Reviewerly/exHarmony huggingface dataset:
Image Restoration by Learning Morphological Opening-Closing Network

Abstract Mathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing . Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net
Turning Transport Data to Comply with EU Standards While Enabling a Multimodal Transport Knowledge Graph

Complying with the EU Regulation on multimodal transportation services requires sharing data on the National Access Points in one of the standards (e.g., NeTEx and SIRI) indicated by the European Commission. These standards are complex and of limited practical adoption. This means that datasets are natively expressed in other formats and require a data translation process for full compliance. This paper describes the solution to turn the authoritative data of three different transport stakeholders from Italy and Spain into a format compliant with EU standards by means of Semantic Web technologies. Our solution addresses the challenge and also contributes to build a multi-modal transport Knowledge Graph of interlinked and interoperable information that enables intelligent querying and exploration, as well as facilitates the design of added-value services.
Feasibility and user-experience of virtual reality in neuropsychological assessment following stroke

Virtual Reality (VR) offers the possibility to assess cognitive functioning in a dynamic environment resembling daily life. In this cross-sectional study, we used two user interfaces, namely non-immersive VR by using a computer monitor (CM) and immersive VR by using a head-mounted display (HMD). We investigated (1) potential differences in feasibility, user-experience, and a potential preference for one user interface over another between stroke patients and healthy controls; (2) potential differences in feasibility, user-experience, and preference between patients referred for inpatient rehabilitation care and patients referred for outpatient rehabilitation care; and (3) potential demographic and clinical characteristics that were related to patients' preference for one user interface over another. Stroke patients (n = 88) and healthy controls (n = 66) performed a VR-task with a CM and HMD. Both user interfaces were feasible to use, irrespective of clinical referral (in- or outpatient rehabilitation care). Patients reported an enhanced feeling of engagement, transportation, flow, and presence, but more negative side effects when tested with a HMD, compared to a CM. The majority of stroke patients had no preference for one user interface over the other, yet younger patients tended to prefer a HMD. VR seems highly feasible in stroke patients.
Multi-Target Multi-Camera Tracking With Optical-Based Pose Association

Multi-target multi-camera tracking (MTMCT) targets to generate trajectories of the object that appeared under multiple cameras automatically. MTMCT can be treated as a combination of intra-camera tracking and cross-camera tracking. The existing work only employs the global description to perform the tracklet generating. However, the global description cannot model the local similarity between targets, leading to existing methods not to be robust to occlusion and fast motion. To handle the mentioned problem, we propose an online Optical-based Pose Association (OPA) for multi-target multi-camera tracking. The proposed method utilizes local pose matching to solve the occlusion problem, and applies optical flow to reduce the distance caused by fast motion. For optical-based pose association, we firstly employ OpenPose to generate human pose for each proposal. Then, we utilize the optical flow generated by PWC-Net to adjust the estimated pose for the previous frame. Finally, the modified Object Keypoint Similarity is used to compute the similarity between the pose of the current frame and adjusted pose in the prior frame. Once obtaining the optical-based pose similarity, we combine it with the visual and bounding box spatial similarities to generate the final similarity matrix, and apply the Kuhn-Munkras algorithm for data association. The experiments on the MTMCT and MOT datasets verify the rationality of using human pose information and prove the superiority of the proposed method.
Toward Smart Security Enhancement of Federated Learning Networks

As traditional centralized learning networks (CLNs) are facing increasing challenges in terms of privacy preservation, communication overheads, and scalability, federated learning networks (FLNs) have been proposed as a promising alternative paradigm to support the training of machine learning (ML) models. In contrast to the centralized data storage and processing in CLNs, FLNs exploit a number of edge devices (EDs) to store data and perform training distributively. In this way, the EDs in FLNs can keep training data locally, which preserves privacy and reduces communication overheads. However, since the model training within FLNs relies on the contribution of all EDs, the training process can be disrupted if some of the EDs upload incorrect or falsified training results, that is, poisoning attacks. In this article, we review the vulnerabilities of FLNs, and particularly give an overview of poisoning attacks and mainstream countermeasures. Nevertheless, the existing countermeasures can only provide passive protection and fail to consider the training fees paid for the contributions of the EDs, resulting in an unnecessarily high training cost. Hence, we present a smart security enhancement framework for FLNs. In particular, a verify-before-aggregate (VBA) procedure is developed to identify and remove the non-benign training results from the EDs. Afterward, deep reinforcement learning (DRL) is applied to learn the behaving patterns of the EDs and to actively select the EDs that can provide benign training results and charge low training fees. Simulation results reveal that the proposed framework can protect FLNs effectively and efficiently.
A simultaneous calibration technique of the extrinsic and turntable for structured-light-sensor-integrated CNC system

The center of the mandrel-target is unique and does not get affected by the extrinsic angle of the projector. On this basis, a mandrel-target-based method is described to simultaneously calibrate the extrinsic parameters and the turntable of a multi-axis, structured-light-sensor-integrated, three-dimensional (3-D) scanning system. Besides, a cross-section-arc algorithm is developed to identify the conjugate pair. By computing the mandrel-center in the light plane, some collinear scanning points are generated along with the spindle scanning. Further, these calibration points are employed to the tip-tilt self-calibration of the turntable. In the two-scanning strategy, three sets of non-colinear points are derived simultaneously by relatively moving the sensor at distinct world coordinates. These generated points are fulfilled to calibrate the extrinsic parameters with the relative motion model. By determining the direction vectors, the skewed sensor coordinates are converted into the Cartesian world coordinates. The effectiveness of the proposed method is verified using a set of experiments. The accuracy of the proposed model is justified by comparing it with the measurements of a CMM trigger probe. The absolute measurement accuracy for a mandrel-target (about 7.4827 mm in radius and 16 mm in depth) is 0.002 mm. The target is cost-effective and simple in shape. The calibration procedure is easy to carry out and highly accurate.
Digitization as politics: Smart farming through the lens of weak and strong data

This paper offers an alternative approach for engaging sociologically, ontologically, and politically with digital farming platforms. A challenge faced by any approach looking to upend intellectual conventions, especially ontological ones, lies in the question of representation, namely, how do we talk about digitization in novel theoretical ways using language rooted in more than two millennia of western human-centered thought? One way to deal with this challenge, the one adopted in this paper, involves decentering familiar terms and repurposing them. Thus, rather than organizing the argument around familiar terms like small and big data, I argue instead that we organize data-assemblages according to what they do, which makes this a political ontological project. To do this, I offer the relational predicate of weak data and strong data, suggesting that they are good to think with in this regard. The argument ultimately lands at a place for analyzing these platforms that offers the potential for critique and perhaps even a degree of optimism, as I suggest a framework for normatively evaluating these practices. The paper draws upon various empirical studies of digital agriculture conducted by the author, which include interviews with farmers, farm laborers, hacktivists, investors, and engineers from numerous countries and locales.
An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods

In this paper, we introduce a new evidential clustering algorithm based on finding the belief-peaks and disjoint neighborhoods, called BPDNEC. The basic idea of BPDNEC is that each cluster center has the highest possibility of becoming a cluster center among its neighborhood and neighborhoods of those cluster centers are disjoint in vector space. Such possibility is measured by the belief notion in framework of evidence theory. By solving an equation related to neighborhood size, the size of such disjoint neighborhoods is determined and those objects having highest belief among their neighborhoods are automatically detected as cluster centers. Finally, a credal partition is created by minimizing an objective function concerning dissimilarity matrix of data objects. Experimental results show that BPDNEC can automatically detect cluster centers and derive an appropriate credal partition for both object data and proximity data. Simulations on synthetic and real-world datasets validate the conclusions.
Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation

The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures.
Chameleons' Oblivion: Complex-Valued Deep Neural Networks for Protocol-Agnostic RF Device Fingerprinting

Prior work has demonstrated techniques for fingerprinting devices based on their network traffic or transmitted signals, which use software artifacts or characteristics of the underlying protocol. However these approaches are not robust or applicable in many real-world scenarios. In this paper we explore the feasibility of device fingerprinting under challenging realistic settings, by identifying artifacts in the transmitted signals caused by devices' unique hardware “imperfections”. We develop RF-DCN, a novel Deep Complex-valued Neural Network (DCN) that operates on raw RF signals and is completely agnostic of the underlying applications and protocols. We introduce two DCN variations: a retrofitted Convolutional DCN (CDCN) originally created for acoustic signals, and a novel Recurrent DCN (RDCN) for modeling time series. Our work demonstrates the feasibility of operating on raw I/Q data collected within a narrowband spectrum from open air captures across vastly different modulation schemes. In contrast to prior work, we do not utilize knowledge of the modulation scheme or protocol intricacies such as carrier frequencies. We conduct an extensive experimental evaluation on large and diverse datasets as part of a DARPA red team evaluation, and investigate the effects of different environmental factors as well as neural network architectures and hyperparameters on our system's performance. Our novel RDCN consistently outperforms all baseline neural network architectures, is robust to noise, and can identify a target device even when numerous devices are concurrently transmitting within the band of interest under the same or different protocols. While our experiments demonstrate the applicability of our techniques under challenging conditions where other neural network architectures break down, we identify additional challenges in signal-based fingerprinting and provide guidelines for future explorations.
Minority Reports Defense: Defending Against Adversarial Patches

Deep learning image classification is widely used yet is vulnerable to adversarial attack, which can change the computer classification without changing how humans classify the image. This is possible even if the attacker changes just a small patch of the image. We propose a defense against patch attacks based on partially occluding the image around each candidate patch location, so that a few occlusions each completely hide the patch. We demonstrate on CIFAR-10, Fashion MNIST, and MNIST that our defense provides certified security against patch attacks of a certain size. For CIFAR-10 and a \(5 \times 5\) patch, we can provide certify accuracy for 43.8% of images, at a cost of only 1.6% in clean image accuracy compared to the architecture we defend or a cost of 0.1% compared to our training of that architecture, and a 0.1% false positive rate.
Construction and validation of a test to assess (pre-service) teachers' technological pedagogical knowledge (TPK)

As society becomes increasingly digital, teachers must be trained to integrate technology effectively into their classrooms. Teachers’ technological pedagogical knowledge (TPK), as defined in the TPACK framework, is considered an important prerequisite for effectively integrating technology. The TPACK framework has received a great deal of attention, yet few knowledge tests have been developed that directly assess TPK. However, those tests are crucial for evaluating the effectiveness of teacher education courses on technology integration. We have developed a 17-item test that covers teacher knowledge about various digital technologies as employed in teaching. Experts rated the items to represent the construct adequately. Data obtained from 245 pre-service teachers supports the test’s internal structure. Concerning convergent and discriminant validity, the pre-service teachers’ test scores were not related to their self-reported TPK, but to their self-reported technological knowledge. The test was sensitive to changes in pre-service teachers’ TPK through teacher education courses.
Spam review detection using self-organizing maps and convolutional neural networks

Online public reviews have significant influenced customers who purchase products or seek services. Fake reviews are posted online to promote or demote targeted products or reputation of the organizations and businesses. Spam review detection has been the focus of many researchers in recent years. As the online services have been growing rapidly, the importance of the issue is ever increasing and needs to be addressed properly. In this regard, there is a variety of approaches that have been introduced to distinguish truthful reviews from the fake ones. The main features engineered in the past studies typically involve two types of linguistic-based and behavioral-based characteristics of the reviews. Unsupervised, supervised and semi-supervised machine learning methods have been widely utilized to perform such a classification. This paper introduces a novel approach to detect fake reviews from the genuine ones using linguistic features. Unsupervised learning via self-organizing maps (SOM) in conjunction with a convolutional neural networks (CNN) are employed to perform classification of the reviews. We transform the reviews into images by arranging semantically-similar words around a pixel of the image or equivalently a SOM grid cell. The resulting review images are consequently fed to the CNN for supervised training and then classification. Comprehensive tests on two gold-standard datasets show the effectiveness of the proposed method on single and multi-domain contexts with accuracy of 88% and 87%, respectively.
Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network

Hearing-impaired people can interact with other people through sign language. The proposed system tears down the communication barrier between Hard of hearing (HoH) community and those who do not know their sign language. In this paper, we have developed an algorithm to detect and segment the hand region from a depth image using the Microsoft Kinect sensor. The proposed algorithm works well in the cluttered environment, e.g. skin color background and hand overlaps the face. Convolution Neural networks (CNN) are applied to automatically construct features from Indian sign language (ISL) signs. These features are invariant to rotation and scaling. The proposed system recognizes gestures accurately up to 99.3%.
Learning Code-Query Interaction for Enhancing Code Searches

Code search plays an important role in software development and maintenance. In recent years, deep learning (DL) has achieved a great success in this domain-several DL-based code search methods, such as DeepCS and UNIF, have been proposed for exploring deep, semantic correlations between code and queries; each method usually embeds source code and natural language queries into real vectors followed by computing their vector distances representing their semantic correlations. Meanwhile, deep learning-based code search still suffers from three main problems, i.e., the OOV (Out of Vocabulary) problem, the independent similarity matching problem, and the small training dataset problem. To tackle the above problems, we propose CQIL, a novel, deep learning-based code search method. CQIL learns code-query interactions and uses a CNN (Convolutional Neural Network) to compute semantic correlations between queries and code snippets. In particular, CQIL employs a hybrid representation to model code-query correlations, which solves the OOV problem. CQIL also deeply learns the code-query interaction for enhancing code searches, which solves the independent similarity matching and the small training dataset problems. We evaluate CQIL on two datasets (CODEnn and CosBench). The evaluation results show the strengths of CQIL-it achieves the MAP@1 values, 0.694 and 0.574, on CODEnn and CosBench, respectively. In particular, it outperforms DeepCS and UNIF, two state-of-the-art code search methods, by 13.6% and 18.1% in MRR, respectively, when the training dataset is insufficient.
Knowledge Distillation for Face Photo–Sketch Synthesis

Significant progress has been made with face photo-sketch synthesis in recent years due to the development of deep convolutional neural networks, particularly generative adversarial networks (GANs). However, the performance of existing methods is still limited because of the lack of training data (photo-sketch pairs). To address this challenge, we investigate the effect of knowledge distillation (KD) on training neural networks for the face photo-sketch synthesis task and propose an effective KD model to improve the performance of synthetic images. In particular, we utilize a teacher network trained on a large amount of data in a related task to separately learn knowledge of the face photo and knowledge of the face sketch and simultaneously transfer this knowledge to two student networks designed for the face photo-sketch synthesis task. In addition to assimilating the knowledge from the teacher network, the two student networks can mutually transfer their own knowledge to further enhance their learning. To further enhance the perception quality of the synthetic image, we propose a KD+ model that combines GANs with KD. The generator can produce images with more realistic textures and less noise under the guide of knowledge. Extensive experiments and a user study demonstrate the superiority of our models over the state-of-the-art methods.
Breast Cancer Detection Using GAN for Limited Labeled Dataset

Mammography is the primary procedure for breast cancer screening, attempting to reduce breast cancer mortality risk with early detection. Deep learning methods have shown strong applicability to various medical images datasets. Due to paucity of available labeled medical images, accurate computer assisted diagnosis requires intensive data augmentation (DA) techniques, such as geometric/intensity transformations of original images. This data when used along with the training data helps to address the limited medical image dataset collected from various sources. Generative Adversarial Networks (GANs) is one of the DA techniques. GAN trained on images can generate new images that contain many authentic characteristics and look realistic to human observers. Therefore, this paper focuses on overcoming the problem of limited labeled dataset, using Deep Convolution GANs (DCGANs). To analyze the closeness between the original and synthetic images, a visualization tool ImageJ was used. In order to validate the proposed model, a visual Turing test was conducted with the help of medical experts.