Reviewerly/exHarmony huggingface dataset:
Improved Security Analysis for Nonce-Based Enhanced Hash-then-Mask MACs

In this paper, we prove that the nonce-based enhanced hash-then-mask MAC ( $$\mathsf {nEHtM}$$ ) is secure up to $$2^{\frac{3n}{4}}$$ MAC queries and $$2^n$$ verification queries (ignoring logarithmic factors) as long as the number of faulty queries $$\mu $$ is below $$2^\frac{3n}{8}$$ , significantly improving the previous bound by Dutta et al. Even when $$\mu $$ goes beyond $$2^{\frac{3n}{8}}$$ , $$\mathsf {nEHtM}$$ enjoys graceful degradation of security. The second result is to prove the security of PRF-based $$\mathsf {nEHtM}$$ ; when $$\mathsf {nEHtM}$$ is based on an n-to-s bit random function for a fixed size s such that $$1\le s\le n$$ , it is proved to be secure up to any number of MAC queries and $$2^s$$ verification queries, if (1) $$s=n$$ and $$\mu <2^{\frac{n}{2}}$$ or (2) $$\frac{n}{2}<s<2^{n-s}$$ and $$\mu <\max \{2^{\frac{s}{2}},2^{n-s}\}$$ , or (3) $$s\le \frac{n}{2}$$ and $$\mu <2^{\frac{n}{2}}$$ . This result leads to the security proof of truncated $$\mathsf {nEHtM}$$ that returns only s bits of the original tag since a truncated permutation can be seen as a pseudorandom function. In particular, when $$s\le \frac{2n}{3}$$ , the truncated $$\mathsf {nEHtM}$$ is secure up to $$2^{n-\frac{s}{2}}$$ MAC queries and $$2^s$$ verification queries as long as $$\mu <\min \{2^{\frac{n}{2}},2^{n-s}\}$$ . For example, when $$s=\frac{n}{2}$$ (resp. $$s=\frac{n}{4}$$ ), the truncated $$\mathsf {nEHtM}$$ is secure up to $$2^{\frac{3n}{4}}$$ (resp. $$2^{\frac{7n}{8}}$$ ) MAC queries. So truncation might provide better provable security than the original $$\mathsf {nEHtM}$$ with respect to the number of MAC queries.
Binarization of ESPI fringe patterns based on an M-net convolutional neural network

The fringe skeleton method is the most straightforward method to estimate phase terms in electronic speckle pattern interferometry (ESPI). It usually needs to binarize the fringe patterns. However, the massive inherent speckle noise and intensity inhomogeneity in ESPI fringe patterns make it difficult to binarize the ESPI fringe patterns. In this paper, we propose a binarization method for ESPI fringe patterns based on a modified M-net convolutional neural network. Our method regards the binarization of fringe patterns as a segmentation problem. The M-net is an excellent network for segmentation and has proven to be a useful tool for skeleton extraction in our previous work. Here we further modify the structure of the previous network a bit to suit our task. We train the network by pairs of ESPI fringe patterns and corresponding binary images. After training, we test our method on 20 computer-simulated and three groups of experimentally obtained ESPI fringe patterns. The results show that even for fringe patterns with high noise and intensity inhomogeneity, our method can obtain good binarization results without image preprocessing. We also compare the modified M-net with a classic segmentation network, the U-net, and a residual encoder-decoder network (RED-net). The RED-net was used for binarization of document images. The experimental results prove the effectiveness of our method.
Mind the Propagation of States

Impossible differentials cryptanalysis and impossible polytopic cryptanalysis are the most effective approaches to estimate the security of block ciphers. However, the previous automatic search methods of their distinguishers, impossible differentials and impossible polytopic transitions, neither consider the impact of key schedule in the single-key setting and the differential property of large S-boxes, nor apply to the block ciphers with variable rotations. Thus, unlike previous methods which focus on the propagation of the difference or s-difference, we redefine the impossible differentials and impossible $$(s+1)$$ -polytopic transitions according to the propagation of state, which allow us to break through those limitations of the previous methods. Theoretically, we prove that traditional impossible differentials and impossible $$(s+1)$$ -polytopic transitions are equivalent to part of our redefinitions, which have advantages from broader view. Technically, we renew the automatic search model and design an SAT-based tool to evaluate our redefined impossible differentials and impossible $$(s+1)$$ -polytopic transitions efficiently. As a result, for GIFT64, we get the 6-round impossible differentials which cannot be detected by all previous tools. For PRINTcipher, we propose the first modeling method for the key-dependent permutation and key-dependent S-box. For MISTY1, we derive 902 4-round impossible differentials by exploiting the differential property of S-boxes. For RC5, we present the first modeling method for the variable rotation and get 2.5-round impossible differentials for each version of it. More remarkable, our tool can be used to evaluate the security of given cipher against the impossible differentials, and we prove that there exists no 5-round 1 input active word and 1 output active word impossible differentials for AES-128 even consider the relations of 3-round keys. Besides, we also get the impossible $$(s+1)$$ -polytopic transitions for PRINTcipher, GIFT64, PRESENT, and RC5, all of which can cover more rounds than their corresponding impossible differentials as far as we know.
Contextualized BERT Sentence Embeddings for Author Profiling: The Cost of Performances

The necessity to know information about the real identity of an online subject is a highly relevant issue in User Profiling, especially for analysis from digital sources such as social media. The digital identity of a user does not always present explicit data about her offline life such as age, gender, work, and more. This problem makes the task of user profiling complex and incomplete. For many years this issue has received a considerable amount of attention from the whole community, which has developed several solutions, also based on machine learning, to estimate user characteristics. The increasing diffusion of deep learning approaches has allowed, on the one hand, to obtain a considerable increase in predictive performance, but on the other hand, to have available models that cannot be interpreted and that require very high computational power. Considering the validity of new pre-trained language models on extensive data for resolving many natural language processing and classification tasks, we decided to propose a BERT-based approach (BERT-DNN) also for the author profiling task. In a first analysis, we compared the results obtained by our model with them of more classical approaches. As a follow, a critical analysis was carried out. We analyze the advantages and disadvantages of these approaches also in terms of resources needed to run them. The results obtained by our model are encouraging in terms of reliability but very disappointing if we consider the computational power required for running it.
Generalized Photon Added and Subtracted <i>f</i>‐Deformed Displaced Fock States

Abstract In this paper, by making use of the nonlinear coherent states approach, the generalized photon added and subtracted f ‐deformed displaced Fock states are introduced. In other words, a natural link between photon added and subtracted displaced Fock states and nonlinear coherent states associated with nonlinear oscillator algebra is obtained. It is found that various kinds of nonclassical states can be generated by adopting appropriately controlling parameters in both linear and nonlinear regimes. Moreover, examining some of the most nonclassical properties such as Mandel's Q parameter, different types of squeezing, namely, quadrature, amplitude–squared and phase entropic squeezing, and Vogel's characteristic function, the nonclassicality features of the considered quantum states of interest are studied. Furthermore, to obtain the degree of quantum coherence, the relative entropy of coherence is investigated. Indeed, the nonclassicality aspects of the states obtained have been numerically studied to understand the roles of deformation functions, photons added and subtracted, and photon number occupied in the Fock state on physical properties. It is demonstrated that the depth and the domain of the nonclassicality features of the system can properly be controlled by selecting the suitable parameters.
A Viewpoint Aware Multi-Task Learning Framework for Fine-Grained Vehicle Recognition

The goal of fine-grained vehicle recognition is to identify the exact subtype of the vehicle from a given image.It plays an important role in the intelligent traffic surveillance system.Although fine-grained vehicle recognition has attracted more and more research interest, it remains as an open problem for vehicle images taken from arbitrary viewpoints.In this study, we present a one-stage deep multi-task learning framework for fine-grained vehicle recognition in traffic surveillance, which performs the fine-grained vehicle recognition and viewpoint estimation simultaneously.In the proposed framework, the fine-grained vehicle recognition is the main task which classifies images into different types, and the viewpoint estimation task is the auxiliary task to learn helpful viewpoint-aware features for improving the main task.We evaluate our method on two common used large-scale fine-grained vehicle recognition datasets, including BoxCars116k and CompCars.The experimental results testify that the proposed multi-task framework can improve the accuracy by incorporating the viewpoint information of the vehicles.In comparison to the stateof-the-art, our approach goes beyond, only requiring 3D bounding box for the training phase, which is important for future inferences using the trained model.
Remote Sensing Image Registration Based on Local Affine Constraint With Circle Descriptor

Many methods have been developed to improve the performance of image registration. In this letter, we introduce a novel method based on a local affine constraint for remote sensing image registration, which can be widely used in image processing and pattern recognition. Our algorithm has three components. First, we exploit the scale invariant feature transform (SIFT) method to extract feature points and calculate the gradient magnitude to establish feature descriptors with a circular instead of square neighborhood. Second, an initial matching is implemented by the nearest neighbor distance ratio (NNDR) and the fast sample consensus (FSC) algorithm. Finally, fine registration is established using more correct matches obtained by the local affine transformation circular region search algorithm. Experimental results show that the proposed method achieves subpixel accuracy. In addition, both the correct matching rate and registration demonstrate the effectiveness and efficiency of our method.
Model-Driven Architecture of Extreme Learning Machine to Extract Power Flow Features

Probabilistic power flow (PPF) calculation is an important power system analysis tool considering the increasing uncertainties. However, existing calculation methods cannot simultaneously achieve high precision and fast calculation, which limits the practical application of the PPF. This article designs a specific architecture of the extreme learning machine (ELM) in a model-driven pattern to extract the power flow features and therefore accelerate the calculation of PPF. ELM is selected because of the unique characteristics of fast training and less intervention. The key challenge is that the learning capability of the ELM for extracting complex features is limited compared with deep neural networks. In this article, we use the physical properties of the power flow model to assist the learning process. To reduce the learning complexity of the power flow features, the feature decomposition and nonlinearity reduction method is proposed to extract the features of the power flow model. An enhanced ELM network architecture is designed. An optimization model for the hidden node parameters is established to improve the learning performance. Based on the proposed model-driven ELM architecture, a fast and accurate PPF calculation method is proposed. The simulations on the IEEE 57-bus and Polish 2383-bus systems demonstrate the effectiveness of the proposed method.
Priority-Awareness of Non-Functional Requirements under Uncertainty

A Self-adaptive system (SAS) is required to continuously satisfy its requirements at runtime under environmental uncertainty. As the run-time context changes, the system may need to re-conFigure itself, and since resources are finite this may require trade-offs between the SAS's non-functional requirements (NFRs). A number of runtime modelling techniques have been developed for resolution of uncertainty in SASs. However, current techniques lack the explicit runtime representation of NFR priorities, leading to the risk that adaptations may fail to respect the NFRs' priorities. In order to address this problem, my research will focus on the explicit representation and a priori elicitation of distinct NFRs' priorities to support autonomous NFR tradeoff decisions. This paper gives an overview of the research area by identifying gaps in existing state of the art techniques. The paper also provides a description of the prospective approaches for solving the problem along with current progress made and next steps.
Evolutionary algorithms deceive humans and machines at image classification: an extended proof of concept on two scenarios

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Compact domain-specific co-processor for accelerating module lattice-based KEM

We present a domain-specific co-processor to speed up Saber, a post-quantum key encapsulation mechanism competing on the NIST Post-Quantum Cryptography standardization process. Contrary to most lattice-based schemes, Saber doesn’t use NTT-based polynomial multiplication. We follow a hardware-software co-design approach: the execution is performed on an ARM core and only the most computationally expensive operation, i.e., the polynomial multiplication, is offloaded to the co-processor to obtain a compact design. We exploit the idea of distributed computing at micro-architectural level together with novel algorithmic optimizations to achieve approximately a 6 times speedup with respect to optimized software at a small area cost, which we demonstrate on a Zynq-7000 ARM/FPGA SoC.
Geometrical features of lips using the properties of parabola for recognizing facial expression

Various real-time applications such as Human–Computer Interactions, Psychometric analysis, etc. use facial expressions as one of the important parameters. The researchers have used Action Units (AU) of the face as feature points and its deformation is compared with the reference points on the face to estimate the facial expressions. Among many parts of the face, features from the mouth contribute largely to all the well-known emotions. In this paper, the parabola theory is used to identify and mark various points on the lips. These points are considered as feature points to construct feature vectors. The Latus Rectum, Focal Point, Directrix, Vertex, etc. are also considered to identify the feature points of the lower lips and upper lips. The proposed approach is evaluated on benchmark datasets such as JAFFEE and Cohn–Kanade dataset and it is found that the performance is encouraging in understanding the facial expressions. The results are compared with contemporary methods and found that the proposed approach has given good classification accuracy in recognizing facial expressions.
Applications of Medical Informatics and Data Analysis Methods

The science of statistics contributes to the development and application of tools for the design, analysis, and interpretation of empirical medical studies [...]
Wrapper based Feature Selection using Integrative Teaching Learning Based Optimization Algorithm

The performance of the machine learning models mainly relies on the key features available in the training dataset. Feature selection is a significant job for pattern recognition for finding an important group of features to build classification models with a minimum number of features. Feature selection with optimization algorithms will improve the prediction rate of the classification models. But, tuning the controlling parameters of the optimization algorithms is a challenging task. In this paper, we present a wrapper-based model called Feature Selection with Integrative Teaching Learning Based Optimization (FS-ITLBO), which uses multiple teachers to select the optimal set of features from feature space. The goal of the proposed algorithm is to search the entire solution space without struck in the local optima of features. Moreover, the proposed method only utilizes teacher count parameter along with the size of the population and a number of iterations. Various classification models have been used for finding the fitness of instances in the population and to estimate the effectiveness of the proposed model. The robustness of the proposed algorithm has been assessed on Wisconsin Diagnostic Breast Cancer (WDBC) as well as Parkinson’s Disease datasets and compared with different wrapper-based feature selection techniques, including genetic algorithm and Binary Teaching Learning Based Optimization (BTLBO). The outcomes have confirmed that FS-ITLBO model produced the best accuracy with the optimal subset of features
Risk Analysis Methodology to Blockchain-based Solutions

The success of blockchain-based solutions, not only on a large scale, but since the beginning of the pilot phase, depends on a set of key factors such as performance, efficiency, usability, scalability. In particular, security, privacy and trust are fundamental, as the use of personal data represents, ultimately, immeasurable impacts on people’s lives. This paper presents the results of risk analysis on a blockchain-based self-sovereign identity solution (SSI), focused on the aspects of privacy, security, protection, resilience and reliability. FINID is a blockchain-based SSI solution that aims to create a unique and portable identity that is enriched and used by Brazilians’ financial institutions.
Energy- and Quality-Aware Video Request Policy for Wireless Adaptive Streaming Clients

We present a straightforward, non-intrusive adaptive bit rate streaming segment quality selection policy which aims at extending battery lifetime during playback while limiting the impact on the user's quality of experience, thus benefiting consumers of video streaming services. This policy relies on the relationship between the available channel bandwidth and the bit rate of the representations in the quality ladder. It results from the characterization of the energy consumed by smartphones when running adaptive streaming client applications for different network connections (Wifi, 4G, and 5G) and the modeling of the energy consumed as a function of said relationship. Results show that a significant amount of energy can be saved (10 to 30%) by slightly modifying the default policy at the expense of a controlled reduction of video quality.
End-to-End Learning of Secure Wireless Communications: Confidential Transmission and Authentication

Aiming to provide more efficient and robust physical layer security strategies for wireless communications, this article investigates the endogenous security of end-to-end learning of communication by addressing two main security issues of communication: confidential transmission and user authentication. For confidential transmission, we have redesigned the loss function of the autoencoder-based deep learning communication model to combat illegal eavesdropping over wireless broadcast channels. While assuming that the eavesdropper has three different ways of decoding prior information, the probability of successful eavesdropping attack is evaluated using the bit error rate criterion. In terms of user authentication, an authentication scheme using "symbol-level fingerprints" is designed for a single user, which takes advantage of the high complexity of parameters of the deep learning model and its natural sensitivity to training conditions. In addition, by leveraging a denoising autoencoder, we extend the authentication to adapt to the multi-user access situation. Experiments have shown that the proposed authentication scheme could guarantee reliability under dynamic channel and resistance to wireless attacks. The results inspire us to rebuild an efficient physical layer secure framework for wireless communication through a new deep learning method.
An eye-tracking study of reading long and short novel and lexicalized compound words

An eye-tracking experiment examined the recognition of novel and lexicalized compound words during sentence reading. The frequency of the head noun in modifier-head compound words was manipulated to tap into the degree of compositional processing. This was done separately for long (12–16 letter) and short (7-9 letters) compound words. Based on the dual-route race model (Pollatsek et al., 2000) and the visual acuity principle (Bertram &amp; Hyönä, 2003), long lexicalized and novel compound words were predicted to be processed via the decomposition route and short lexicalized compound words via the holistic route. Gaze duration and selective regression-path duration demonstrated a constituent frequency effect of similar size for long lexicalized and novel compound words. For short compound words the constituent frequency effect was negligible for lexicalized words but robust for novel words. The results are consistent with the visual acuity principle that assumes long novel compound words to be recognized via the decomposition route and short lexicalized compound words via the holistic route.
Exploring Pointer Assisted Reading (PAR): Using Mouse Movements to Analyze Web Users’ Reading Behaviors and Patterns

This paper explores Pointer Assisted Reading (PAR), a reading behavior consisting of moving the mouse cursor (also known as the pointer) along sentences to mark the reading position, similarly to finger-pointing when reading a book. The study shows that PAR is an uncommon reading technique and examines methods to extract and visualize the PAR activity of web users. An analysis shows that PAR data of real users reveal reading properties, such as speed, and reading patterns, such as skipping and rereading. Eye-tracking is usually used to analyze user reading behaviors. This paper advocates for considering PAR-tracking as a feasible alternative to eye-tracking on websites, as tracking the eye gaze of ordinary web users is usually impractical. PAR data might help in spotting quality issues in the textual content of a website, such as unclear text or content that might not interest the website users, based on analyzing reading properties and patterns (e.g. reading speed, skipping, and rereading). Accordingly, PAR-tracking may have various practical applications in a wide range of fields, and particularly in educational technology, e-learning, and web analytics.