the estimation of the scene albedo, depth, and ambient light, which are then used to learn to reconstruct the input slices through a cyclic loss. We rely on temporal consistency between a given frame and neighboring gated slices to estimate depth in regions with shadows and reflections. We experimentally validate that the proposed approach outperforms existing super-vised and self-supervised depth estimation methods based on monocular RGB and stereo images, as well as super-vised methods based on gated images. Code is available at https://github.com/princeton-computational-imaging/Gated2Gated.
Toward Data Security in 6G Networks: A Public-Key Searchable Encryption Approach

With the advances of the fifth generation (5G) mobile communication technology, smart applications enhance the quality of daily life, the urban management by the government and the effective allocation of resources. Smart applications collect data through the Internet of Things, store massive data in the cloud server, and use cloud computing and deep learning to analyze the data, according to the data analysis results to guide human production and life. However, with the breakthrough of 6G technology, the amount of data applied is increasing, and the demand for privacy protection is becoming more prominent. Encrypting data using traditional cryptographic algorithms can solve the problem of privacy leakage, but it hampers the availability of the data. Searchable encryption is a special encryption structure with keyword search, which balances the availability and privacy of massive data. In this article, we analyze some typical security and privacy issues in 6G-based applications, discuss the solutions to these problems, and present a framework of 6G-based smart cities with searchable encryption, which provides a guarantee for the privacy and availability of smart city data (including ciphertext search, access control, etc.). We also propose a searchable encryption solution based on ciphertext-policy attribute-based encryption to solve the conflict between security and data availability of the smart cities as a specific scenario in order to demonstrate the contribution of cryptographic technologies such as public-key searchable encryption to the 6th generation mobile communication technology.
Models and Measures in Theory and Practice of Manufacturing Processes

Deterministic and probabilistic models of measured quantities, processes and fields in control systems of manufacturing processes, as well as physical and probabilistic measures, make it possible to form a measurement result, to provide it with the properties of objectivity and reliability. The measuring instruments necessary for obtaining new knowledge on the manufacturing process are being developed and improved. Issues of improving and developing models and measures in measurement methodology play an increasingly important role in achieving high measurement accuracy and expanding the areas in control systems and expert systems for manufacturing processes. The measure is formed by many factors, the action of most of which is of a random nature. It is possible to determine such a measure as probabilistic measure, which can be applied, for example, transmission of measurement data via communication channels, registration of the measurement results of manufacturing processes. The stochastic approach in the theory of measurements is of particular importance in the case of measurements of physical quantities that have a probabilistic nature. The basic idea of the paper is to do transformation from the well-known triad "model → algorithm → program" to a more reasonable methodology "model → measure → algorithm → program". The methodology allows to improve accuracy and reliability of the obtained measurement results of control systems and expert systems of manufacturing processes.