Reviewerly/exHarmony huggingface dataset:
Photovoltaic Generation Forecast: Model Training and Adversarial Attack Aspects

Forecasting photovoltaic (PV) power generation, as in many other time series scenarios, is a challenging task. Most current solutions for time series forecasting are grounded on Machine Learning (ML) algorithms, which usually outperform statistical-based methods. However, solutions based on ML and, more recently, Deep Learning (DL) have been found vulnerable to adversarial attacks throughout their execution. With this in mind, in this work we explore four time series analysis techniques, namely Naive, a baseline technique for time series, Auto-regressive Integrated Moving Average (ARIMA), from the statistical field, and Long Short-term Memory (LSTM) and Temporal Convolutional Network (TCN), from the DL family. These techniques are used to forecast the power generation of a PV power plant 15 minutes and 24 hours ahead, having as input only power generation historical data. Two main aspects were analyzed: i) how training size influenced the performance of the forecasting models and ii) how univariate time series data could be modified by an adversarial attack to decrease models' performance through cross-technique transferability. For i), the mentioned methods were used and evaluated with monthly updates. For ii), Fast Gradient Sign Method (FGSM), along with a logistic regression substitute model and past data, were used to perform attacks against DL models at test time. LSTM and TCN decreased the error as the training sample size increased and outperformed Naive and ARIMA models. Adversarial samples were able to reduce the performance of LSTM and TCN, particularly for short-term forecasts.
An Integrated Approach of Deep Learning and Symbolic Analysis for Digital PDF Table Extraction

Deep learning has shown great success at interpreting unstructured data such as object recognition in images. Symbolic/logical-reasoning techniques have shown great success in interpreting structured data such as table extraction in webpages, custom text files, spreadsheets. The tables in PDF documents are often generated from such structured sources (text-based Word/LATEX documents, spreadsheets, webpages) but end up being unstructured. We thus explore novel combinations of deep learning and symbolic reasoning techniques to build an effective solution for PDF table extraction. We evaluate effectiveness without granting partial credit for matching part of a table (which may cause silent errors in downstream data processing). Our method achieves a 0.725 F1 score (vs. 0.339 for the state-of-the-art) on detecting correct table bounds-a much stricter metric than the common one of detecting characters within tables-in a well known public benchmark (ICDAR 2013) and a 0.404 F1 score (vs. 0.144 for the state-of-the-art) on our private benchmark with more widely varied table structures.
Parallel Mining of Frequent Subtree Patterns

Mining frequent subtree patterns in a tree database (or, forest) is useful in domains such as bioinformatics and mining semi-structured data. We consider the problem of mining embedded subtrees in a database of rooted, labeled, and ordered trees. We compare two existing serial mining algorithms, PrefixTreeSpan and TreeMiner, and adapt them for parallel execution using PrefixFPM, our general-purpose framework for frequent pattern mining that is designed to effectively utilize the CPU cores in a multicore machine. Our experiments show that TreeMiner is faster than its successor PrefixTreeSpan when a limited number of CPU cores are used, as the total mining workloads is smaller; however, PrefixTreeSpan has a much higher speedup ratio and can beat TreeMiner when given enough CPU cores.
The public security test of Brazilian e-Voting system

The article is an analysis of The Public Security Tests of Brazilian Electronic Voting System (TPS) in 2017 or TPS 2017. The Brazilian Superior Electoral Court (SEC), the national electoral authority, began to organize hacking challenges in 2009. These are a restricted event, including in research objects, where pre-approved external and independent researchers can examine the security mechanisms implemented within the system, find vulnerabilities, and provide suggestions for improvement. In 20 years of the Brazilian e-voting system, only four official securities tests, and all of them presented some failure or vulnerability. On this, three important scenarios in this research: a) the impact of the restrictions imposed on TPS 2017 in an election; b) the environment (laboratory) of TPS in the SEC; c) human fact in the security chain of the e-voting system. The originality of the work is related to the electoral observation of the three scenarios held at the SEC headquarters.
FiAAR: An Augmented Reality Firetruck Equipment Assembly and Configuration Assistant Technology

Augmented reality (AR) is the technology that expands the physical world by enhancing the objects that reside in the real world with computer generated perceptual information to provide interactive experience in a real-world environment. AR is used effectively in many business sectors such as the engineering, education and training, medicine, logistics and transport, and others. Rescue services is one of the challenging areas where the use of AR technology has extremely high demands for robustness and ease of use. In this paper, we have introduced two augmented reality versions for the FiAAR project developed by Karlsruhe University of Applied Sciences. The first version is developed for Realmax HMT-1 device with hands free interaction utilizing speech recognition. The second version in turn focuses on hand recognition and it's developed for Realwear Qian device with Leap Motion sensor. Our intention is to show the potential of current AR technologies in demanding use cases.
Restoring Latent Vectors From Generative Adversarial Networks Using Genetic Algorithms

Rapid and massive advances in deep learning have made it possible to address with issues with computer vision. In recent years, one type of generative model has emerged, generative adversarial networks (GAN), that enables creating realistic and plausible images. GAN allows for building competition models based on game theory that allows for modeling data probability distributions. Since the introduction of GAN, researchers have conducted many follow-up studies to apply and improve these models. In this article, using a global optimization technique called a genetic algorithm, we suggest the methodology restoring latent vector of pre-trained GAN and measure its performance as hyper-parameters and fitness functions; specifically, we utilize the mating and mutation rate as hyper-parameters of the genetic algorithms and use the mean squared error and the structural similarity as the fitness functions and evaluate their impact. We obtain image reconstruction results through experiment using the MNIST, Fashion-MNIST, Cifar10, and CelebA dataset, and we compare our method with the gradient descent method. We discuss limitations of these experiments and future research topics.
Online Neuro-Fuzzy Controller: Design for Robust Stability

The Online Neuro-Fuzzy Controller (ONFC) is a fuzzy-based adaptive control that uses a very simple structure and can control nonlinear, time-varying and uncertain systems. Its efficiency and low computational cost allowed applications in several industrial plants successfully. However, none of the previous works on the ONFC provided a design procedure endowed with formal guarantees of robust closed-loop stability. In this paper, some conditions for ONFC robust stability, considering system polytopic uncertainties, are presented using the Lyapunov method. A new adaptation rule is proposed that dynamically varies the adaptation gain and incorporates the dead-zone technique to ensure robustness to the noise measurement. A reference model is also introduced, in order to allow a direct specification of the closed-loop dynamics. Simulation results show that the new design conditions present good performance in the control of several types of systems.
Hand Tracking Based on the Kinect and Kalman Filter

Gesture Recognition (GR) is one of the hot issues in the field of computer vision. Gestures are generally divided into static and dynamic gestures. The recognition of static gestures mainly lies in the extraction and matching of hand shape features, whereas the key feature of dynamic gestures is hand trajectory. Therefore, it is necessary to track the hand to obtain the temporal and spatial information. In this paper, we use the Microsoft Kinect, with the function of recognizing and positioning human joints, to detect the hand position from the captured body image sequence. However, in practice, hand detection results of the Kinect are sometimes biased, leading to a significant impact on the feature description of gesture trajectory. Therefore, the Kalman filter is introduced to correct the hand position returned from the Kinect. The experimental results show that the hand trajectory curves obtained by the proposed tracking algorithm are more smooth and stable.
Range Query in Blockchain-based Data Sharing Model for Electronic Medical Records

Abstract The data sharing of electronic medical records (EMRs) has great positive significance for research on disease and epidemic prevention. Recently, blockchain-based EMRs storing and sharing schemes have brought many benefits. Nevertheless, the existing medical data sharing solutions cannot fully meet the query requirements in multi-user scenarios where data users with different identities have different needs for data usage. In this paper, we propose a block construction method for storing EMRs. We combine the characteristics of the Merkle tree and prefix dictionary tree in the block to reduce the time consumption of queries in the block. Besides, the block header is identified by four attributes, namely hospital ID, Department ID, disease type, and time period. According to the query conditions, the block where the requested data is located is quickly found. In the blockchain-based data sharing model of EMRs, data requesters with different identities can make combined queries with multiple conditions according to their own needs, and then the block and transactions in the block are located accurately. In conclusion, we are the first to build the block based on disease types and our model supports more flexible combined queries with multiple conditions.
Modeling city land use with an ontology

Urban planning developers, city government and citizens need a digital city model for sustainable development and city planning. Such models are based on main regulatory documents that differ by the city. In the current project, we aimed to develop a Land Use Ontology (LUO) for St. Petersburg as well as to expand general approaches to create such ontologies. The project is at its initial stage, i.e. development of the Land Use and Development Rules model that is a crucial document for the city land use system. We propose the ontological model of the city land use. The model includes classes for city land sites description, their coordinates, and permissions for construction works. The model is implemented in Web Ontology Language that assures the five-star rating of open data. The model source code can be found at https://github.com/inxaoc/cityont-public.
Completing the determination of the next-to-minimal weights of affine cartesian codes

In a previous paper (see [3]) we determined the values of the next-to-minimal Hamming weights of affine cartesian codes of order d, for almost all values of d. In the present paper we determine the next-to-minimal weights for the missing values of d, assuming that the sets in the cartesian product which appears in the definition of the code are subfields of Fq.
Augmenting Images for ASR and TTS Through Single-Loop and Dual-Loop Multimodal Chain Framework

Previous research has proposed a machine speech chain to enable automatic speech recognition (ASR) and text-to-speech synthesis (TTS) to assist each other in semi-supervised learning and to avoid the need for a large amount of paired speech and text data. However, that framework still requires a large amount of unpaired (speech or text) data. A prototype multimodal machine chain was then explored to further reduce the need for a large amount of unpaired data, which could improve ASR or TTS even when no more speech or text data were available. Unfortunately, this framework relied on the image retrieval (IR) model, and thus it was limited to handling only those images that were already known during training. Furthermore, the performance of this framework was only investigated with single-speaker artificial speech data. In this study, we revamp the multimodal machine chain framework with image generation (IG) and investigate the possibility of augmenting image data for ASR and TTS using single-loop and dual-loop architectures on multispeaker natural speech data. Experimental results revealed that both single-loop and dual-loop multimodal chain frameworks enabled ASR and TTS to improve their performance using an image-only dataset.
Cartesian Genetic Programming for Synthesis of Optimal Control System

The problem of general synthesis of optimal control system of mobile robot is considered. In the problem it is necessary to find a feedback control function such that a mobile robot can achieve the set terminal position from any point of area of initial conditions. The solution of this problem is a mathematical expression of control function. For solution of this problem Cartesian genetic programming (CGP) is used. CGP is one of symbolic regression methods. The methods of symbolic regression allow numerical with the help of computer to find analytical form of mathematical expression. CGP codes multi-dimension function in a form of integer matrix on the base of the sets of arguments and elementary functions. Every string of this matrix is a code of one call of a function. For search of optimal solution, a variation genetic algorithm is used that realizes the principle of small variation of basic solution. The algorithm searches for a mathematical expression of the feedback control function in the form of code and at the same time value of parametric vector that is one of arguments of this function. An example of numerical solution of the control system synthesis problem for mobile robot is presented. It is introduced a conception a space of machine made functions. In this space functions can’t have value is infinity, and all function can be presented in the form of Taylor’s series only with a finite number of members.
Towards the Monitoring and Evaluation of Trainees’ Activities in Cyber Ranges

Cyber ranges are virtual environments used in several contexts to enhance the awareness and preparedness of users to cybersecurity threats. Effectiveness of cyber ranges strongly depends on how much realistic are the training scenarios provided to trainees and on an efficient mechanism to monitor and evaluate trainees’ activities. In the context of the emulation environment of the THREAT-ARREST cyber range platform, in this paper we present a preliminary design of our work in progress towards the definition of a model-driven approach to monitor and evaluate the trainee performance. We enhance the platform emulation environment with an agent-based system that checks trainees’ behavior in order to collect all the trainee’s actions performed while executing a training exercise. Furthermore, we propose a modular taxonomy of the actions that can be exploited for the description of the trainee’s expected behavior in terms of the expected trace, i.e., the sequence of actions that is required for the correct execution of an exercise. We model the expected and actual trainee activities in terms of finite state machines, then we apply an existing algorithm for graph matching to score the trainee performance in terms of graph distance.
mr$$^2$$NST: Multi-resolution and Multi-reference Neural Style Transfer for Mammography

Computer-aided diagnosis with deep learning techniques has been shown to be helpful for the diagnosis of the mammography in many clinical studies. However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model. In this study, we explicitly address style variety issue with the proposed multi-resolution and multi-reference neural style transfer (mr\(^2\)NST) network. The mr\(^2\)NST can normalize the styles from different vendors to the same style baseline with very high resolution. We illustrate that the image quality of the transferred images is comparable to the quality of original images of the target domain (vendor) in terms of NIMA scores. Meanwhile, the mr\(^2\)NST results are also shown to be helpful for the lesion detection in mammograms.
Research on Superconducting Qubit Manipulation Based on Arbitrary Waveform Generator

Abstract Quantum computing is an emerging technology which will influence the future. Quantum computing is currently limited by the scalability of qubits, especially the qubits manipulation of superconducting computing. In this paper, a superconducting qubits manipulation method based on AWG (Arbitrary Waveform Generator) is proposed. Based on the superconducting quantum computing system, the overall scheme of qubits manipulation is presented, including overall framework, multichannel coherent arbitrary waveform generation design, quantum state reading and acquisition design, multichannel synchronization design and quick feedback transceiver design. The simulation and experiment results show that the proposed method has good scalability, high synchronization index and good signal quality, which can meet the requirements of superconducting quantum computing for qubits manipulation.
Review of Web Authentication

Abstract In the server situation of web security, a large number of Web sites and users are under the threat of network attacks. Fortunately, a complete authentication system can effectively protect them from some attacks. With the help of the authentication system, web sites can identify their users correctly and then decide whether to provide the corresponding services. The web authentication system is mainly constructed by the credential and the authentication strategy, the credential symbolizes identity and the authentication strategy judges the authenticity of identity. This paper will analyse and discuss some related literature and research findings of credentials and authentication strategies in the world, grasp overall direction, and look forward to the development of web authentication.
Lattice CP-ABE Scheme Supporting Reduced-OBDD Structure

Ciphertext attribute-based encryption (CP-ABE) schemes from lattices are considered as a flexible way of ensuring access control, which allow publishers to encrypt data for many users without worrying about quantum attacks and exponential computational inefficiencies. However, most of the proposed schemes are inefficient, inflexible, and cannot support flexible expression of access policy. To achieve an efficient and flexible access policy expression, we construct CP-ABE scheme from lattice which supports reduced ordered binary decision diagram (reduced-OBDD) structure. This approach is entirely different but can achieve an efficient and flexible access policy. Encryption and decryption are based on walking on the links between the nodes instead of using the nodes. By adopting the strategy of using Reduced-OBDD and walking on its links, we can obtain an optimized access structure for our policy which do not only support AND, OR, and Threshold operations but also support negative and positive attributes. We finally prove our scheme to be secured under decision R-Learning with error problem in a selective set model.
Progress Toward an All-Microwave Quantum Illumination Radar

Quantum illumination has been suggested as a way to improve the sensitivity of remote target detection by taking advantage of quantum entanglement between a traveling electromagnetic beam of light interrogating the target, and a second beam kept in the lab. In the microwave domain, superconducting quantum circuit utilizing superconducting Josephson tunnel junctions can be used as quantum-limited amplifiers of faint microwave signals, as well as bright sources of quantum entangled signals. These Josephson parametric amplifiers (JPAs) are poised to become fundamental building block in a future quantum illumination radar operating in the microwave domain. Demonstrating a practical advantage of quantum illumination with ambient temperature signals is, however, challenging. Indeed, current quantum protocols are not practical as they require a complex receiver with stringent phase-matching conditions, which cannot be met in the field. Notably, it is also not currently known how one can transfer faint microwave signals out of cryogenic systems without destroying their fragile entanglement. In this article, we discuss the recent progress and challenges regarding the development of an all-microwave quantum illumination radar using the recently developed and implemented quantum-enhanced noise radar protocol. This quantum illumination protocol is simpler to implement and procures a quantum enhancement over its classical analog. Finally, we discuss different strategies that may resolve the issue of transmitting microwave entanglement under ambient conditions. We, thus, conclude that successful transmission of entangled microwaves can be done in the near future, with possible demonstrations of practical quantum advantage over short distances.
Enhanced Visualization of Method Invocations by Extending Reverse-engineered Sequence Diagrams

Software} maintainers employ reverse-engineered sequence diagrams to visually understand software behavior, especially when software documentation is absent or outdated. Much research has studied the adoption of reverse-engineered sequence diagrams to visualize program interactions. However, due to the forward-engineering nature of sequence diagrams, visualizing more complex programming scenarios can be challenging. In particular, sequence diagrams represent method invocations as unidirectional arrows. However, in practice, source code may contain compound method invocations that share values/objects implicitly. For example, method invocations can be nested, e.g., fun (foo ()), or chained, e.g., fun (). foo (). The standard notation of sequence diagrams does not have enough expressive power to precisely represent compound scenarios of method invocations. Understanding the flow of information between method invocations simplifies debugging, inspection, and exception handling operations for software maintainers. Despite the research invested to address the limitations of UML sequence diagrams, previous approaches fail to visualize compound scenarios of method invocations. In this paper, we propose sequence diagram extensions to enhance the visualization of (i) three widely used types of compound method invocations in practice (i.e., nested, chained, and recursive) and (ii) lifelines of objects returned from method invocations. We aim through our extensions to increase the level of abstraction and expressiveness of method invocation code. We develop a tool to reverse engineer compound method invocations and generate the corresponding extended sequence diagrams. We evaluate how our proposed extensions can improve the understandability of program interactions using a controlled experiment. We find that program interactions are significantly more comprehensible when visualized using our extensions.