Reviewerly/exHarmony huggingface dataset:
Indefinite twin support vector machine with DC functions programming

Twin support vector machine (TWSVM) is an efficient algorithm for binary classification. However, the lack of the structural risk minimization principle restrains the generalization of TWSVM and the guarantee of convex optimization constraints TWSVM to only use positive semi-definite kernels (PSD). In this paper, we propose a novel TWSVM for indefinite kernel called indefinite twin support vector machine with difference of convex functions programming (ITWSVM-DC). The indefinite TWSVM (ITWSVM) leverages a maximum margin regularization term to improve the generalization of TWSVM and a smooth quadratic hinge loss function to make the model continuously differentiable. The representer theorem is applied to the ITWSVM and the convexity of the ITWSVM is analyzed. In order to address the non-convex optimization problem when the kernel is indefinite, a difference of convex functions (DC) is used to decompose the non-convex objective function into the subtraction of two convex functions and a line search method is applied in the DC algorithm to accelerate the convergence rate. A theoretical analysis illustrates that ITWSVM-DC can converge to a local optimum and extensive experiments on indefinite and positive semi-definite kernels show the superiority of ITWSVM-DC.
A Survey on Applications of Augmented, Mixed and Virtual Reality for Nature and Environment

Augmented, virtual and mixed reality (AR/VR/MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing. However, the possibilities that AR/VR/MR offer in the area of environmental applications are not yet widely explored. In this paper we present the outcome of a survey meant to discover and classify existing AR/VR/MR applications that can benefit the environment or increase awareness on environmental issues. We performed an exhaustive search over several online publication access platforms and past proceedings of major conferences in the fields of AR/VR/MR. Identified relevant papers were filtered based on novelty, technical soundness, impact and topic relevance, and classified into different categories. Referring to the selected papers, we discuss how the applications of each category are contributing to environmental protection and awareness. We further analyze these approaches as well as possible future directions in the scope of existing and upcoming AR/VR/MR enabling technologies.
Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods –including entropy, mutual information, and the Compounded Aggregated Positivity Index– allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties’ positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations.
Recursive multi-model complementary deep fusion for robust salient object detection via parallel sub-networks

Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper and more complex, which easily homogenize their learned deep features, resulting in a clear performance bottleneck. In sharp contrast to the conventional “deeper” schemes, this paper proposes a “wider” network architecture which consists of parallel sub-networks with totally different network architectures. In this way, those deep features obtained via these two sub-networks will exhibit large diversity, which will have large potential to be able to complement with each other. However, a large diversity may easily lead to the feature conflictions, thus we use the dense short-connections to enable a recursively interaction between the parallel sub-networks, pursuing an optimal complementary status between multi-model deep features. Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections. Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework.
Adaptive Label-Aware Graph Convolutional Networks for Cross-Modal Retrieval

The cross-modal retrieval task has raised continuous attention in recent years with the increasing scale of multi-modal data, which has broad application prospects including multimedia data management and intelligent search engine. Most existing methods mainly project data of different modalities into a common representation space where label information is often exploited to distinguish samples from different semantic categories. However, they typically treat each label as an independent individual and ignore the underlying semantic structure of labels. In this paper, we propose an end-to-end adaptive label-aware graph convolutional network (ALGCN) by designing both the instance representation learning branch and the label representation learning branch, which can obtain modality-invariant and discriminative representations for cross-modal retrieval. Firstly, we construct an instance representation learning branch to transform instances of different modalities into a common representation space. Secondly, we adopt Graph Convolutional Network (GCN) to learn inter-dependent classifiers in the label representation learning branch. In addition, a novel adaptive correlation matrix is proposed to efficiently explore and preserve the semantic structure of labels in a data-driven manner. Together with a robust self-supervision loss for GCN, the GCN model can be supervised to learn an effective and robust correlation matrix for feature propagation. Comprehensive experimental results on three benchmark datasets, NUS-WIDE, MIRFlickr and MS-COCO, demonstrate the superiority of ALGCN, compared with the state-of-the-art methods in cross-modal retrieval.
An efficient technique for cloud storage using secured de-duplication algorithm

Techniques of data compression involve de-duplication of data that plays an important role in eliminating duplicate copies of information and has been widely employed in cloud storage to scale back the storage capacity and save information measure. A secure AES encryption de-duplication system for finding duplication with the meaning and store up it in the cloud. To protect the privacy of sensitive information whereas supporting de-duplication, The AES encryption technique and SHA-256 hashing algorithm have been utilized to encrypt the information before outsourcing. Pre-processing is completed and documents are compared and verified with the use of wordnet. Cosine similarity is employed to see the similarity between both the documents and to perform this, a far economical VSM data structure is used. Wordnet hierarchical corpus is used to see syntax and semantics so that the identification of duplicates is done. NLTK provides a large vary of libraries and programs for symbolic and statistical natural language process (NLP) for the Python programming language that is used here for the unidentified words by cosine similarity. Within the previous strategies, cloud storage was used abundantly since similar files were allowed to store. By implementing our system, space for storing is reduced up to 85%. Since AES and SHA-256 are employed, it provides high security and efficiency.
Blockchain as a decentralized communication tool for sustainable development

This research introduces the distributed ledger technology (blockchain) in terms of sustainable development in the future. This study is based on private, public and global communications in various industries. The growing demands for secure and decentralized transactions around the world demonstrate the need for Blockchain technology as a medium of operations and communications. The blockchain system is completely decentralized and allows users to exchange messages in an efficient and secure manner. The highlight of the paper is the anticipated future use of blockchain networks as a communication tool in every business and digital economy for a safe and sustainable development, while presenting its advantages and limitations that have been confirmed based on the experiences of various experts. Moreover, the article provides recommendations for the use of blockchain to greatly complicate its identification in future communications. The paper also concludes that the use of the blockchain system could be beneficial and enhance future communications and digital technologies.
Differential-Linear Cryptanalysis from an Algebraic Perspective

The differential-linear cryptanalysis is an important cryptanalytic tool in cryptography, and has been extensively researched since its discovery by Langford and Hellman in 1994. There are nevertheless very few methods to study the middle part where the differential and linear trail connect. In this paper, we study differential-linear cryptanalysis from an algebraic perspective. We first introduce a technique called Differential Algebraic Transitional Form (DATF) for differential-linear cryptanalysis, then develop a new theory of estimation of the differential-linear bias and techniques for key recovery in differential-linear cryptanalysis. The techniques are applied to the CAESAR and LWC finalist Ascon, the AES finalist Serpent, and the eSTREAM finalist Grain v1. The bias of the differential-linear approximation is estimated for Ascon and Serpent. The theoretical estimates of the bias are more accurate than that obtained by the Differential-Linear Connectivity Table (Bar-On et al., EUROCRYPT 2019), and the techniques can be applied with more rounds. Our general techniques can also be used to estimate the bias of Grain v1 in differential cryptanalysis, and have a markedly better performance than the Differential Engine tool tailor-made for the cipher. The improved key recovery attacks on round-reduced variants of these ciphers are then proposed. To the best of our knowledge, they are thus far the best known cryptanalysis of Serpent, as well as the best differential-linear cryptanalysis of Ascon and the best initialization analysis of Grain v1. The results have been fully verified by experiments. Notably, security analysis of Serpent is one of the most important applications of differential-linear cryptanalysis in the last two decades. The results in this paper update the differential-linear cryptanalysis of Serpent-128 and Serpent-256 with one more round after the work of Biham, Dunkelman and Keller in 2003.
Deep Learning Techniques for Advancing 6G Communications in the Physical Layer

As current 5G communication systems cannot fulfill the stringent requirements brought by emerging applications, 6G will innovatively employ deep learning (DL) techniques to fundamentally rethink the communication systems design problem from the bottom to top layers. Although recent evidence has shown the power of DL techniques in the communication domain, the exploration and utilization of DL techniques in communication systems is still in its infancy and should come in a progressive manner. To effectively and efficiently implement DL techniques in future 6G communications in the physical layer, we give some potential deployment strategies and key enabling technologies that relate to 6G in terms of joint design of block-structured and end-to-end DL, integration of model-driven and data-driven DL, combination of online and offline training, ubiquitous learning and explainable DL techniques.
Blockchain-Based Reliable and Privacy-Aware Crowdsourcing With Truth and Fairness Assurance

The ubiquity of crowdsourcing has reshaped the static sensor-enabled data sensing paradigm with cost efficiency and flexibility. Still, most existing triangular crowdsourcing systems only work under the centralized trust assumption and suffer from various attacks mounted by malicious users. Although incorporating the emerging blockchain technology into crowdsourcing provides a possibility to mitigate some of the issues, how to concretely implement the crucial components and their functionalities in a verifiable and privacy-aware manner remains unaddressed. In this article, we present BRPC, a blockchain-based decentralized system for general crowdsourcing. BRPC integrates the confident-aware truth discovery algorithm to provide task requesters with reliable task truths while evaluating each worker’s data quality. To mitigate the biased evaluation of malicious requesters, we propose a privacy-aware verification protocol leveraging the threshold Paillier cryptosystem, with which a certain number of workers can collaboratively verify the evaluation results without knowing any sensory data. Furthermore, we define the three roles of a user and elaborate a comprehensive reputation evaluation model enforced by smart contracts for its trustworthy running. Financial and social incentives are both offered to motivate users’ honest participation. Finally, we implement a prototype of BRPC and deploy it on the Ethereum blockchain. Theoretical analyses and experiment results show its security and practicality.
Learning sequentially diversified representations for fine-grained categorization

Learning representation carrying rich local information is essential for recognizing fine-grained objects. Existing methods to this task resort to multi-stage frameworks to capture fine-grained information. However, they usually require multiple forward passes of the backbone network, resulting in efficiency deterioration. In this paper, we propose Sequentially Diversified Networks (SDNs) that enrich representation by promoting their diversity while maintaining the extraction efficiency. Specifically, we construct multiple lightweight sub-networks to model mutually different scales of discriminative patterns. The design of these sub-networks follows the sequentially diversified constraint, encouraging them to be varied in spatial attention. By inserting these sub-networks into a single backbone network, SDNs enable information interaction among local regions of the fine-grained image. In this way, SDNs jointly promote diversity in terms of scale and spatial attention in the one-stage pipeline, thereby facilitating the learning of diversified representation efficiently. We evaluate our proposed method on three challenging datasets, namely CUB-200-2011, Stanford-Cars, and FGVC-Aircraft. Experiments demonstrate its effectiveness in learning diversified information. Moreover, our method achieves state-of-the-art performance, only requiring a single forward pass of the backbone network, which reduces inference time noticeably.
Chaotic Path Planning for 3D Area Coverage Using a Pseudo-Random Bit Generator from a 1D Chaotic Map

This work proposes a one-dimensional chaotic map with a simple structure and three parameters. The phase portraits, bifurcation diagrams, and Lyapunov exponent diagrams are first plotted to study the dynamical behavior of the map. It is seen that the map exhibits areas of constant chaos with respect to all parameters. This map is then applied to the problem of pseudo-random bit generation using a simple technique to generate four bits per iteration. It is shown that the algorithm passes all statistical NIST and ENT tests, as well as shows low correlation and an acceptable key space. The generated bitstream is applied to the problem of chaotic path planning, for an autonomous robot or generally an unmanned aerial vehicle (UAV) exploring a given 3D area. The aim is to ensure efficient area coverage, while also maintaining an unpredictable motion. Numerical simulations were performed to evaluate the performance of the path planning strategy, and it is shown that the coverage percentage converges exponentially to 100% as the number of iterations increases. The discrete motion is also adapted to a smooth one through the use of B-Spline curves.
Statistical Model Checking for Hyperproperties

Hyperproperties have shown to be a powerful tool for expressing and reasoning about information-flow security policies. In this paper, we investigate the problem of statistical model checking (SMC) for hyperproperties. Unlike exhaustive model checking, SMC works based on drawing samples from the system at hand and evaluate the specification with statistical confidence. The main benefit of applying SMC over exhaustive techniques is its efficiency and scalability. To reason about probabilistic hyperproperties, we first propose the temporal logic HyperPCTL* that extends PCTL* and HyperPCTL. We show that HyperPCTL* can express important probabilistic information-flow security policies that cannot be expressed with HyperPCTL. Then, we introduce SMC algorithms for verifying HyperPCTL* formulas on discrete-time Markov chains, based on sequential probability ratio tests (SPRT) with a new notion of multidimensional indifference region. Our SMC algorithms can handle both non-nested and nested probability operators for any desired significance level. To show the effectiveness of our technique, we evaluate our SMC algorithms on four case studies focused on information security: timing side-channel vulnerability in encryption, probabilistic anonymity in dining cryptographers, probabilistic noninterference of parallel programs, and the performance of a randomized cache replacement policy that acts as a countermeasure against cache flush attacks.
Drone classification using RF signal based spectral features

Drone detection and classification, important in military and civilian applications, are performed using different sensor signals. Proposed study handles this task using Radio Frequency (RF) signals utilizing basic machine learning methods. It is composed of two main stages as feature extraction succeeded by training/testing of the model. In feature extraction stage, valuable information for classification, contained in the RF signal, is obtained. For this purpose, spectral features, frequently used in speech processing applications, are employed. Specifically, Power Spectral Density (PSD), Mel-Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) are adopted by adjusting filter bank margins and parameters for this task. In the second stage, a Support Vector Machine (SVM) classifier is first trained based on the obtained features and finally tested to measure its performance. All experimental studies are carried out using publicly available DroneRF dataset. This dataset contains 2-Class, 4-Class and 10-Class samples for drone existence vs. background (BG), drone types and drone operation modes, respectively. The best classification results are obtained using, PSD, MFCC and LFCC based features for 2-Class, MFCC and LFCC based features for 4-Class and LFCC based features for 10-Class. Accuracy rates for 2-Class, 4-Class and 10-Class are 100%, 98.67% and 95.15%, respectively. These results show that the proposed method outperforms the results given in the literature for DroneRF dataset.
Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction

Understanding complex social interactions among agents is a key challenge for trajectory prediction. Most existing methods consider the interactions between pairwise traffic agents or in a local area, while the nature of interactions is unlimited, involving an uncertain number of agents and non-local areas simultaneously. Besides, they treat heterogeneous traffic agents the same, namely those among agents of different categories, while neglecting people’s diverse reaction patterns toward traffic agents in different categories. To address these problems, we propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN), which predicts trajectories of heterogeneous agents in multiple categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area. Meanwhile, a hierarchical graph attention module is proposed to obtain category-to-category interaction and agent-to-agent interaction. Finally, parameters of a Gaussian Mixture Model are estimated for generating the future trajectories. Extensive experimental results on benchmark datasets demonstrate a significant performance improvement of our method over the state-of-the-art methods.
Quality Requirements for Implementing Augmented Reality in Heritage Spaces: Teachers’ Perspective

As a consequence of the scarcity of studies of augmented reality (AR) in Spain, this study developed a questionnaire to evaluate teachers’ perceptions on the use of AR for heritage teaching (n = 347 teachers: n = 131 in-service/n = 216 in-training). The objectives were to: (1) identify teachers’ existing knowledge about AR; (2) evaluate educational strategies teachers value most in AR apps for teaching; (3) evaluate necessary AR functions; (4) determine desired technical and functional characteristics; and (5) compare any significant differences between the two groups. The results provide a contribution to the increasing implementation of AR apps in heritage education, which promotes the understanding, enjoyment, experience and knowledge of heritage. Heritage education is increasingly present in Spanish classrooms due to awareness of the country’s rich heritage, and AR is a good tool for understanding and linking society with its heritage. From the results, it is clear that, despite their desire for implementation, there is a lack of teacher training in applying AR; both subsamples pointed out the importance of humanising the explanations in AR apps with experts or actors who mediate heritage and value the ease of use of these apps, multifunctionality, low battery consumption, and use in the background. Significant differences suggest potentially greater educational commitment among active teachers who seek deep and meaningful learning, above the superficiality and enjoyment reported by teachers in training and, on the contrary, the value placed on technical and visual aspects, related to the high use of mobile devices.