Reviewerly/exHarmony huggingface dataset:
Towards Defeating Backdoored Random Oracles: Indifferentiability with Bounded Adaptivity

In the backdoored random-oracle (BRO) model, besides access to a random function $$\mathsf {H}$$ , adversaries are provided with a backdoor oracle that can compute arbitrary leakage functions f of the function table of $$\mathsf {H}$$ . Thus, an adversary would be able to invert points, find collisions, test for membership in certain sets, and more. This model was introduced in the work of Bauer, Farshim, and Mazaheri (Crypto 2018) and extends the auxiliary-input idealized models of Unruh (Crypto 2007), Dodis, Guo, and Katz (Eurocrypt 2017), Coretti et al. (Eurocrypt 2018), and Coretti, Dodis, and Guo (Crypto 2018). It was shown that certain security properties, such as one-wayness, pseudorandomness, and collision resistance can be re-established by combining two independent BROs, even if the adversary has access to both backdoor oracles. In this work we further develop the technique of combining two or more independent BROs to render their backdoors useless in a more general sense. More precisely, we study the question of building an indifferentiable and backdoor-free random function by combining multiple BROs. Achieving full indifferentiability in this model seems very challenging at the moment. We however make progress by showing that the xor combiner goes well beyond security against preprocessing attacks and offers indifferentiability as long as the adaptivity of queries to different backdoor oracles remains logarithmic in the input size of the BROs. We even show that an extractor-based combiner of three BROs can achieve indifferentiability with respect to a linear adaptivity of backdoor queries. Furthermore, a natural restriction of our definition gives rise to a notion of indifferentiability with auxiliary input, for which we give two positive feasibility results. To prove these results we build on and refine techniques by Göös et al. (STOC 2015) and Kothari et al. (STOC 2017) for decomposing distributions with high entropy into distributions with more structure and show how they can be applied in the more involved adaptive settings.
InnoChain: a Distributed Ledger for Industry with Formal Verification on all Implementation Levels

The extent of formal verification methods applied to industrial projects has always been limited. The proliferation of distributed ledger systems (DLS), also known as blockchain, is rapidly changing the situation. Since the main area of DLSs' application is the automation of financial transactions, the properties of predictability and reliability are critical for implementing such systems. The actual behavior of the DLS is determined by the chosen consensus protocol, which properties require strict specification and formal verification. Formal specification and verification of the consensus protocol is necessary but not sufficient. It is required to ensure that the software implementation of the DLS nodes complies with this protocol. The verified software implementation of the protocol must run on a fairly reliable operating system. The so-called “smart contracts”, which are an important part of the applied implementations of specific business processes based on DLSs, must be verifiable as well. In this paper, we describe an ongoing industrial project that will result in a DLS verified at least at the four technological levels described above. We then share our experience with the formal specification and verification of HotStuff, a leader-based fault-tolerant protocol that ensures reaching distributed consensus in the presence of Byzantine processes.
Engineering Education Based on Integration with Science and Industry

The development of globalization processes requires the provision of scientific and technological leadership of economies. In solving this goal, universities are called upon to play a special role, which can ensure the competitiveness of engineering education. Among the main indicators of academic leadership are ensuring the world level of training content, increasing academic mobility, growing demand and efficiency of master's and postgraduate studies, increase in the number of online educational programs, increase in the number of competitive courses and programs taught online, development of a targeted training system. The key condition for increasing the competitiveness of engineering education is the strengthening of its integration with science and the industrial sector. This is due to the possibility of solving a set of tasks for the development of content, organization and management of the educational process. The effectiveness of their solution is determined by the composition of partners involved in joint activities. The analysis shows that there are trends in the expansion of partnerships and the intensification of international cooperation. This facilitates the development of various formats of interaction to improve the training of engineers. Among the most developing integrated forms of training are networked educational programs. A distinctive feature of their implementation is the growth in the number of participants in network partnerships, among them are universities (as a rule more than one), industry institutes, enterprises (more than one). The network format is being implemented and remains promising in the organization of student practices. The basic departments created at enterprises are the main instrument of targeted training. With the development of the system of professional standards, the role of integration of universities with enterprises in organizing and conducting procedures for assessing the qualifications of personnel has increased. The need to solve the problem of increasing the global competitiveness of education reinforces the requirement to develop the integration of universities with academic science in the form of scientific and educational centers, with foreign universities, enterprises and high-tech businesses.
Virtual Reality Application for Fostering Interest in Art

There is concern within the cultural community about the decreasing number of people, especially young people, visiting museums and cultural institutions. To fight this trend, new ways of accessing art are needed. We believe that technologies like virtual reality can greatly foster the relationship between users and works of art. In this article, we present an elaborated virtual reality experience, ImmersArt, whose aim is to take users literally inside a work of art and the creative universe of its creator. The motivational impact of this tool was empirically analyzed and compared with other formats for experiencing art such as illustrated books and audiovisual presentations. The study suggests that young people are more motivated to study artwork when using virtual reality.
Smart Home Automation System Based on Eye Tracking for Quadriplegic Users

Disabled people are unable to move independently or without assistive equipment. Patients with quadriplegia do not have the ability of controlling all of their muscles except the head and eyes. This paper presents a development of a smart home automation system that is designed for disabled people especially for people suffering from quadriplegia with partial or complete paralysis. One interesting feature of the developed system is that, the home appliances can be controlled by tracking the eyes of user. The appliances are controlled by computer application program which is developed to allow disabled user to control them by eye movement and blinking command captured and detected by a webcam. A unique smart home system is designed using python programming language and OpenCV libraries and algorithms based on detecting and recognition of eye movements. A simple user interface has been designed to allow people with disability to control appliances of the home through their eyes. Moreover, with this available features incorporated in a single system in addition to good and simple user interface, this system is cost effective and perfect for disabled people and old age people.
Prevalência de sintomas psicológicos em tempos de isolamento social / Prevalence of psychological symptoms in times of social isolation

Depression has become an important public health problem, especially due to its prevalence and morbimortality.It affects people of all ages, mainly elderly due to biological and psychosocial factors.Besides, it's characterized by: presence of humor predominantly sad, empty or irritable, associated with somatic and cognitive alterations that changes significantly the functional capacitive of the individue.In december of 2019, the new coronavirus (COVID-19), responsible for a new primary atypical pneumonia and highly contagious, affected several countries and continents.Thereby, a pandemic was declared, resulting in social isolation from the population.As a result, people have been subjected to social isolation and distance and have consequently undergone significant changes in their lifestyles, which may culminate in an increase in depressive disorders in the short and long term, in populations of all ages and worldwide.Thus, such a study is essential for a better understanding of the changes caused in the mental health of the population in general, and the best way to approach them for clinical management and choosing the best treatment during and after social isolation
A Fast and Efficient FPGA-based Level Set Hardware Accelerator for Image Segmentation

Level set algorithm has been widely used for image segmentation due to its high accuracy. In addition, compared to the deep learning-based image segmentation methods, the level set algorithm can be implemented without training data, which significantly reduce the labelling efforts. However, the normal level set algorithm is still developed using software, involving complex computation with a large number of pixels and iterations. Also, the software implementation induces long processing time and large power consumption. In this work, we propose a FPGA-based level set hardware accelerator for image segmentation. The proposed hardware accelerators contain four design components: task-level parallel processing, image splitting processing, fully-pipelined processing architecture, and time multiplexed gradient and divergence processing engine. Based on the experimental results, our proposed hardware accelerator achieves up to 10.7× acceleration compared to the level set algorithm executing on CPU, with only 2.2 W power consumption.
Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities

The wide proliferation of sensors and devices of Internet of Things(IoT), together with Artificial Intelligence (AI), has created the so-called Smart Environments. From a network perspective, these solutions suffer from high latency and increased data transmission. This paper proposes a Federated Learning (FL) architecture for Real-Time Traffic Estimation, supported by Roadside Units (RSU’s) for model aggregation. The solution envisages that learning will be done on clients with their local data, and fully distributed on the Edge, with high learning rates, low latency, and less bandwidth usage. To achieve that,this paper discusses tools and requirements for FL implementation towards a model for real-time traffic estimation, as well as how such solution could be evaluated using VANET and network simulators. As a first practical step, we show a preliminary evaluation of a learning model using a data set of cars that demonstrate a distributed learning strategy. In the future, we will use a similar distributed strategy within our proposed architecture.
Distributed Saddle-Point Problems: Lower Bounds, Optimal Algorithms and Federated GANs

GAN is one of the most popular and commonly used neural network models. When the model is large and there is a lot of data, the learning process can be delayed. The standard way out is to use multiple devices. Therefore, the methods of distributed and federated training for GANs are an important question. But from an optimization point of view, GANs are saddle-point problems: $\min_x \max_y f(x,y)$. Therefore, this paper focuses on the distributed optimization of smooth stochastic saddle-point problems. The first part of the paper is devoted to lower bounds for the distributed methods for saddle-point problems as well as the optimal algorithms by which these bounds are achieved. Next, we present a new algorithm for distributed saddle-point problems - Extra Step Local SGD. In the experimental part of the paper, we use the Local SGD technique in practice. In particular, we train GANs in a distributed manner.
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers

Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programming (LP) on linearly relaxed sub-domains. In this paper, we propose to use the backward mode linear relaxation based perturbation analysis (LiRPA) to replace LP during the BaB process, which can be efficiently implemented on the typical machine learning accelerators such as GPUs and TPUs. However, unlike LP, LiRPA when applied naively can produce much weaker bounds and even cannot check certain conflicts of sub-domains during splitting, making the entire procedure incomplete after BaB. To address these challenges, we apply a fast gradient based bound tightening procedure combined with batch splits and the design of minimal usage of LP bound procedure, enabling us to effectively use LiRPA on the accelerator hardware for the challenging complete NN verification problem and significantly outperform LP-based approaches. On a single GPU, we demonstrate an order of magnitude speedup compared to existing LP-based approaches.
Statistical Analysis of the Dynamics of Performance Indicators of Russian Universities

The article presents the results of a study based on statistical methods for analyzing trends in the Russian higher education system over the ﬁve-year period preceding the global crisis provoked by the coronavirus pandemic COVID-19. This study is one of the ﬁrst attempts at a large-scale analysis of the growth rates of the performance indicators of universities in recent years. Its results, according to the author, make it possible to form both a general picture of changes in Russian higher education and a detailed picture for individual groups of universities. The author has analyzed the dynamics of six indicators of the activity of organizations of higher professional education (535 universities from 80 constituent entities of the Russian Federation): the passing score of the Uniﬁed State Examination (USE), the qualiﬁcations of academic staff, their publication activity, the internationalization of the student body, the proﬁtability of activities and the provision of educational and laboratory premises. Trends in the change in the values of these indicators are revealed depending on the form of ownership, specialization and status of the university. It was found that, in general, the average values of the indicators for ﬁve years increased: the passing score of the USE - by 5%; the share of the teaching staff with an academic degree in the total number of teaching staff - by 5%; the number of publications per one scientiﬁc and pedagogical worker (SPW) - 2.2 times; the share of foreign students in the total number of students - 1.5 times; proﬁtability of activities - 1.3 times; the total area of educational and laboratory premises - by 8%. At the same time, in non-state universities, the USE passing score has decreased, and they lag behind state universities in terms of growth in the publication activity of the teaching staff and the internationalization of the student body. However, their results are higher in terms of the growth of proﬁtability of activities and provision of educational and laboratory premises. Specialized universities have achieved an increase in the indicators of the publication activity of academic staff and the internationalization of the student body. Among the negative trends, a decrease in the passing score of the USE in medical universities, as well as in the equipment of teaching and laboratory facilities of medical and sports universities, was revealed. The highest growth in the publication activity of SPW was observed in national research universities (24.5%), and in the internationalization of the student body - in federal universities (33.3%). Changes in the operating conditions of universities during a pandemic are likely to have an ambiguous effect on the analyzed indicators in subsequent periods. For their adequate assessment, it may be necessary to adjust the methodology for calculating indicators of the activity of higher education institutions, taking into account their intensive use of distance learning technologies.
Strongly entangled system-reservoir dynamics with multiphoton pulses beyond the two-excitation limit: Exciting the atom-photon bound state

Within the matrix product state framework, we study the non-Markovian feedback dynamics of a two-level system interacting with the electromagnetic field inside a semi-infinite waveguide where the excitation of an atom-photon bound state is possible. Taking the steady-state excitation of the emitter as a figure of merit, we compare the trapped excitation for an initially excited quantum emitter and an emitter prepared via quantized pulses containing up to four photons. In the latter case, we find that for large feedback delay times, multi-photon pulses can yield a significantly higher steady-state excitation than possible with an initially excited emitter since the stimulated emission process can enhance the trapping probability in comparison to the spontaneous decay of an initially excited emitter.
On the Exact Analysis of an Idealized Quantum Switch

Protocols that exploit quantum communication technology offer two advantages: they can either extend or render feasible the capabilities of their classical counterparts, or they exhibit functionality entirely unachievable through classical means alone. For an example of the former, quantum key distribution protocols such as E91 [2] and BBM92 [1] can in principle yield information-theoretic security by using entanglement to generate secure key bits. These raw secret key bits can then be distilled into a one-time pad to encode messages sent between two parties. For an example of the latter, distributed quantum sensing frameworks such as [3] and [11] employ entanglement to overcome the standard quantum limit [4].
Forensic analysis of open-source XMPP/Jabber multi-client instant messaging apps on Android smartphones

Abstract In the quest for a panacea to ensure digital privacy, many users have switched to using decentralized open-source Extensible Messaging and Presence Protocol multi-client instant messaging (IM) apps for secure end-to-end communication. In this paper, we present a forensic analysis of the artefacts generated on Android smartphones by Conversations and Xabber apps. We identified databases maintained by each app and external Secure Digital card directories that store local copies of user metadata. We analysed each app’s storage locations for forensic artefacts and how they can be used in a forensic investigation. The results in this paper show a detailed analysis of forensic files of interest which can be correlated to identify the local user’s multiple IM accounts and contact list, contents of messages exchanged with contacts, deleted files, time, and dates in the order of their occurrence. The contributions of this research include a comprehensive description of artefacts, which are of forensic interest, for each app analysed.
A Novel Deep Learning Architecture for Image Hiding

Watermarking is a today's digital hiding technique within certain electronic content: for example, message, image, video, or audio recordings. Recent times, it was created as a modern copyright security tool. The pattern in zero watermarking technique isn't really inserted directly in the cover image, but has a logical relation with that cover image. In this article, we propose a powerful convolution neural Networks (CNN) and deep learning algorithm-based-watermarking technique in which the CNN produces robust inherent selected features and is merged with the XOR activity of host's watermark sequence. The outcomes of our proposed method present the courage of the watermark counter to many typical image processing techniques.
Parametricity for Primitive Nested Types

Abstract This paper considers parametricity and its resulting free theorems for nested data types. Rather than representing nested types via their Church encodings in a higher-kinded or dependently typed extension of System F, we adopt a functional programming perspective and design a Hindley-Milner-style calculus with primitives for constructing nested types directly as fixpoints. Our calculus can express all nested types appearing in the literature, including truly nested types. At the term level, it supports primitive pattern matching, map functions, and fold combinators for nested types. Our main contribution is the construction of a parametric model for our calculus. This is both delicate and challenging: to ensure the existence of semantic fixpoints interpreting nested types, and thus to establish a suitable Identity Extension Lemma for our calculus, our type system must explicitly track functoriality of types, and cocontinuity conditions on the functors interpreting them must be appropriately threaded throughout the model construction. We prove that our model satisfies an appropriate Abstraction Theorem and verifies all standard consequences of parametricity for primitive nested types.
A Scalable and Automated Machine Learning Framework to Support Risk Management

Due to the growth of data and widespread usage of Machine Learning (ML) by non-experts, automation and scalability are becoming key issues for ML. This paper presents an automated and scalable framework for ML that requires minimum human input. We designed the framework for the domain of telecommunications risk management. This domain often requires non-ML-experts to continuously update supervised learning models that are trained on huge amounts of data. Thus, the framework uses Automated Machine Learning (AutoML), to select and tune the ML models, and distributed ML, to deal with Big Data. The modules included in the framework are task detection (to detect classification or regression), data preprocessing, feature selection, model training, and deployment. In this paper, we focus the experiments on the model training module. We first analyze the capabilities of eight AutoML tools: Auto-Gluon, Auto-Keras, Auto-Sklearn, Auto-Weka, H2O AutoML, Rminer, TPOT, and TransmogrifAI. Then, to select the tool for model training, we performed a benchmark with the only two tools that address a distributed ML (H2O AutoML and TransmogrifAI). The experiments used three real-world datasets from the telecommunications domain (churn, event forecasting, and fraud detection), as provided by an analytics company. The experiments allowed us to measure the computational effort and predictive capability of the AutoML tools. Both tools obtained high-quality results and did not present substantial predictive differences. Nevertheless, H2O AutoML was selected by the analytics company for the model training module, since it was considered a more mature technology that presented a more interesting set of features (e.g., integration with more platforms). After choosing H2O AutoML for the ML training, we selected the technologies for the remaining components of the architecture (e.g., data preprocessing and web interface).