Reviewerly/exHarmony huggingface dataset:
Hybrid Decoding – Classical-Quantum Trade-Offs for Information Set Decoding

The security of code-based constructions is usually assessed by Information Set Decoding (ISD) algorithms. In the quantum setting, amplitude amplification yields an asymptotic square root gain over the classical analogue. However, already the most basic ISD algorithm by Prange suffers enormous width requirements caused by the quadratic description length of the underlying problem. Even if polynomial, this need for qubits is one of the biggest challenges considering the application of real quantum circuits in the near- to mid-term. In this work we overcome this issue by presenting the first hybrid ISD algorithms that allow to tailor the required qubits to any available amount while still providing quantum speedups of the form $$T^\delta $$ , $$0.5<\delta <1$$ , where T is the running time of the purely classical procedure. Interestingly, when constraining the width of the circuit instead of its depth we are able to overcome previous optimality results on constraint quantum search. Further we give an implementation of the fully-fledged quantum ISD procedure and the classical co-processor using the quantum simulation library Qibo and SageMath.
Sponge-Based Authenticated Encryption: Security Against Quantum Attackers

In this work, we study the security of sponge-based authenticated encryption schemes against quantum attackers. In particular, we analyse the sponge-based authenticated encryption scheme $${ \textsc {Slae}}$$ as put forward by Degabriele et al. (ASIACRYPT’19) due to its modularity. We show that the scheme achieves security in the post-quantum (QS1) setting in the quantum random oracle model by using the one-way to hiding lemma. Furthermore, we analyse the scheme in a fully-quantum (QS2) setting. There we provide a set of attacks showing that $${ \textsc {Slae}}$$ does not achieve ciphertext indistinguishability and hence overall does not provide the desired level of security.
The Robot Operating System (ROS1 &amp;2): Programming Paradigms and Deployment

The amount of knowledge needed to deploy a robotic system can sometimes feel overwhelming. However, many individual problems were solved already, including software ecosystems to simulate and then deploy our robots seamlessly. The Robot Operating System (ROS) is an open-source solution addressing this critical sharing need for robotic sensing, control, planning, simulation, and deployment. Not to be confused with a library, it is a software ecosystem (the concept of an operating system might be too strong) facilitating the integration, maintenance, and deployment of new functionalities and hardware from simulations to physical deployment.
Autoencoder: An Unsupervised Deep Learning Approach

With the advent of science and technology, it has been observed that autoencoder plays a vital role in unsupervised learning and in deep architectures for many tasks like transfer learning and other tasks. Learn efficient data coding in an unsupervised manner, a variety of artificial neural network is used, which is known as autoencoder. Here we present an entire overview of an autoencoder and learning about what actually autoencoder is and how are they used. Here we will learn about the architecture of autoencoder, their implementation, types of autoencoder like denoising autoencoder, sparse autoencoder, the use cases, and a lot more things.
<scp>Blockchain‐based</scp> security enhancement mechanism for employee performance assessment system

Summary In the proposed approach, the blockchain based employee performance assessment system is introduced to enhance the security and privacy of employee data. The proposed three phases are used to enhance the security mechanism in the employee performance assessment system: authentication, encryption, and data retrieval phase. The authentication process is performed in the first phase by utilizing Lamport Merkle digital signature (LMDS) algorithm. The LMDS algorithm generates and verifies a digital ID for each employee. In the next phase blockchain based encryption is carried on employee data using the new light weight cryptography algorithm (NLCA), which uses a 16 bytes (128 bit) block cipher and needs 16 bytes (128‐bit) key to encrypt the data. At last, the data retrieval phase allows users to access the data through the secret key. Proposing a modified Diffie‐Hellman (ReDH) algorithm to exchange the secret key to ensure security. The proposed approach is implemented in the Python platform, and the performance of the proposed approach is compared with existing methods in terms of encryption time, throughput, decryption time, storage efficiency, and search time. Using a blockchain‐based employee performance assessment system enhances system trust level, transparency, accountability, and security efficiency.
A Hybrid Model for Commercial Brand Marketing Prediction Based on Multiple Features with Image Processing

Recently, deep learning has been employed in automatic feature extraction and has made remarkable achievements in the fields of computer vision, speech recognition, natural language processing, and artificial intelligence. Compared with the traditional shallow model, deep learning can automatically extract more complex features from simple features, which reduces the intervention of artificial feature engineering to a certain extent. With the development of the Internet and e-commerce, picture advertising, as an important form of display advertising, has the characteristics of high visibility, strong readability, and easy-to-obtain user recognition. An increasing number of Internet companies are paying attention to what kind of advertising pictures can attract more clicks. Based on deep learning technology, this paper studies the prediction model of click-through rate (CTR) for advertising and proposes an end-to-end CTR prediction depth model for display advertising, which integrates the feature extraction of display advertising and CTR prediction to directly predict the probability of an advertisement image being clicked by users. This paper studies the deep-seated nonlinear characteristics through the multilayer network structure of the deep network and carries out several groups of experiments on the private display advertising data set of a commercial advertising platform. The results show that the model proposed in this paper can effectively improve the prediction accuracy of CTR compared with other benchmark models and predict whether an advertisement is clicked or not by given advertisement information and user information. By establishing a reasonable advertising click-through rate prediction model, it can help the platform estimate future revenue so as to make cooperative decisions with advertisers. For advertisers, it is necessary to evaluate the price by predicting the click-through rate and estimate the bidding price of their own advertisements.
Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.
The Mathematical Model of Cyclic Signals in Dynamic Systems as a Cyclically Correlated Random Process

This work is devoted to the procedure for constructing of a cyclically correlated random process of a continuous argument as a mathematical model of cyclic signals in dynamic systems, which makes it possible to consistently describe cyclic stochastic signals, both with regular and irregular rhythms, not separating them, but complementing them within the framework of a single integrated model. The class of cyclically correlated random processes includes the subclass of cyclostationary (periodically) correlated random processes, which enable the use of a set of powerful methods of analysis and the forecasting of cyclic signals with a stable rhythm. Mathematical structures that model the cyclic, phase and rhythmic structures of a cyclically correlated random process are presented. The sufficient and necessary conditions that the structural function and the rhythm function of the cyclically correlated random process must satisfy have been established. The advantages of the cyclically correlated random process in comparison with other mathematical models of cyclic signals with a variable rhythm are given. The obtained results contribute to the emergence of a more complete and rigorous theory of this class of random processes and increase the validity of the methods of their analysis and computer simulation.
VirtualActionNet: A strong two-stream point cloud sequence network for human action recognition

In this paper, we propose a strong two-stream point cloud sequence network VirtualActionNet for 3D human action recognition. In the data preprocessing stage, we transform the depth sequence into a point cloud sequence as the input of our VirtualActionNet. In order to encode intra-frame appearance structures, static point cloud technologies are first employed as a virtual action generation sequence module to abstract the point cloud sequence into a virtual action sequence. Then, a two-stream network framework is presented to model the virtual action sequence. Specifically, we design an appearance stream module for aggregating all the appearance information preserved in each virtual action frame. Moreover, a motion stream module is introduced to capture dynamic changes along the time dimension. Finally, a joint loss strategy is adopted during data training to improve the action prediction accuracy of the two-stream network. Extensive experiments on three publicly available datasets demonstrate the effectiveness of the proposed VirtualActionNet.
Focusing the First Phase – An Interdisciplinary Approach to Modeling an Interactive System on the Use-Case Indoor-Blind-Navigation

Concepts such as "resource" or "system" pose a challenge when it comes to adapting to the context. So the main problem is to adapt the concepts and the development process to the requirements of the users. The user is not only the methodological foundation of the concepts, but also a resource and a component of the system. Therefore, the initial phase of the development process becomes crucial. In order to explore the basic requirements of the product and the requirements of the potential use, it is necessary to combine several methods for interactive modeling. By linking qualitative research methods and formal iteration processes, new insights are generated. For handling the complexity of the problem, the experiences of AppPlant, Gräbert GmbH and a team at Leipzig University are applied. The project "Outdoor and Indoor Navigation for Blind and Visually Impaired People" (IBN) combines systems research with the application of social methods to improve successful human-machine interaction. The developed solution is based on the term "interactive system" of ISO 9241–11 as a methodological starting point. The key concept for an interactive system is usability, which describes the extension to "which a system, product or service can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use". In order to focus on user requirements, the iterative approach of Design Thinking, TRIZ Modeling and Mixed Methods of social sciences were combined.
The Use of Publicly Available Image Search Engines to Find Solution Ideas Efficient Use of TRIZ Information Resources

The present paper proposes a set of steps to search for data and generate relevant information to come up with solution ideas for a given problem or product improvement. Among others, the terms data and information are defined and an overview of the current state-of-the-art, which contains the TRIZ tool Function-Oriented Search and the description of possible search engines, is given. For getting relevant search results and generating information a workflow is suggested. This workflow is based on pictorial analogy to a sketched problem situation and the use of standard internet browsers with image search functionality. The task of improving the design of a tea press illustrates the workflow and its steps. The obtained solution ideas show the feasibility of the proposed workflow and lead to the conclusion that even the single use of pictorial analogy yields useful results.
Compound short- and long-term memory for memory augmented neural networks

Adding memory to artificial intelligence systems in an effective way has been addressed by researchers for many years. Recurrent neural networks and long short-term memories (LSTMs), among other neural network systems, have some inherent memory capabilities. Recently, in memory augmented neural networks, such as neural Turing machine (NTM) and its variants, a separate memory module is implemented, which can be accessed via read and write heads. Despite its capabilities in simple algorithmic tasks, such as copying and repeat copying, neural Turing machines fail when doing complex tasks with long-term dependencies due to their limited memory capacity. In this paper, we propose a new memory module in which data storing and access mechanisms are based on a graph-based neural structure rather than a matrix model. This is inspired by the human memory system, in which memories are stored via synapses (connections between neurons) and are recalled through a path passing from different neuronal networks. Differentiable mechanisms are designed for this graph-based neural memory so that it can be trained via backpropagation. The proposed structure is used to solve tasks with long-time dependencies and operations on the input sequences as well as the bAbI question answering dataset. The analysis shows that the proposed memory system can help solve these tasks better than NTM and LSTM in terms of the convergence speed and the final error
Streaming video and academic libraries: a survey on commonalities in acquisitions

AbstractAbstractThe rapid shift to online instruction at academic institutions due to the COVID-19 pandemic increased demand for streaming media resources offered by academic libraries. This shift prompted many academic libraries to employ new methods to provide access to streaming content. The authors sought to survey academic libraries with the purpose of assessing activities libraries participate in regarding streaming video acquisitions. This article examines the results of a survey the authors created and distributed to academic libraries in the United States. By sharing these results, the authors aim to provide useful information for others who manage streaming video collections.Keywords: streaming videostreaming video acquisitionsacademic librariesworkflows Disclosure statementNo potential conflict of interest was reported by the authors.
Improving Person Re-Identification With Multi-Cue Similarity Embedding and Propagation

Most existing person re-identification (Re-ID) methods rely on the visual appearance of the human body. However, face cues are rarely explored in the Re-ID community despite the face that it is an important biometric identifier for human beings. In this work, we propose a Similarity Ensemble Framework (SEF) that uses multi-cue similarity embedding and propagation to effectively fuse body and face information for person re-identification. Specifically, for each query, we first perform standard pedestrian retrieval using body and face cues, respectively, to obtain some candidate results with high confidence. Next, the body and face similarities are combined and embedded into a shared space as node features, and two graphs with the same nodes and different edges with respect to body and face affinities are constructed. Then, the similarity features are propagated in both body and face graphs using graph convolution to capture the relationship among the candidates using different cues. Lastly, the refined features are used to compute the final similarities with the query. The proposed method not only combines the similarities of body and face, but also takes into account the relationship among all the other candidate samples under different cues. Extensive experiments demonstrate that the use of face cues effectively improves the performance of person Re-ID even if the performance obtained by the face alone is much lower than that of the body, suggesting that our approach is able to capture valuable information beyond body from weaker face cues in person Re-ID scenarios.
Generalized Nets Representing C - based Programming Constructs

The paper presents generalized nets, which model control structures in C and its derivative programming languages. An approach for generating generalized nets for functions with multiple control operators is also proposed. All models are implemented through a Windows application; this process continues works on software for generalized nets. The approach is useful in applications with generalized nets as a tool for modeling power electronic devices and systems; in this way it is possible both power circuits and controllers to be described by a single tool.
An Accurate Line-of-Sight Rate Estimation Method Based on LSTM Recurrent Neural Network for Strapdown Imaging Seeker

Obtaining accurate line-of-sight (LOS) rates is a challenging task for strapdown imaging seekers. The traditional Kalman filter and its improved algorithm are difficult to deal with the non-Gaussian noise. To this end, we design a model-driven and data-driven filter using long short-term memory (LSTM) recurrent neural network. A decoupling algorithm is proposed to isolate LOS information from the attitude angle and generate one-step predictions. Predictions and observations are used as input for supervised learning, which solves the problem of difficult to describe noise features. Finally, we design a simulation experiment to compare the proposed LSTM model with extended Kalman filter (EKF), unscented Kalman filtering (UKF) and particle filter (PF) on the task of estimating the LOS angle and LOS rate. The experimental results show that the proposed LSTM model is better than the other three methods in terms of accuracy.
Temporal Merge Tree Maps:A Topology-Based Static Visualization for Temporal Scalar Data

Creating a static visualization for a time-dependent scalar field is a non-trivial task, yet very insightful as it shows the dynamics in one picture. Existing approaches are based on a linearization of the domain or on feature tracking. Domain linearizations use space-filling curves to place all sample points into a 1D domain, thereby breaking up individual features. Feature tracking methods explicitly respect feature continuity in space and time, but generally neglect the data context in which those features live. We present a feature-based linearization of the spatial domain that keeps features together and preserves their context by involving all data samples. We use augmented merge trees to linearize the domain and show that our linearized function has the same merge tree as the original data. A greedy optimization scheme aligns the trees over time providing temporal continuity. This leads to a static 2D visualization with one temporal dimension, and all spatial dimensions compressed into one. We compare our method against other domain linearizations as well as feature-tracking approaches, and apply it to several real-world data sets.
Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement

The super-resolution reconstruction method based on deep learning can significantly improve the spatial superresolution of remote sensing images.However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of super-resolution reconstruction.This paper proposed a new super-resolution reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of super-resolution reconstruction of remote sensing images.Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator.But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations.Specifically, a depth-wise convolution is used to extract the local context information of each band of the image.A depth-wise dilation convolution is used to capture the remote context information in the image.Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images.In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator, to improve the model's ability to identify image details.And the spectral-spatial total variation loss function is introduced to ensure the spectral-spatial reliability of the reconstructed images.The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better super-resolution reconstruction performance.Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification.