Reviewerly/exHarmony huggingface dataset:
Differentially private GANs by adding noise to Discriminator’s loss

Differentially private generative adversary network (GAN) is a very promising field in data privacy, with many practical real-world applications. The idea of differentially private GAN is to provide differential privacy protection for sensitive training datasets. By using differentially private GANs, training datasets can be protected from being remembered or encoded into GAN’s parameters or generated data. Therefore, generated data can be safely used for data augmentation or replacing real sensitive data with very little privacy loss. However, existing methods for differentially private GANs are notoriously inefficient, most of them use a modified Tensorflow library, i.e., Tensorflow Privacy provided by Google, which is not only hard for programming but also very time-consuming even on TPU. In this paper, we provide a simpler and more efficient way to achieve differentially private GANs: when we set the training process of a GAN under a certain manner, we can use discriminator’s loss as vehicle to achieve differential privacy. Compared with existing methods, our method needs no modification on Tensorflow core functions and will not significantly drag down the training process. We test our method on GANs for real-world datasets MNIST, Fashion MNIST, and SVHN datasets, experimental results show that our method is easy to implement, more practical, and more efficient than existing methods. With these advantages, we believe our method for differentially private GANs will be widely used in the very near feature.
Prototype transfer generative adversarial network for unsupervised breast cancer histology image classification

Breast cancer (BC) has become a common tumor that threatens women's health. The decision on the treatment for breast cancer depends on multi-classification. Therefore, for preventive diagnosis, the development of automatic malignant BC detection system suitable for patient imaging can reduce the burden on pathologists and help avoid misdiagnosis. At present, most of the research methods are supervised learning methods that require lots of labeled data, and annotating histology images is more difficult and expensive due to the complicated disease representation in breast cancer. In this paper, we propose an unsupervised learning method, named prototype transfer generative adversarial network (PTGAN), which embeds generative adversarial networks and prototypical networks for classifying a large number of data sets by training a transfer learning model from a small number of labeled source data sets from similar domain. Without requiring lots of labeled target images, this method also reduces the style difference between the source and target domains by generating an adversarial network, thereby it can effectively reduce the pixel-level distribution gap for breast histology images captured from different devices with individual style. Then, it embeds the feature vectors learned by a prototype network into the metric space, which can distil discriminative knowledge from the prototype into target domain. We then use a special “distance” in the metric space to train a classifier to predict the large amounts of target data. The experimental results on the BreakHis dataset show that the accuracy of the proposed PTGAN for classifying benign and malignant tissues has reached nearly 90%. This proves the advantage of our method in providing an effective tool for breast cancer multi-classification in clinical settings, economizing the complicated annotating cost.
CACrowdGAN: Cascaded Attentional Generative Adversarial Network for Crowd Counting

Crowd counting is a valuable technology for extremely dense scenes in the transportation. Existing methods generally have higher-order inconsistencies between ground truth density maps and generated density maps. To address this issue, we incorporate an attentional discriminator to take charge of checking the density map between the generator and the ground truth. Thus, a Cascaded Attentional Generative Adversarial Network (CACrowdGAN) is proposed that enables the attentional-driven discriminator to distinguish implausible density maps and simultaneously to guide the generator to deliver fine-grained high quality density maps. The proposed CACrowdGAN consists of two components: an attentional generator and a cascaded attentional discriminator. The attentional generator has an attention module and a density module. The attention module is developed for the generator to focus on the crowd regions of the input images, while the density module is used to provide the attentional input of the discriminator. In addition, a cascaded attentional discriminator is proposed to synthesize attentional-driven fine-grained details at different crowd regions of the input image and compute a per-pixel fine-grained loss for training generator. The proposed CACrowdGAN achieves the state-of-the-art performance on five popular crowd counting datasets (ShanghaiTech, WorldEXPO’10, UCSD, UCF_CC_50 and UCF_QNRF), which demonstrates the effectiveness and robustness of the proposed approach in the complex scenes.
Exposing previously undetectable faults in deep neural networks

Existing methods for testing DNNs solve the oracle problem by constraining the raw features (e.g. image pixel values) to be within a small distance of a dataset example for which the desired DNN output is known. But this limits the kinds of faults these approaches are able to detect. In this paper, we introduce a novel DNN testing method that is able to find faults in DNNs that other methods cannot. The crux is that, by leveraging generative machine learning, we can generate fresh test inputs that vary in their high-level features (for images, these include object shape, location, texture, and colour). We demonstrate that our approach is capable of detecting deliberately injected faults as well as new faults in state-of-the-art DNNs, and that in both cases, existing methods are unable to find these faults.
Efficient dynamic multi-replica auditing for the cloud with geographic location

Data backup is a pervasive strategy adopted by cloud service providers (CSPs) to avoid potential risks of data loss. As a result, a CSP normally maintains multiple replicas for each piece of data on geographically distributed servers to improve reliability. A key problem of this replication technique is that users will be charged more when more replicas are stored. Therefore, an auditing service is desired to help users verify whether an untrusted CSP stores all their replicas in different geographic locations or not. In this paper, we propose a dynamic multi-replica auditing scheme that has the following features: (1) it can verify both the integrity and geographic locations of a cloud user’s data replicas; (2) the proposed scheme can identify different copies with geographic locations, which reduces the complexity in data preprocessing, dynamic operations and backup recovery; (3) by introducing an Indexed Merkle Hash Tree (IMHT), we can reduce overall costs of existing Merkle Hash Trees. We prove the security of the proposed scheme under the random oracle model, and further provide a comprehensive comparison between the proposed scheme and existing schemes. The theoretical analysis and experiment evaluation show that our scheme reduces both the communication and computation costs compared with existing schemes. The analysis also shows that the proposed scheme can achieve 99% verification probability by challenging only 90 data blocks.
Artificial Intelligence Techniques in Smart Cities Surveillance Using UAVs: A Survey

The security and urbanization challenge is expected to rise to 90% by 2050, and to leverage existing resources, technology is the solitary means to cope with this anticipated raise in entail. The Smart City is focused on the smooth convergence of Information and Communication Technology with the most technological innovations like well-connected home and equipment. Smart city augments the lifestyle of its residents by providing efficacious infrastructure and enhanced security. Surveillance is a recurring and monotonous assignment that descends the performance of human guards when continued for a longer period of time. Unmanned Aerial Vehicles (UAVs) or Drones can be deployed as security cameras to augment human guards. It can be deployed to track intruders, monitor unusual activities such as theft, violence and unprecedented corona-virus pandemic scenarios. UAV based visual surveillance in Smart cities, produces a huge amount of multimedia data. The need to process and analyze the data automatically in real-time is critical. Artificial Intelligence and Deep learning imitates human intelligence and provides excellent analytical capabilities to learn about complex data obtained in real environments. The integrated solution of Deep learning technology with the UAVs an electronic eye-in-the-sky has leveraged the capability of detection, recognition and deterrence in a scalable surveillance system. A comprehensive review on the potential benefits of UAVs and its applications for surveillance in smart cities has been presented. This chapter elaborates seamless integration of UAVs and Deep Learning technologies solutions for smart city surveillance. The paper concludes with a description of main challenges for the application of UAVs in deep learning solutions.
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice.
Training Generative Adversarial Networks in One Stage

Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a general training scheme that enables training GANs efficiently in only one stage. Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort. We also computationally analyze the efficiency of the proposed method, and empirically demonstrate that, the proposed method yields a solid 1.5× acceleration across various datasets and network architectures. Furthermore, we show that the proposed method is readily applicable to other adversarial-training scenarios, such as data-free knowledge distillation. The code is available at https://github.com/zju-vipa/OSGAN.
Calculation of vibrational eigenenergies on a quantum computer: Application to the Fermi resonance in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>CO</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>

We apply a modified version of the multistate contracted variational quantum eigensolver method to calculate vibrational eigenstates of ${\mathrm{CO}}_{2}$ on a quantum computer. A two-mode model of ${\mathrm{CO}}_{2}$ is employed, and the vibrational wave function is expanded using three harmonic-oscillator basis functions for each mode. The wave functions are mapped to four qubits by a compact mapping method. The Hamiltonian matrix elements are evaluated on a simulator including noise and on a quantum computer available at IBM Quantum, while the Hamiltonian matrix is diagonalized on a classical computer. We propose an error mitigation method by which the shift of the numerical values of the matrix elements originating from the noise can be corrected, and examine the dependence of the statistical uncertainties on the number of executions of each quantum circuit. We find that, at about $8\ifmmode\times\else\texttimes\fi{}{10}^{6}$ executions, the energy eigenvalues of the Fermi resonance states in ${\mathrm{CO}}_{2}$ can be obtained with an uncertainty within 1 ${\mathrm{cm}}^{\ensuremath{-}1}$.
Repeated Exposure to Illusory Sense of Body Ownership and Agency Over a Moving Virtual Body Improves Executive Functioning and Increases Prefrontal Cortex Activity in the Elderly

We previously showed that the illusory sense of ownership and agency over a moving body in immersive virtual reality (displayed in a first-person perspective) can trigger subjective and physiological reactions on the real subject’s body and, therefore, an acute improvement of cognitive functions after a single session of high-intensity intermittent exercise performed exclusively by one’s own virtual body, similar to what happens when we actually do physical activity. As well as confirming previous results, here, we aimed at finding in the elderly an increased improvement after a longer virtual training with similar characteristics. Forty-two healthy older subjects (28 females, average age = 71.71 years) completed a parallel-group randomized controlled trial (RCT; UMIN000039843, umin.ac.jp) including an adapted version of the virtual training previously used: while sitting, participants observed the virtual body in a first-person perspective (1PP) or a third-person perspective (3PP) performing 20 min of virtual high-intensity intermittent exercise (vHIE; the avatar switched between fast and slow walking every 2 min). This was repeated twice a week for 6 weeks. During the vHIE, we measured the heart rate and administered questionnaires to evaluate illusory body ownership and agency. Before the beginning of the intervention, immediately after the first session of vHIE, and at the end of the entire intervention, we evaluated the cognitive performance at the Stroop task with online recording of the hemodynamic activity over the left dorsolateral prefrontal cortex. While we confirm previous results regarding the virtual illusion and its physiological effects, we did not find significant cognitive or neural improvement immediately after the first vHIE session. As a novelty, in the 1PP group only, we detected a significant decrease in the response time of the Stroop task in the post-intervention assessment compared to its baseline; coherently, we found an increased activation on left dorsolateral prefrontal cortex (lDLPFC) after the entire intervention. While the current results strengthen the impact of the virtual full-body illusion and its physiological consequences on the elderly as well, they might have stronger and more established body representations. Perhaps, a longer and increased exposure to those illusions is necessary to initiate the cascade of events that culminates to an improved cognitive performance.
Decentralized geoprivacy: leveraging social trust on the distributed web

Despite several high-profile data breaches and business models that commercialize user data, participation in social media networks continues to require users to trust corporations to safeguard their personal data. Since these data increasingly contain geographic references that allude to individuals’ locations and movements, the need for new approaches to geoprivacy and data sovereignty has grown. We develop a geoprivacy framework that couples two emerging technologies – decentralized data storage and discrete global grid systems – to facilitate fine-grained user control over the ownership of, access to and map-based representation of their data. The framework is illustrated with a dynamic k-anonymity model that links the geographic precision of shared data to social trust within in a social network. In this framework, users’ spatio-temporal data are shared through a decentralized system and are represented on a discrete global grid data model at spatial resolutions that correspond to varying degrees of trust between individuals who are exchanging information. Our framework has several advantages over centralized geoprivacy approaches, namely trust in a third-party entity is not required and geoprivacy is dynamic and context-dependent with users maintaining autonomy. As the distributed web begins to emerge, so too can the next generation of geographic information sharing tools.
Factors Affecting the Academic Achievement of Nursing College Students in a Flipped Learning Simulation Practice

A flipped learning simulation practice is composed of two parts. First, it involves learning a practical subject in advance via video in a place other than the classroom, then performing a simulation practice consisting of pre-learning, simulation, and debriefing sections. This study was performed to determine and confirm the factors affecting the level of academic achievement of nursing college students in a flipped learning simulation practice. A cross-sectional descriptive design was used. The participants were 160 nursing students who had experience of a flipped learning simulation practice in a nursing college in South Korea. The factors measured were the general characteristics of the participants, the participants’ academic achievement, the analysis ability of the flipped learning class, the participants’ self-directed learning preparation, the participants’ self-efficacy, and the participants’ learning satisfaction. Data were collected from September to November, 2019. As a result of the analysis, we found that the factor that had the greatest influence on the academic achievement of nursing students was self-efficacy, followed by gender, flipped learning education experience, learning satisfaction, age, and the analysis ability of the flipped learning class. In the nursing practicum, nursing educators need to pay attention to the use of flipped learning simulation practice and the factors affecting the academic achievement of nursing college students. In flipped learning simulation practice, specific interventions and strategies are required to improve the academic achievement of nursing college students.
ICINet: ICI-Aware Neural Network Based Channel Estimation for Rapidly Time-Varying OFDM Systems

A novel intercarrier interference (ICI)-aware orthogonal frequency division multiplexing (OFDM) channel estimation network ICINet is presented for rapidly time-varying channels. ICINet consists of two components: a preprocessing deep neural subnetwork (PreDNN) and a cascaded residual learning-based neural subnetwork (CasResNet). By fully taking into account the impact of ICI, the proposed PreDNN first refines the initial channel estimates in a subcarrier-wise fashion. In addition, the CasResNet is designed to further enhance the estimation accuracy. The proposed cascaded network is compatible with any pilot patterns and robust against mismatched system configurations. Simulation results verify the superiority of ICINet over existing networks in terms of better performance and much less complexity.
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text.

Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP systems is a crucial aspect and requires a guarantee that the decisions they make are fair and robust. Aligned with this, we propose a novel framework GYC, to generate a set of exhaustive counterfactual text, which are crucial for testing these ML systems. Our main contributions include a) We introduce GYC, a framework to generate counterfactual samples such that the generation is plausible, diverse, goal-oriented, and effective, b) We generate counterfactual samples, that can direct the generation towards a corresponding \texttt{condition} such as named-entity tag, semantic role label, or sentiment. Our experimental results on various domains show that GYC generates counterfactual text samples exhibiting the above four properties. GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.
Project-Based Learning in “Practical Development of Electronic Systems” Course, Weaknesses and Strengths in the Context Imposed by the COVID-19 Disease

We present the development planning of the subject “Practical Development of Electronic Systems” using the project-based learning strategy. The planning stages include: project specification; electronic design and schematic capture; the design and manufacture of printed circuit boards; the implementation, simulation and debugging of the firmware; the assembly, verification and characterization of the hardware; and finally the project documentation. All of this describe the main stages of engineering from the first sketch to the final and complete realization of a portable game console. The initial project seeks to make a video game console, compatible with games of a computer from the 80s: the Sinclair ZX Spectrum. Plan B involves the realization of a video game in native mode as a consequence of the difficulty of completing the initial objective in the duration of the subject. Students motivation, obtained with the project-based learning method and the project proposal, is very high. In addition, a SWOT analysis of the adaptation to synchronous telematic teaching is carried out, within the context given by the recent COVID-19 pandemic. As conclusions we want to say that, despite not having been able to carry out the physical and face-to-face development stages of the project included in the initial planning, motivation remained very high, without dropouts, and the project was ended by all.
Hold My Beer: A Case Study of how Ransomware Affected an Australian Beverage Company

This case study follows the cybersecurity attack that impacted Lion, a large Australian beverage company with a portfolio of brands in beer, milk, wine, and other products. The ransomware attack, which impacted Lion in different phases, took place during a time of strained resources and increased restrictions due to the COVID-19 pandemic. This paper provides an overview of the warnings, events, and incidents leading up to the cyberattacks, and describes the impact to the company's operations. This case shows the parallel and connected challenges that the company faced in dealing with the ransomware and the pandemic's impact. While the cyber incidents had a significant impact on Lion's operations, the organization appeared to recover from it and resume operations. The case is suitable for students and practitioners interested in cybersecurity or information systems topics and presents the compounded impacts of cyberattacks and the COVID-19 pandemic on firms.