Reviewerly/exHarmony huggingface dataset:
Attention-based 3D Object Reconstruction from a Single Image

Recently, learning-based approaches for 3D reconstruction from 2D images have gained popularity due to its modern applications, e.g., 3D printers, autonomous robots, self-driving cars, virtual reality, and augmented reality. The computer vision community has applied a great effort in developing functions to reconstruct the full 3D geometry of objects and scenes. However, to extract image features, they rely on convolutional neural networks, which are ineffective in capturing long-range dependencies. In this paper, we propose to substantially improve Occupancy Networks, a state-of-the-art method for 3D object reconstruction. For such we apply the concept of self-attention within the network's encoder in order to leverage complementary input features rather than those based on local regions, helping the encoder to extract global information. With our approach, we were capable of improving the original work in 5.05% of mesh IoU, 0.83% of Normal Consistency, and more than 10× the Chamfer-L1 distance. We also perform a qualitative study that shows that our approach was able to generate much more consistent meshes, confirming its increased generalization power over the current state-of-the-art.
Compressive Recovery Defense: Defending Neural Networks Against ℓ<sub>2</sub>, ℓ<sub>∞</sub>, and ℓ<sub>0</sub> Norm Attacks

We consider the problem of defending neural networks against adversarial inputs. In particular, we extend the framework introduced in [1] to defend neural networks against ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> , and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm attacks. We call this defense framework Compressive Recovery Defense (CRD) as it utilizes recovery algorithms from the theory of compressive sensing. For defending against ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm attacks, we use Basis Pursuit (BP) as the recovery algorithm and for the case of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm attacks, we utilize the Dantzig Selector (DS) with a novel constraint. For each recovery algorithm used, we provide rigorous recovery guarantees that do not depend on the noise generating mechanism and can therefore be utilized by CRD against any ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> , or ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm attacks. Finally, we experimentally demonstrate that CRD is effective in defending neural networks against state of the art ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm attacks.
CORPORATE CYBERESPIONAGE: IDENTIFICATION AND PREVENTION PART 1

The purpose of this paper is to present an argument for a methodology to detect and prevent cyber-based corporate espionage. The focus of this paper will be on the human domain and how it affects control measures in the electronic and physical domain of cybersecurity. The reason for this approach is because the human domain has consistently been identified as the irrational and unpredictable component in cybersecurity. Human actions, when related to cyber-based corporate espionage, cannot be described through rudimentary factors supporting causation for “betrayal” and the motivation to compromise proprietary technology and economic information belonging to a business entrusting its secrets with an employee. The underlying issue is about how to identify a potential actor willing to compromise their “integrity” in exchange for personal gain or personal interest even when the penalty will be far greater than the reward. The next step is to propose preventative measures to either dissuade such behavior or expose a threat actor intending to compromise a corporation’s information assets.
SBN: Scale Balance Network for Accurate Salient Object Detection

Recent great progress has been made on Salient Object Detection (SOD) by deep Convolutional Neural Networks (CNNs). However, most SOD methods still suffer from scale imbalance issue, which pays more attention on large salient areas but ignores small salient areas though they belong to the same object. To address this issue, this paper proposes a Scale Balance Network (SBN) to accurately locate large salient areas and recognize small salient areas. Firstly, a backbone network specifically designed for object detection is adopted in this paper, which captures larger resolution with more spatial features in deeper layers. Secondly, to focus on the balance between the large salient areas and the small salient areas, this paper proposes a novel Connective Feature Pyramid Module (CFPM) for sufficiently leveraging the multi-scale features and the multi-level features, which includes Feature Coherence Enhancement (FCE) and Feature Progressive Extraction (FPE). FCE is designed to enhance the coherence between high-level and low-level features, and FPE is designed to extract the progressive features in different convolutional layers. Finally, an Edge Enhancement Architecture with Various Kernels (EEAVK) is proposed to refine the edge features. Experimental results on five benchmark datasets show that the proposed method outperforms or achieves consistently superior performance in comparison with other methods under different evaluation metrics.
Development of Pictograms to Communicate Technological Solution Instructions (Labeling) Among Low-Literacy Users

Conveying information for appropriate use of devices is uniquely challenging in low-resource settings. Drug makers have previously developed culturally meaningful informational pictograms to, for example, convey timing of doses, among low-literacy patients. We tested a similarly structured process among Ugandan smallholder farmers. Over 67% of the pictograms scored a passing grade after the second round of testing, meeting our overall success criterion. The process is efficacious in developing instructional/teaching (labeling) pictograms. These findings can help solution/device developers for low-resource settings to provide correctly interpretable pictograms and thus eliminate misuse-driven low uptake.
Deployment of SE-SqueezeNext on NXP BlueBox 2.0 and NXP i.MX RT1060 MCU

Convolution neural system is being utilized in field of self-governing driving vehicles or driver assistance systems (ADAS), and has made extraordinary progress. Before the CNN, conventional AI calculations helped ADAS. Right now, there is an incredible investigation being done in DNNs like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN designs and made it increasingly appropriate to actualize on real-time embedded systems. Due to the model size complexity of many models, they cannot be deployed straight away on real-time systems. The most important requirement will be to have less model size without a tradeoff with accuracy. Squeeze-and-Excitation SqueezeNext which is an efficient DNN with best model accuracy of 92.60% and with least model size of 0.595MB is chosen to be deployed on NXP BlueBox 2.0 and NXP i.MX RT1060. This deployment is very successful because of its less size and better accuracy. The model is trained and validated on CIFAR-10 dataset.
Adaptive Secondary Transform For Improved Image Coding Efficiency In JPEG2000

This paper proposes a secondary transform for wavelet based image compression, whose aim is to predict detail sub-bands from the corresponding low-pass sub-band so as to improve energy compaction. The proposed prediction strategy exploits the orientation of local image features to untangle aliasing components in the low-pass sub-band, and achieves prediction by projecting the cleaned synthesized low-pass sub-band back into the wavelet domain. At low bit rates, the proposed scheme can considerably reduce distortion in a JPEG 2000 based compression framework, especially for images with strong geometric flow.
Threshold-Based Low Power Consumption Human Fall Detection for Health Care and Monitoring System

The limitation of time are often seen as the primary cause of the absence of people assisting elderly. Non-invasive, inexpensive, and easy-to-use healthcare approaches becomes quite critical to support continuous health monitoring and the needs of healthcare. Smartphone is one of the devices that have limited specification. But smartphone is powerful enough to become human fall detection because it already been included with sensors and elderly has accepted and used it (especially in the future). Threshold-based low power consumption human fall detection by using accelerometer-based with four critical characteristics, Alpha Degree, and AGPeak can be the answer to solves the problem specially to achieve low power consumption computation. By using this thresholding-based fall detection method, it can be implemented in smartphone (low power device) and give fast classification result, under 0.001 second/data - below the realtime response tolerance.
The French Correction: When Retrieval Is Harder to Specify than Adaptation

A common idea in the field of case-based reasoning is that the retrieval step can be specified by the use of some similarity measure: the retrieved cases maximize the similarity to the target problem and, then, the adaptation step has to take into account the mismatches between the retrieved cases and the target problem in order to this latter. The use of this methodological schema for the application described in this paper has proven to be non efficient. Indeed, designing a retrieval procedure without the precise knowledge of the adaptation procedure has not been possible. The domain of this application is the correction of French sentences: a problem is an incorrect sentence and a valid solution is a correction of this problem. Adaptation consists in solving an analogical equation that enables to execute the correction of the retrieved case on the target problem. Thus, retrieval has to ensure that this application is feasible. The first version of such a retrieval procedure is described and evaluated: it is a knowledge-light procedure that does not use linguistic knowledge about French.
Graph Convolutional Neural Networks to Classify Whole Slide Images

Conventional biomedical measurement and tests used for patient diagnosis are increasingly digitized in the current clinical practice, which provides great opportunities for researchers to build smart computer-aided decision support systems using advanced data analytics. In this paper, we discuss how to build such systems for whole slide images (WSIs), the digitization of entire histology slides used by pathologists. While conventional techniques use grid-style-tile-based processing strategy with extracted features aggregated based on first-order statistics to detect regions of interest such as cancerous regions, they ignore the spatial relationships between nearby tiles: the closer the two tiles are spatially, the more similar they are in disease conditions. To capture such spatial proximity information, we present a novel application of graph convolutional networks (GCNs) to analyze WSIs. We model each tile of a WSI as a node in a graph, and apply GCNs to the resulting graph to aggregate the features for final classification. We use two histopathological image classification datasets, a breast cancer pathological dataset of 58 total samples with 36 benign ones, and another colon cancer dataset of 100 H&E images with 49 benign ones. The diagnosis accuracy achieved by GCN is consistently better than that achieved by conventional methods. The experiments results showed the potential of graph-based neural networks to improve biomedical imaging data analysis.
Development of Tracking and Control System Based on Computer Vision for RoboMaster Competition Robot

In this paper, we present a tracking and control system used to auto-aiming and shooting some given targets. By combining the traditional computer vision method and the neutral network method, our detection for some specific objects achieves real-time (100fps+) and it is robust enough to work on most normal situations. To achieve better performance in shooting moving targets, we fuse the IMU data to do the motion prediction. This makes the system outperforms the human players in hit rate. This system is applied for the RoboMaster competition as an aim-support system and shows good results.
Evaluating the Effect of User-Given Guiding Attention on the Learning Process

Most current supervised learning systems require large quantities of labelled data, limiting their applicability in domains where labelled data is scarce and hard to obtain. We introduce a novel approach for incorporating additional, user-given areas of interest during training by which the learning process can be guided. The provided guiding attention is incorporated in the training phase as a form of data augmentation, which ensures that input dimensions do not vary between train and test/deployment time, when no guiding attention is present. We evaluate this approach by extending the CIFAR-10 dataset with prototypical information and ascertain, that our approach reduces the required amount of samples by up to 44.89%, when combined with traditional data augmentation techniques. This would enable the use of learning systems in parts of manufacturing such as commissioning, where additional samples are scarce and costly to obtain while providing guiding attention is a matter of seconds.
Randomizing the Self-Adjusting Memory for Enhanced Handling of Concept Drift

Real-time learning from data streams in non-stationary environments gains ever more relevance due to the exponentially increasing amounts of generated data. Recently, the Self-Adjusting Memory (SAM) was proposed, an algorithm able to robustly handle heterogeneous types on the basis of two dedicated memories for the current and former concepts that continuously preserve consistency with explicit filtering. Yet, since the algorithm is restricted to one memory architecture, the variety of possible alternatives is limited by design in favor of an overall model consistency. Moreover, it does not actively detect drift, thus adapting with a relatively high delay in case of abrupt changes. We propose a dynamic ensemble on the basis of the SAM algorithm, which is triggered by both, the inherent passive adaptation of SAM and active drift detection. Further, since SAM is based on the stable k-Nearest-Neighbor algorithm, we investigate multiple approaches to obtain a high diversity in the ensemble, resulting in an effective overall strategy. The increased computational demand is countered on the basis of a parallel implementation. We extensively evaluate the method on numerous benchmarks, where it consistently achieves superior results in comparison to state-of-the-art methods.
Open Source DMIs: Towards a Replication Certification for Online Shared Projects of Digital Musical Instruments

The internet has allowed for the flourishing of several shared projects. Developers from different parts of the world can work asynchronously on the same project, formulating ideas, and implementing complex systems. For remote collaboration in software development projects, sharing information and code becomes more straightforward, given the textual and abstract nature of the source. However, for projects involving hardware and physical artifacts development, it is necessary to have a more detailed level of information, since many other characteristics of the project need to be covered in the documentation. In the case of digital musical instruments, which are physical artifacts aimed at musical interaction, we have several aspects such as mechanical structure, electronic, programming, mapping, and sound design. Currently, there are few initiatives in the literature that indicate possible ways to guide designers and developers in the process of documenting the replication of their projects. Given the importance of advancing the area of new interfaces for musical expression, the diffusion of ideas among innovators, and the popularization of innovative musical instruments, this paper discusses the challenges of sharing DMIs. Our main goal is to propose a checklist to help designers and developers to share their projects to increase the potential for replicability. As future steps, we hope to move towards a certificate that guarantees how replicable a given project is considering makers other than its developers. Besides, with better documentation and a more organized sharing process, we expect to encourage designers and developers to reproduce existing DMIs in different parts of the world so we could understand the adoption of these devices in different contexts and cultures. This paper presents a step towards making the projects more accessible to make the DMI community more connected.
Deep Learning in Health Care: Automatic Cervix Image Classification Using Convolutional Neural Network

Cervical malignancy can be viably counteracted if identified in the pre-cancerous stage. In order to appropriately treat cervical cancer, making an accurate determination of a patient’s cervical type is critical. However, doing so can be difficult, even for trained healthcare providers, because of the thin-line difference among the various cervix types. Kaggle and Mobile ODT have distributed a gathering of a few thousand commented on photographs of cervices. In this paper, we utilize profound learning approaches in computer vision, for example, convolutional neural networks and transfer learning. We try different things to experiment the models, for example, batch normalization, image augmentation, and dataset methodologies, for example, cropping the images. The initiations are utilized for the preprepared model Inception v3 which was prepared on the ImageNet dataset of 1.2 million pictures.
Comparative Study of Prewitt and Canny Edge Detector Using Image Processing Techniques

Medical image processing provides the information regarding the detection of brain tumor. Image processing techniques are used to find out the brain tumor with the help of various steps. The steps are image acquisition of MRI images, image preprocessing, image enhancement which improves the quality of image, feature extraction and classification. Edge detection is a process of classifying and perceiving shrill cutouts in an image. The disjointedness is unforeseen deviation in pixel strength gray-level value. Edge detection is an important technique in many image processing applications such as object recognition, motion analysis, pattern recognition and medical image processing. In this manuscript various edge detection techniques are discussed. This paper shows the comparison between Prewitt and Canny operators with various images to extract edge of objects in the images. Comparison of these edge detectors shows that Canny produced the sharpest images and produced the best continuity of the edge lines. Results also show that Canny produces sharpest and clear edge than Prewitt.
Soft Rotation Equivariant Convolutional Neural Networks

A key to the generalization ability of Convolutional Neural Networks (CNNs) is the idea that patterns that appear in one region of the image have a high probability of appearing in other regions. This notion is also true for other spatial relationships, such as orientation. Motivated by the fact that in the early layers of CNNs distinct filters often encode for the same feature at different angles, we propose to incorporate the rotation equivariant prior in these models. In this work, different regularization strategies that capture the notion of approximate equivariance were designed and quantitatively evaluated in their ability to generate rotation-equivariant models and their effect on the model's capacity to generalize to unseen data. Some of these strategies consistently lead to higher test set accuracies when compared to a baseline model, on classification tasks. We conclude that the rotation equivariance prior should be adopted in the general setting when modeling visual data.
Policy Making Analysis and Practitioner User Experience

This article presents the work on social media analysis-driven policy-making platforms that are powered by classic social media analysis technologies, such as policy modelling, linguistic analysis, opinion mining, sentiment analysis and information visualization. The approach examines the user design perspective towards user experience in policymaking for all the innovative modules used. The technology behind such complex task is presented while the resulting platform is appraised on the potential for real world application. The findings drive the development and the requirements for the summative usability assessment tests. We also report on the level the practitioners adopted the policy formulation tools.
Foveation for Segmentation of Mega-Pixel Histology Images

Segmenting histology images is challenging because of the sheer size of the images with millions or even billions of pixels. Typical solutions pre-process each histology image by dividing it into patches of fixed size and/or down-sampling to meet memory constraints. Such operations incur information loss in the field-of-view (FoV) (i.e., spatial coverage) and the image resolution. The impact on segmentation performance is, however, as yet understudied. In this work, we first show under typical memory constraints (e.g., 10G GPU memory) that the trade-off between FoV and resolution considerably affects segmentation performance on histology images, and its influence also varies spatially according to local patterns in different areas (see Fig. 1). Based on this insight, we then introduce foveation module, a learnable “dataloader” which, for a given histology image, adaptively chooses the appropriate configuration (FoV/resolution trade-off) of the input patch to feed to the downstream segmentation model at each spatial location (Fig. 1). The foveation module is jointly trained with the segmentation network to maximise the task performance. We demonstrate, on the Gleason2019 challenge dataset for histopathology segmentation, that the foveation module improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off. Moreover, our model achieves better segmentation accuracy for the two most clinically important and ambiguous classes (Gleason Grade 3 and 4) than the top performers in the challenge by 13.1% and 7.5%, and improves on the average performance of 6 human experts by 6.5% and 7.5%.