Reviewerly/exHarmony huggingface dataset:
Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data

Nowadays, robotics, AR, and 3D modeling applications attract considerable attention to single-view depth estimation (SVDE) as it allows estimating scene geometry from a single RGB image. Recent works have demonstrated that the accuracy of an SVDE method hugely depends on the diversity and volume of the training data. However, RGB-D datasets obtained via depth capturing or 3D re-construction are typically small, synthetic datasets are not photorealistic enough, and all these datasets lack diversity. The large-scale and diverse data can be sourced from stereo images or stereo videos from the web. Typically being uncalibrated, stereo data provides disparities up to unknown shift (geometrically incomplete data), so stereo-trained SVDE methods cannot recover 3D geometry. It was recently shown that the distorted point clouds obtained with a stereo-trained SVDE method can be corrected with additional point cloud modules (PCM) separately trained on the geometrically complete data. On the contrary, we propose <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GP^{2}$</tex> , General-Purpose and Geometry-Preserving training scheme, and show that conventional SVDE models can learn correct shifts themselves without any post-processing, benefiting from using stereo data even in the geometry-preserving setting. Through experiments on dif-ferent dataset mixtures, we prove that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GP^{2}$</tex> -trained mod-els outperform methods relying on PCM in both accuracy and speed, and report the state-of-the-art results in the general-purpose geometry-preserving SVDE. Moreover, we show that SVDE models can learn to predict geometrically correct depth even when geometrically complete data com-prises the minor part of the training set.
A Low-Complexity Method to Address Process Variability in True Random Number Generators based on Digital Nonlinear Oscillators

We discuss a monitoring system aiming to select, among a set of integrated entropy sources affected by process variability, the source guarantying the highest worst-case entropy. The approach is particularly suitable when considering True Random Number Generators based on Digital Nonlinear Oscillators, since multiple instances of the entropy sources can be implemented at a reduced hardware cost. In general, the approach can be applied for TRNGs based on parametric systems, thus offering entropy tuning capabilities. The original theoretical results have been validated with experiments.
Towards Understanding the Behaviors of Pretrained Compressed Convolutional Models

We investigate the behaviors that compressed convolutional models exhibit for two key areas within AI trust: (i) the ability for a model to be explained and (ii) its ability to be robust to adversarial attacks. While compression is known to shrink model size and decrease inference time, other properties of compression are not as well studied. We employ several compression methods on benchmark datasets, including ImageNet, to study how compression affects the convolutional aspects of an image model. We investigate explainability by studying how well compressed convolutional models can extract visual features with t-SNE, as well as visualizing localization ability of our models with class activation maps. We show that even with significantly compressed models, vital explainability is preserved and even enhanced. We find with applying the Carlini & Wagner attack algorithm on our compressed models, robustness is maintained and some forms of compression make attack more difficult or time-consuming.
Capturing Prior Knowledge in Soft Labels for Classification with Limited or Imbalanced Data

Successful applications of deep learning often depend on large amount of training data. However, in practical image recognition tasks, available training data are often limited or imbalanced across classes, causing the over-fitting issue or the prediction bias issue during model training. In this paper, based on word embedding models from studies in natural language processing, the prior knowledge about the relationships between image classes is utilized to help train more generalizable classifiers under the condition of limited or class-imbalanced training data. Such inter-class relational knowledge is captured in the word embedding vectors for the textual names of image classes. Using these word embedding vectors as soft labels for corresponding image classes, the feature extractor part of a deep learning model can be guided to learn to extract visual features which contain both class-specific and class-shared information. Experiments on multiple image classification datasets confirm that the proposed learning framework helps improve model performance when training data is limited or class-imbalanced.
Evading Deepfake Detectors via High Quality Face Pre-Processing Methods

Today, various multimedia content can be accessed and shared from any location via the Internet. In addition to normal content, an extensive amount of manipulated multimedia can raise various social concerns. Among the various types of manipulated media, deepfakes can be abused in impersonation or spreading fake information. Therefore, numerous studies have been performed to detect deepfakes to alleviate these concerns, and studies such as FaceForensics++ (FF++) and DeepFake Detection Challenge (DFDC) have sparked these studies by providing deepfake datasets. The deepfake datasets were utilized for supervised learning in conjunction with developing sophisticated neural networks and showed a high detection performance. Since powerful neural networks can learn even subtle details about an image, they must be trained on realistic deepfakes created by advanced deepfake generation technologies to improve the robustness of existing detectors. In order to boost the performance of deepfake detection models, we propose an approach to creating more realistic deepfake images by removing "detectable" artifacts from existing deepfake datasets’ images, including GAN-based images. By applying the proposed method to the original deepfake dataset, we demonstrate that our technique can significantly reduce the detection performance of existing deepfake detectors. Our experimental results show the vulnerability of deployed detectors and pave the way for further improvement.
Codes for Multiple-Access Asynchronous Techniques

This article discusses asynchronous techniques with spreading sequences (codes) that are statistically uncorrelated for arbitrarily random starting points. We provide improved codes for the spread-spectrum multiple-access asynchronous techniques. The article provides methods for generating improved codes, as well as estimates of their parameters. We investigate the cross-correlation of codes for arbitrarily random starting points. The comparative analysis shows that with a slight increase in the correlation modulus, we can significantly increase the cardinality of the spreading codes. This useful property can be used in future radio control systems and future smart grids to implement soft capacity, i.e. the base station can increase the subscriber capacity with a slight decrease in the quality of service.
Fine Tuning and Comparing Tacotron 2, Deep Voice 3, and FastSpeech 2 TTS Models in a Low Resource Environment

Text-to-speech (TTS) models are used to generate speech from a sequence of characters provided as input. Existing TTS systems require a high-quality large dataset and vast computational resources for training. However, most of the publicly available datasets do not meet such standards, and access to powerful GPUs may not always be possible. Hence, in our work, we have successfully trained and compared TTS models, specifically Tacotron 2, FastSpeech 2, and Deep Voice 3 on a Tesla T4 GPU using a subset of the LJSpeechl.1 dataset. Subsequently, we have surveyed to analyze the performance of the models when trained on small datasets, and we discovered that the Tacotron 2 TTS model synthesized the most realistic sounding speeches. The survey revealed that the Tacotron 2 TTS model achieved a mean opinion score (MOS) at a 95% confidence interval of 4.25± 0.17, and sounded the most natural to our listeners when compared to the ground truth.
Are Research Universities Meeting the Educational Challenge of the New Economy?

Rapid technological change is affecting workforce structure and deepening employment polarization. Driven by automation, data science, and increasingly artificial intelligence and cyber-physical systems, new employment opportunities require knowledge and skills in emerging fields or higher technical level that are in short supply in the current workforce. Closing this gap challenges traditional universities to develop diverse portfolios of accessible and affordable programs of industry-relevant content available to working adults for part-time study. As the pace of technological change continues to accelerate and working lifespans continue to increase, successful careers are predicated on lifelong learning, upskilling, and reskilling. Academia, recognizing this challenge, is moving continuing professional education, especially online learning, into the university's core mission. However, there are significant hurdles to integrating online programs into traditional research departments. This paper identifies the main difficulties and proposes approaches for their resolution.
Substantiation of the technological feasibility of using permanent polymer formwork for reinforced concrete screw piles installing

The article presents the results of research in the field of reinforced concrete screw piles installation. One of the possible ways to reduce the values of torques during the construction of the cavity, for the subsequent formation of the screw piles body, is proposed. In the laboratory, a polyethylene terephthalate coating was used, which is sufficiently strong to tear and shock, and is also resistant to aggressive chemical influences. During the research, it was found that such a coating reduces the friction of reinforced concrete piles with the ground and can be used as a permanent form for the manufacture of permanent reinforced concrete piles. Also, the use of pile coatings will significantly reduce labor costs both at the manufacturing stage and at the stage of installing prefabricated monolithic reinforced concrete screw piles.
Camera Motion Compensation and Person Detection in Construction Site Using Yolo-Bayes Model

For autonomous driving of construction machinery such as construction crane, it is necessary to detect objects (e.g. person) in a construction site using the camera hung from the crane’s boom. Since the camera is attached to the boom, it is in motion as the boom moves from one place to another. In this research, we propose a camera motion compensation and person detection technique in a construction site for moving camera. The motion parameters are estimated using point-to-point features correspondences with RANSAC-like algorithm. The algorithm selects the best model with few randomly selected sample points. After compensating the motion effect, a Bayesian interface along with the Yolo model (called Yollo-Bayes model) is used for person detection. Two types of Bayes models are proposed to detect missing person by the Yolo. Bayes model for moving person is used to detect the person when he/she is in motion and Yolo fail to detect him/her. On the other hand, Bayes model for nonmoving person is used when the person is not in motion and Yolo miss to detect him/her. The motion estimation accuracy is confirmed by creating ground truth images with translation and rotation changes. To evaluate the performance of the Yolo-Bayes model, extensive experimental evaluations have been done with a lot of video images collected from the real construction site. It reveals that the proposed Yolo-Bayes model outperforms Yolo model. The proposed model is more effective when the number of training sample is small (F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</inf> – score is 5.77% higher than Yolo) or the model is tested on a completely new dataset (F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</inf> – score is 13.75% higher than Yolo).
Don’t Reinvent the Wheel: Towards Automatic Replacement of Custom Implementations with APIs

Reusing code is a common practice in software development: It helps developers speedup the implementation task while also reducing the chances of introducing bugs, given the assumption that the reused code has been tested, possibly in production. Despite these benefits, opportunities for reuse are not always in plain sight and, thus, developers may miss them. We present our preliminary steps in building RETIWA, a recommender able to automatically identify custom implementations in a given project that are good candidates to be replaced by open source APIs. RETIWA relies on a "knowledge base" consisting of real examples of custom implementation-to-API replacements. In this work, we present the mining strategy we tailored to automatically and reliably extract replacements of custom implementations with APIs from open source projects. This is the first step towards building the envisioned recommender.
Adversarial Example Attacks against ASR Systems: An Overview

With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot. As The models get simpler, the difficulty of development and deployment become easier, ASR systems are getting closer to our life. On the one hand, we often use APPs or APIs of ASR to generate subtitles and record meetings. On the other hand, smart speaker and self-driving car rely on ASR systems to control AIoT devices. In past few years, there are a lot of works on adversarial examples attacks against ASR systems. By adding a small perturbation to the waveforms, the recognition results make a big difference. In this paper, we describe the development of ASR system, different assumptions of attacks, and how to evaluate these attacks. Next, we introduce the current works on adversarial examples attacks from two attack assumptions: white-box attack and black-box attack. Different from other surveys, we pay more attention to which layer they perturb waveforms in ASR system, the relationship between these attacks, and their implementation methods. We focus on the effect of their works.
CPA-Planner: Motion Planner With Complete Perception Awareness for Sensing- Limited Quadrotors

Motion planning in unknown environments suffers from the limited sensing range of cameras and occlusions. Therefore, perception awareness (PA) should be fully considered to ensure flight safety. Specifically, the unknown part of the planned trajectory should be sensed in advance (visibility constraint) at a safe distance (safety constraints). However, most of the optimization-based methods considered PA merely in the optimization stage in terms of the occlusions and ignored the limitation of the sensing model. As a result, the PA constraints remain violated due to poor initialization. Meanwhile, existing sampling methods can only ensure the visibility constraint rather than the safety constraint. In this paper, an optimization-based planning framework is proposed to solve the raised problems in real-time. Specifically, in the path-finding stage, a fast PA checking algorithm based on incremental convex sensing hulls is proposed and combined with the motion primitive propagation to find the initial path that satisfies the PA constraints. Subsequently, in the trajectory optimization stage, perception awareness with respect to occlusion and sensing model are jointly considered and formulated into the nonlinear optimization problem based on a grid map. Benchmarks with the state-of-the-art and physical experiments show that the proposed planner can generate safe trajectories with the highest success rate in different occluded environments and achieve comparable efficiency and aggressiveness simultaneously.
Solar Panels Detection of High-Resolution Aerial Images Based on Improved Faster-RCNN

Detecting and counting solar panels from high-resolution aerial images timely and accurately is essential for monitoring and management of industrial solar photovoltaic (PV) systems. Due to the influence of weather and light, the detection results of traditional methods are usually unsatisfactory. For the purpose of improving detection accuracy, we propose a method that combined residual network and channel attention module to improve the Faster RCNN framework. First, the balance between the training data size and model complexity is investigated, and the residual network is utilized to deepen the feature extractor within the effective range. Then, the channel attention modules are introduced into the network to further enhance the feature representation. Experimental results, conducted on high-resolution aerial image over Guilin, China, prove that the proposed method can detect solar panels with better accuracy than other related methods.
Semi-supervised Learning Method for Object Detection based on Adjacent Frame Consistency Measurement

Deep neural network requires a large amount of labeled data to train the parameters of the model, but the cost of data labeling is high, and only a small part of the collected data is labeled as the dataset. Therefore, a large amount of unlabeled data is not used in the training process. The reasonable utilization of unlabeled data has attracted much attention from the academic and industrial areas. In this paper, a semi-supervised learning method for object detection based on the consistency measurement of adjacent frames was proposed. The semi-supervised learning task of the object detection model was completed by using the semantic similarity of the adjacent frames in the video. We found that the adjacent frames of the video have similar semantic properties and meet the requirements of consistency samples. Using adjacent frames as consistency samples to generate the loss on unlabeled data, the average precision of the neural network could get 83.0%.
A STUDY OF THE CORRELATION BETWEEN SOCIONIC PERSONALITY TYPES AND TEMPERAMENT TYPES

Purpose. The article is concerned with socionics, which is a typological approach-based framework that serves for modelling of the human capacity to perceive, process and produce information. The article studies the current state of socionics and the areas for further development. One of the primary focus areas of socionics is the experimental studies aiming to prove the construct validity of socionics. For this purpose, the correlation between socionic dichotomies and different psychologic models, such as a model of temperament, is studied. There are three hypotheses on a correlation between socionic personality types and temperament types.&#x0D; Methodology. To conduct the research we used methods of socionic type identification, the Eysenck Personality Questionnaire adapted by Shmelev A.G., the “Type of Behavioral Activity” questionnaire by Wasserman L.I., and Gumenyuk N.V., and calculated the point-biserial correlation coefficient.&#x0D; Results. The correlation analysis revealed a positive correlation between the Extraversion scale of the Eysenck Personality Questionnaire and the poles of Extraversion, Ethics and Declaring. A positive correlation is revealed between the Neuroticism scale of the Eysenck Personality Questionnaire and the Dynamic socionic pole, as well as between the Type of Behavioral Activity scale and the Extraversion pole. The findings confirm the hypothesis suggested by Gulenko W.W., according to which the socionic types correlate with the temperament types. They also confirm the hypotheses by Filatova E.S., and Stepanov R.A. / Lytov D.A., according to which Extraversion sensu Eysenck correlate to Extraversion, the socionic pole, and disprove their hypotheses on the correlation between the Neuroticism scale and socionic dichotomies.&#x0D; Practical implications. The findings contribute to proving the construct validity of the socionics model. This ensures that practitioners who use socionic methods draw on the experimentally verified hypotheses.
Curriculum Defense: An Effective Adversarial Training Method

To resolve the challenge of adversarial attacks, several methods have been proposed. Adversarial training is one of the most effective defense methods. However, most of the adversarial training methods sacrifice the accuracy on the clean testing dataset and demonstrate the mediocre performance under the multi-step attacks. In our opinion, building a trustworthy machine learning system is an iterative learning process. The process by which a machine builds intelligence is similar to the process by which the human brain builds intelligence, following a process from easiness to difficulty. Such a learning technology can be implemented by curriculum learning. In this paper, we propose a curriculum learning based method to improve the robustness of the visual model. We treat adversarial examples as augmented data whose fitting difficulties surpass the clean examples. The “curriculum dropout” regularization mechanism is added in the adversarial defense to improve the adversarial robustness under the attack. In our experiment, we first guarantee the generalization of the model without the perturbations and then try to train the neural visual classifier under the single-step Fast Gradient Signed Method (FGSM) attack and multi-step Projected Gradient Descent (PGD) attack. The considerable generalization ability and robustness on the CIFAR-10 dataset could be achieved via the curriculum defense method. Our finding in the curriculum defense can be highlighted that the wide neural network model with fewer forgetting defects would remember even more. The accuracy of most models on the clean testing dataset would rise if the new sub-task contains similar but varied data compared with data in old sub-tasks, and the accuracy growth of the wide neural network model is highest.
Adjustable acoustics for imperfect rooms

Very often, acousticians are faced with room dimensions that are fixed and cannot be adjusted. This dilemma is exascerbated when the room is to be used for purposes of music performance. It is further complicated when the goal of the room is to present a broad spectrum of music types from classical symphony to modern jazz or rock. An example will be presented of an essentially square room space that was designed and treated to be adaptable to meet the requirements of all types of performances. Tabor College in Hillsboro, KS had space on its campus to build one concert that had to serve all of its performance needs. The physical space available, in order to maximize the size of the hall, also turned out to be a nearly perfect square in the audience area. Tom Ryan of The Hallani Group, working with the author and RealAcoustix, designed an acoustical system that allows the room to perform well for all types of music performances while being easy to change and architecturally beautiful. The design criteria, auralizations, and other information will be presented showing how this was accomplished.