Reviewerly/exHarmony huggingface dataset:
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning

As a decentralized model training method, federated learning is designed to integrate the isolated data islands and protect data privacy. Recent studies, however, have demonstrated that the Generative Adversarial Network (GAN) based attacks can be used in federated learning to learn the distribution of the victim's private dataset and accordingly reconstruct human-distinguishable images. In this paper, we exploit defenses against GAN-based attacks in federated learning, and propose a framework, Anti-GAN, to prevent attackers from learning the real distribution of the victim's data. The core idea of Anti-GAN is to corrupt the visual features of the victim's private training images, such that the images restored by the attacker are indistinguishable to human eyes. Specifically, in Anti-GAN, the victim first projects the personal dataset onto a GAN's generator, then mixes the fake images generated by the generator with the real images to obtain the training dataset, which will be fed into the federated model for training. We redesign the structure of the victim's GAN to encourage it to learn the classification features (instead of the visual features) of the real images. We further introduce an unsupervised task to the GAN model for obfuscating the visual features of the generated images. The experiments demonstrate that Anti-GAN can effectively prevent the attacker from learning the distribution of the private images, meanwhile causing little harm to the accuracy of the federated model.
Deep 3D Portrait from a Single Image

In this paper, we present a learning-based approach for recovering the 3D geometry of human head from a single portrait image. Our method is learned in an unsupervised manner without any ground-truth 3D data. We represent the head geometry with a parametric 3D face model together with a depth map for other head regions including hair and ear. A two-step geometry learning scheme is proposed to learn 3D head reconstruction from in-the-wild face images, where we first learn face shape on single images using self-reconstruction and then learn hair and ear geometry using pairs of images in a stereo-matching fashion. The second step is based on the output of the first to not only improve the accuracy but also ensure the consistency of overall head geometry. We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images. We alter pose based on the recovered geometry and apply a refinement network trained with adversarial learning to ameliorate the reprojected images and translate them to the real image domain. Extensive evaluations and comparison with previous methods show that our new method can produce high-fidelity 3D head geometry and head pose manipulation results.
PROSPECTS FOR USING BLOCKCHAIN TECHNOLOGY IN ACCOUNTING

The purpose of the article is the theoretical basis and further prospects for the use of blockchain technology in modern accounting.The study examines the theoretical and technical aspects of blockchain functioning.The essence of the distributed registry technology and the security of transaction records are substantiated.The article highlights the basics of miners' work and the concept of remuneration in the form of cryptocurrency.As a rule, these are commissions from all participants of transactions recorded in the block, and remuneration from the network itself.The possibility of using blockchain in the modern accounting system is analyzed.The main advantages and disadvantages of using this technology in the process of accounting operations are considered.Among researchers of this theory there are both supporters and opponents.The emphasis is placed on saving information in the blockchain network due to the fact that information is not stored in one place and it cannot be changed.In the blockchain registers with data about the owners of assets cannot be forged.It is noted that the ambiguity of the domestic regulatory framework may be an obstacle to the implementation of the blockchain platform in the accounting sphere.There are often cases when explanations are published with different points of view on the same aspect of accounting.It is proposed to introduce new technologies gradually in the form of software for tax accounting and control systems.The new technology will effectively combat fraud and significantly expand the ability of regulatory authorities to track the organization's activities, make accounting transparent, and allow it to be conducted in real time.The development of information technologies will increasingly penetrate all areas of the economy.At the same time, even in the future, the blockchain will not replace traditional accounting, but only complement it.Thus, its influence will be most noticeable in the sphere of control over settlements between different contractors or with the state.The need for further research on the use of blockchain in the field of accounting was noted.
Improved Adversarial Training via Learned Optimizer

Adversarial attack has recently become a tremendous threat to deep learning models. To improve the robustness of machine learning models, adversarial training, formulated as a minimax optimization problem, has been recognized as one of the most effective defense mechanisms. However, the non-convex and non-concave property poses a great challenge to the minimax training. In this paper, we empirically demonstrate that the commonly used PGD attack may not be optimal for inner maximization, and improved inner optimizer can lead to a more robust model. Then we leverage a learning-to-learn (L2L) framework to train an optimizer with recurrent neural networks, providing update directions and steps adaptively for the inner problem. By co-training optimizer's parameters and model's weights, the proposed framework consistently improves the model robustness over PGD-based adversarial training and TRADES.
Pamatizglītības standarta raksturojums kontekstā ar 9. klašu skolēnu vērtību sistēmas un atbildības veidošanos

The new basic school curriculum provides an opportunity to promote the development of student value system and responsibility in the pedagogical process. The emphasized requirements for Grade 9 graduates are: self-regulation, positive attitude, tolerance, personal responsibility, civic engagement, patriotism, and understanding of the important values of the society, ensuring the transfer and application of knowledge to the existing reality. Summarizing the empirical research data, it can be concluded that the predominant values for Grade 9 pupils are family, friends, peers and concern about education. Some students find it difficult to name their own values and the values of Latvia. The students involved in the study can be described as responsible only in those areas that affect the respondents themselves on a short term basis. Research highlights the lack of responsibility particularly in such categories as students` ability to take responsibility for their country, their future and their education. This means that in the pedagogical process, during the implementation of the conceptual framework of the new basic education curriculum, it is necessary to promote students' understanding in order to develop a long-term perspective.
Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks

Effective defense of deep neural networks against adversarial attacks remains a challenging problem, especially under powerful white-box attacks. In this paper, we develop a new method called ensemble generative cleaning with feedback loops (EGC-FL) for effective defense of deep neural networks. The proposed EGC-FL method is based on two central ideas. First, we introduce a transformed deadzone layer into the defense network, which consists of an orthonormal transform and a deadzone-based activation function, to destroy the sophisticated noise pattern of adversarial attacks. Second, by constructing a generative cleaning network with a feedback loop, we are able to generate an ensemble of diverse estimations of the original clean image. We then learn a network to fuse this set of diverse estimations together to restore the original image. Our extensive experimental results demonstrate that our approach improves the state-of-art by large margins in both white-box and black-box attacks. It significantly improves the classification accuracy for white-box PGD attacks upon the second best method by more than 29% on the SVHN dataset and more than 39% on the challenging CIFAR-10 dataset.
Optimizing the Performance of Clouds Using Hash Codes in Apache Hadoop and Spark

Hash functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the hash function performs badly, the load in the reduced part will not be balanced and access times will spike. To investigate this problem further, we ran the Wordcount program with numerous different hash functions on Amazon AWS. In particular, we will leverage the Amazon Elastic MapReduce framework. The paper investigates the general purpose, cryptographic, checksum, and special hash functions. Through the analysis, we present the corresponding runtime results.
The Strengths and Pitfalls of Large-Scale Text Mining for Literary Studies

Nina Tahmasebi, Centre for Digital Humanities, Sprakbanken, University of GothenburgSimon Hengchen, COMHIS, Department of Digital Humanities, University of HelsinkiThe strengths and pitfalls of lar ...
Metric learning by Similarity Network for Deep Semi-Supervised Learning

Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed input sets. Although these methods may achieve positive results, they ignore the relationship information between data instances. To solve this problem, we propose a novel method named Metric Learning by Similarity Network (MLSN), which aims to learn a distance metric adaptively on different domains. By co-training with the classification network, similarity network can learn more information about pairwise relationships and performs better on some empirical tasks than state-of-art methods.
Cavity-assisted enhanced and dephasing-immune squeezing in the resonance fluorescence of a single quantum dot

We theoretically demonstrate the enhanced and dephasing immune squeezing in the resonance fluorescence of a single quantum dot (QD) confined to a pillar-microcavity and driven by a continuous wave laser. We employ a formalism based on Polaron master equation theory for incorporating the influence of exciton-phonon coupling quite accurately in the dot-cavity system. We show a significant enhancement of squeezing due to cavity coupling of the QD as compared to that of an ideal single two-level system in free space. Particularly, we show a four-fold enhancement in squeezing as compared to that of a single QD without cavity coupling. We further demonstrate the persistence of squeezing even when the pure dephasing becomes greater than the radiative decay rate. These novel features are attributed to the cavity-enhanced coherence causing partial reduction of the deteriorating effects of phonon-induced incoherent rates. We also show that the deteriorating effects of phonon-induced incoherent rates on squeezing can be partially circumvented by properly adjusting the detunings.
Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?

Intelligent video-surveillance (IVS) is currently an active research field in computer vision and machine learning and provides useful tools for surveillance operators and forensic video investigators. Person re-identification (PReID) is one of the most critical problems in IVS, and it consists of recognizing whether or not an individual has already been observed over a camera in a network. Solutions to PReID have myriad applications including retrieval of video-sequences showing an individual of interest or even pedestrian tracking over multiple camera views. Different techniques have been proposed to increase the performance of PReID in the literature, and more recently researchers utilized deep neural networks (DNNs) given their compelling performance on similar vision problems and fast execution at test time. Given the importance and wide range of applications of re-identification solutions, our objective herein is to discuss the work carried out in the area and come up with a survey of state-of-the-art DNN models being used for this task. We present descriptions of each model along with their evaluation on a set of benchmark datasets. Finally, we show a detailed comparison among these models, which are followed by some discussions on their limitations that can work as guidelines for future research.
The Implementation of Metagraph Agents Based on Functional Reactive Programming

The main ideas of the metagraph data model and the metagraph agent model are discussed. The metagraph approach for semantic complex event processing is presented. The metagraph agent implementation based on Functional Reactive Programming is proposed.
Towards a Proof of the Fourier--Entropy Conjecture?.

The total influence of a function is a central notion in analysis of Boolean functions, and characterizing functions that have small total influence is one of the most fundamental questions associated with it. The KKL theorem and the Friedgut junta theorem give a strong characterization of such functions whenever the bound on the total influence is $o(\log n)$. However, both results become useless when the total influence of the function is $\omega(\log n)$. The only case in which this logarithmic barrier has been broken for an interesting class of functions was proved by Bourgain and Kalai, who focused on functions that are symmetric under large enough subgroups of $S_n$. In this paper, we build and improve on the techniques of the Bourgain-Kalai paper and establish new concentration results on the Fourier spectrum of Boolean functions with small total influence. Our results include: 1. A quantitative improvement of the Bourgain--Kalai result regarding the total influence of functions that are transitively symmetric. 2. A slightly weaker version of the Fourier--Entropy Conjecture of Friedgut and Kalai. This weaker version implies in particular that the Fourier spectrum of a constant variance, Boolean function $f$ is concentrated on $2^{O(I[f]\log I[f])}$ characters, improving an earlier result of Friedgut. Removing the $\log I[f]$ factor would essentially resolve the Fourier--Entropy Conjecture, as well as settle a conjecture of Mansour regarding the Fourier spectrum of polynomial size DNF formulas. Our concentration result has new implications in learning theory: it implies that the class of functions whose total influence is at most $K$ is agnostically learnable in time $2^{O(K\log K)}$, using membership queries.
Analysis of the Influence of Deceptive Interference on Airborne Radar Sensor

The detection mechanism of airborne radar sensor is simulated dynamically and realistically by data simulation method, including the detection function, the track function and resource scheduling function. A functional level detection model is established, which can realize the detection process of airborne radar and obtain the position and velocity parameters of aerial targets in three work modes such as single target tracking(STT), tracking while searching(TWS) and tracking add searching(TAS). The typical target scene is set up, and the detection mechanism of airborne radar sensor is analyzed and verified through three simulations for different purposes. Meanwhile, the influence of deceptive jamming such as range dragging or speed dragging on airborne radar detection ability is tested and analyzed.
SLA-driven Load Scheduling in Multi-tier Cloud Computing: Financial Impact Considerations

A cloud service provider strives to effectively provide a high Quality of Service (QoS) to client jobs.Such jobs vary in computational and Service-Level-Agreement (SLA) obligations, as well as differ with respect to tolerating delays and SLA violations.The job scheduling plays a critical role in servicing cloud demands by allocating appropriate resources to execute client jobs.The response to such jobs is optimized by the cloud service provider on a multi-tier cloud computing environment.Typically, the complex and dynamic nature of multi-tier environments incurs difficulties in meeting such demands, because tiers are dependent on each others which in turn makes bottlenecks of a tier shift to escalate in subsequent tiers.However, the optimization process of existing approaches produces single-tier-driven schedules that do not employ the differential impact of SLA violations in executing client jobs.Furthermore, the impact of schedules optimized at the tier level on the performance of schedules formulated in subsequent tiers tends to be ignored, resulting in a less than optimal performance when measured at the multi-tier level.Thus, failing in committing job obligations incurs SLA penalties that often take the form of either financial compensations, or losing future interests and motivations of unsatisfied clients in the service provided.Therefore, tolerating the risk of such delays on the operational performance of a cloud service provider is vital to meet SLA expectations and mitigate their associated commercial penalties.Such situations demand the cloud service provider to employ scalable service mechanisms that efficiently manage the execution of resource loads in accordance to their financial influence on the system performance, so as to ensure system reliability and cost reduction.In this paper, a scheduling and allocation approach is proposed to formulate schedules that account for differential impacts of SLA violation penalties and, thus, produce schedules that are optimal in financial performance.A queue virtualization scheme is designed to facilitate the formulation of optimal schedules at the tier and multi-tier levels of the cloud environment.Because the scheduling problem is NPhard, a biologically inspired approach is proposed to mitigate the complexity of finding optimal schedules.The reported results in this paper demonstrate the efficacy of the proposed approach in formulating costoptimal schedules that reduce SLA penalties of jobs at various architectural granularities of the multi-tier cloud environment.
An adaptive structure on a new local branching algorithm using instantaneous dimensions and convergence speed: a case study for multi-commodity network design problems

This study presents an adaptive parameter control structure for the New Local Branching Algorithm (NLBA) to enhance the overall efficiency of the algorithm by implementing an exploitation and exploration approach within solution search subspaces. In other words, the optimization problem is divided into several sub-problems and the proposed adaptive parameter control structure introduces some deterministic rules to check the convergence speed and adapt the performance of the algorithm in each subspace. So, the proposed adaptive structure uses an exploration approach to avoid becoming trapped at local optimum that may not be the global optimum, as well as an exploitation approach to look near good solutions for even better ones at each iteration. To evaluate the performance of the proposed adaptive parameter control structure, 26 multi-commodity network design problems are tested. The experimental results of this adaptive structure for NLBA (ANLBA) confirm that its performance compared to the original NLBA, a hybrid structure that consists of the Self-Adaptive Harmony Search (SAHS) algorithm as a parameter tuning method for NLBA, and CPLEX is significantly improved.
Composition Proposal Generation for Manga Creation Support

In recent years, cognition and use of manga pervade, and people who use manga for various purposes such as entertainment, study, marketing are increasing more and more. However, when people who do not specialize in it create it for these purposes, they can write plots expressing what they want to convey but the technique of the composition which arranges elements in manga such as characters or balloons corresponding to the plot create obstacles to using its merits for comprehensibility based on high flexibility of its expression. Therefore, we consider that support of this composition technique is necessary for amateurs to use manga while taking advantage of its benefits. We propose a method of generating composition proposal to support manga creation by amateurs. For the method, we also define new manga metadata model which summarize and extend metadata models by earlier studies. It represents the compostion and the plot in manga. We apply a neural machine translation mechanism for learing the relation between the composition and the plot. It considers that the plot annotation is the source of the composition annotation that is the target, and learns from the annotation dataset based on the metadata model. We conducted experiments to evaluate how the composition proposal generated by our method helps amateur manga creation, and demonstrated that it is useful.
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent. The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner. Experimental results validate the white-box adversarial strength and black-box transferability of ACE. A range of examples demonstrates the perceptual quality of images that ACE produces. ACE makes an important contribution to recent work that moves beyond $L_p$ imperceptibility and focuses on unrestricted adversarial modifications that yield large perceptible perturbations, but remain non-suspicious, to the human eye. The future potential of filter-based adversaries is also explored in two directions: guiding ACE with common enhancement practices (e.g., Instagram filters) towards specific attractive image styles and adapting ACE to image semantics. Code is available at https://github.com/ZhengyuZhao/ACE.
Secure Single-Server Nearly-Identical Image Deduplication

Cloud computing is often utilized for file storage. Clients of cloud storage services want to ensure the privacy of their data, and both clients and servers want to use as little storage as possible. Cross-user deduplication is one method to reduce the amount of storage a server uses. Deduplication and privacy are naturally conflicting goals, especially for nearly-identical (``fuzzy'') deduplication, as some information about the data must be used to perform deduplication. Prior solutions thus utilize multiple servers, or only function for exact deduplication. In this paper, we present a single-server protocol for cross-user nearly-identical deduplication based on secure locality-sensitive hashing (SLSH). We formally define our ideal security, and rigorously prove our protocol secure against fully malicious, colluding adversaries with a proof by simulation. We show experimentally that the individual parts of the protocol are computationally feasible, and further discuss practical issues of security and efficiency.