Reviewerly/exHarmony huggingface dataset:
Markov-Chain-Based Musical Creative Intelligent Agent Passing Successfully the Turing Test

Nowadays, the multimedia industry needs a quick generation of large volumes of new music content. For this purpose, we present in this paper an efficient solution for automated music composition based on Artificial Intelligence (AI). In particular, the developed approach integrates multiple machine learning algorithms based on Markov chains (MCs) within a system comprising a user-friendly interface for intelligent human-computer interactions, leading to a full, creative intelligent agent which is able to produce Musical Instrument Digital Interface (MIDI) format files with new, original music based on monophonic input seed music provided by the user. This AI-based music composition agent goes beyond algorithmic music generation, since it autonomously generates new pieces of music with a balance between harmonious musical form and non-deterministic novelty. Indeed, after passing through all the testing levels, this creative software agent has been deployed in real-world conditions and passed successfully the Turing test.
Decentralized Social Network

The work is devoted to the analysis of technologies and the design of a responsive social network application with elements of decentralization. Despite all the positives that social networks bring, we can’t forget the potential problems associated with their use. Some of the problems can be minimized by using decentralization, which has the effect of limiting third-party control.The centralized part is created using the Vue.js and Laravel frameworks, which provide basic functions. In the context of decentralization, is very important the creation of a smart contract that is stored in the Ethereum blockchain. By using the Web3.js library, it is possible with a smart contract and a MetaMask wallet to carry out the transactions necessary for storing data on the blockchain.To increase efficiency, the IPFS distributed file system is used, which offers storage of larger files or texts and generates their content, while it is possible to access this data. By decentralizing these functions, data immutability is achieved, censorship is excluded, and data security is also provided.
Securing IoT Smart Irrigation Systems with Adapted Blockchain Based Approach

Blockchain (BC) technology promotes the scalability and stability of Internet of Things (IoT) applications and avoids the need for a single trusted authority. However, an IoT Smart Irrigation System (ISIS) comprises constrained devices such as microcontrollers and IoT gateways. A lightweight consensus mechanism is required to reduce computational costs in such systems. This paper presents a novel secure architecture for ISIS based on an adapted BC approach by implementing a lightweight Proof of Authority (PoA) consensus mechanism called Aura (Authority Round). The proposed mechanism is designed to overcome reported security limitations in commonly private BC. We apply our solution to the existing ISIS prototype developed in our laboratory. Experimental results show the feasibility and efficiency of BC technology in ISIS.
Extending Synthetic Data Evaluation Metrics

This paper explores the impact of the evolving evaluation metrics used to evaluate synthetic data and the related generative mechanism in the context of Differential Privacy (DP). Specifically, the authors provide an overview of current synthetic data evaluation methodologies and most commonly used metrics, identifying improvement possibilities. As the field continues to evolve, the authors also propose an improved version of a previously introduced comprehensive evaluation metric useful to redefine such evaluation frameworks in order to make them more complete and exhaustive. This proposed composite metric offers new ideas and directions for future comprehensive quantitative analysis of differentially private synthetic data and, thus, of their related generators.
Increasing of security of an actual social networks

One of the most visited web pages nowadays is undoubtedly social networks. Millions of users visit them every day, creating posts, adding comments, or sharing posts from other users. It is true that almost everything can be added to a social network today, leading to the creation of large communities where different untruthful or hateful speeches may arise. These speeches can be transferred to the real world. The increasing number of social network users necessitates an increase in their security. In the first chapter of our work, we examine the current state of the most widely used social networks and introduce a new system for enhancing their security. We then propose the system. Our design contains various UML diagrams mentioned in the second chapter. In conclusion, we discuss the advantages and disadvantages of our design and suggest possible future expansions for our system.
Blockchain-Based System for Security and Privacy of Students' Health Records

Nowadays and after the outbreak of the coronavirus pandemic, companies, institutions, schools, and universities were obliged to switch to remote mode. In this context, digitalization has played a crucial role in keeping the activities continuity and the business running. Consequently, the need for data storage spaces and data security has grown exponentially. As a solution, Blockchain technology has been widely implemented and proven its efficiency in different application fields. In this paper, we propose to design a blockchain architecture along with a smart contract in order to secure students' medical data. The use of blockchain technology in the educational field may potentially prevent the falsification or unauthorized access to critical medical students' records. We then project the proposed approach to the use case of university campuses and boarding schools.
Clustering Enhancement Using Particle Swarm Optimization Algorithm

Clustering aims to find useful groups of objects (clusters), based on the closest distance to clusters centers. Soft Subspace Clustering algorithms are generally formulated as an optimization problem that minimizes an objective function, which makes a great improvement in classification. However, each time several iterations to determine the best cluster center are required. In addition, objective function optimization does not always find the global minimum but some local ones. In this paper, We have introduced the algorithm of Particle Swarm Optimization (PSO), which is combined with two subspace clustering algorithms to enhance the clusters centers positions. the proposed classification method has been tested on a synthetic and aerial data set. Two metrics have been used to compare and validate the final results: Normalized Mutual Information (NMI) and classification accuracy. The clustering results obtained are good and improved.
Phishing Attacks Detection Over Machine Learning Algorithms

Cybercrimes have risen steadily since the Covid-19 epidemic. Although efforts are being made to detect cyberattacks, more scientific research is still needed as cybercriminals are constantly innovating and becoming more sophisticated. Phishing is still the most common attack and a major threat to both individuals and companies. This paper aims to select the appropriate machine learning algorithm to build an accurate model capable of detecting phishing attacks. After running several algorithms belonging to different ML categories, the obtained results show that Random Forest was the best algorithm with a high accuracy value of 98.36%.
New Distributed Partitioning Approach for Large Spatial Networks

In urban areas, geographic spread of spatial networks continually produces massive data. In fact, the analysis of these spatial networks is squarely related to the partitioning of networks into sub-networks in a balanced manner using scalable analysis methods with the aim of minimizing processing time, namely, partitioning parallel and distributed with (Big Data, ML). Our objective, consist to partition in a distributed way a large spatial network into several Subnets. Therefore, we are conducting a comparative study of distributed partitioning algorithms such as Clustering and Hierarchical Clustering methods with the method that we have proposed in another paper [26] allowing to decompose the spatial network into sub-networks taking into account the Euclidean proximity between the nodes and the different Voronoï generators. the comparative study of the three partitioning methods that we have implemented, namely, Clustering, Hierarchical Clustering and our proposed method, have allowed us to deduce that our method is the most reliable and competitive compared to others, in terms of balance partitions as well as in terms of optimizing the partitioning time.
Pumping Lemmas Could be 'Harmful'

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Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks

Abstract Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN), but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target’s context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.
Measuring Hate: Does a Definition Affect Self-Reported Levels of Perpetration and Exposure to Online Hate in Surveys?

The purpose of this research is to test the validity of commonly used measures of exposure to and production of online extremism. Specifically, we investigate if a definition of hate influences survey responses about the production of and exposure to online hate. To explore the effects of a definition, we used a split experimental design on a sample of 18 to 25-year-old Americans where half of the respondents were exposed to the European Union’s definition of hate speech and the other half were not. Then, all respondents completed a survey with commonly used items measuring exposure to and perpetration of online hate. The results reveal that providing a definition affects self-reported levels of exposure and perpetration, but the effects are dependent on race. The findings provide evidence that survey responses about online hate may be conditioned by social desirability and framing biases. The findings that group differences exist in how questions about hate are interpreted when definitions of it are not provided mean we must be careful when using measures that try to capture exposure to and the production of hate. While more research is needed, we recommend providing a clear, unambiguous definition when using surveys to measure online hate.
Functional diffusion maps

Abstract Nowadays many real-world datasets can be considered as functional, in the sense that the processes which generate them are continuous. A fundamental property of this type of data is that in theory they belong to an infinite-dimensional space. Although in practice we usually receive finite observations, they are still high-dimensional and hence dimensionality reduction methods are crucial. In this vein, the main state-of-the-art method for functional data analysis is Functional PCA. Nevertheless, this classic technique assumes that the data lie in a linear manifold, and hence it could have problems when this hypothesis is not fulfilled. In this research, attention has been placed on a non-linear manifold learning method: Diffusion Maps. The article explains how to extend this multivariate method to functional data and compares its behavior against Functional PCA over different simulated and real examples.
Seismic Soundoff: How to chart a successful career in 2023

Kurt Marfurt reflects on his career and the recent award of SEG's highest honor, the Maurice Ewing Medal. He shares key takeaways from various career stops along his path and how volunteer roles at SEG advanced his career. He also provides wisdom on what has and has not changed in building a successful geophysical career. It is a privilege to hear from geophysicists at the top of their profession, and Marfurt provides actionable advice with a fun and insightful look back on his long career.
Nature-Inspired Information Retrieval Systems: A Systematic Review of Literature and Techniques

Information Retrieval (IR) is the field of Data Mining, growing recently and gained the attention of many researchers due to increasing text data and it is very difficult to handle this unstructured data by computers also. The technique for mining meaningful data or information from phrases, texts, or documents. A few years ago, many Data Mining techniques are proposed to solve the problem of document clustering and Information Retrievals most of these techniques are fail to achieve precision and effectiveness for information retrievals. Some of the techniques are widely used but some are least used for their performance over time complexity. All the new nature-inspired algorithms that are used to replace traditional data classification, clustering, and text mining algorithms for information retrieval are investigate here. This will give new feature direction in information retrievals and document clustering which is the field of data mining research. This paper presents a systematic review of literature for various techniques and tools which find an alternative for IR techniques for knowledge discovery. Additionally, focus on various hybrid classification and document clustering algorithms used for information retrievals and pattern discovery also presenting a bibliographic survey of various hybrid algorithms in the field of data mining used for information retrieval.
Decentralized Over-the-Air Federated Learning by Second-Order Optimization Method

Federated learning (FL) is an emerging technique that enables privacy-preserving distributed learning. Most related works focus on centralized FL, which leverages the coordination of a parameter server to implement local model aggregation. However, this scheme heavily relies on the parameter server, which could cause scalability, communication, and reliability issues. To tackle these problems, decentralized FL, where information is shared through gossip, starts to attract attention. Nevertheless, current research mainly relies on first-order optimization methods that have a relatively slow convergence rate, which leads to excessive communication rounds in wireless networks. To design communication-efficient decentralized FL, we propose a novel over-the-air decentralized second-order federated algorithm. Benefiting from the fast convergence rate of the second-order method, total communication rounds are significantly reduced. Meanwhile, owing to the low-latency model aggregation enabled by over-the-air computation, the communication overheads in each round can also be greatly decreased. The convergence behavior of our approach is then analyzed. The result reveals an error term, which involves a cumulative noise effect, in each iteration. To mitigate the impact of this error term, we conduct system optimization from the perspective of the accumulative term and the individual term, respectively. Numerical experiments demonstrate the superiority of our proposed approach and the effectiveness of system optimization.
Towards Hierarchical Intent Disentanglement for Bundle Recommendation

Bundle recommendation aims to recommend a bundle of items for the user to purchase together, for which two scenarios ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> Next-bundle recommendation and Within-bundle recommendation) are explored to recommend a specific bundle of items for the user and a specific item to fill the user's current bundle, respectively. Previous works largely model the user's preference with a uniform intent, without considering the diversity of intents when adopting the items within the bundle. In the real scenario of bundle recommendation, user intents modeling actually needs to be considered from three hierarchical levels, for that: a user's intents may be naturally distributed in different bundles (user level), one bundle may contain multiple intents of a user (bundle level), and an item in different bundles may also present different user intents (item level). To this end, we develop a novel model, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u> ierarchical <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> ntent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> isentangle <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> raph <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etworks (HIDGN) for bundle recommendation. HIDGN is capable of capturing the diversity of the user's intent precisely and comprehensively from the hierarchical structure with an cross-task intent contrastive learning, which is unified with the supervised next-/within-bundle recommendation sub-tasks as a multi-task framework. Extensive experiments on three benchmark datasets demonstrate that HIDGN outperforms the state-of-the-art methods by 43.0%, 13.2%, and 73.3%, respectively.
LUNA: Efficient Backward-Private Dynamic Symmetric Searchable Encryption Scheme With Secure Deletion in Encrypted Database

Dynamic symmetric searchable encryption (SSE) enables clients to perform searches and updates on an encrypted database outsourced to an untrusted server while preserving the privacy of data and queries. For restricting information leakage, it is very important to limit what the server can learn about the deleted data during searches after the deletion, i.e., to satisfy backward privacy. However, previous backward privacy definitions only considered the logical deletion of keywords in documents while ignoring security risks caused by the actual deletion of documents. Moreover, existing SSE schemes often depend on heavy cryptographic primitives for achieving high-level backward privacy, which greatly degrades the end-to-end performance. To this end, we define a new backward privacy notion named BP-DEL, which restricts the information leakage of the actual deletion. Moreover, we design a hybrid index structure that provides BP-DEL for SSE schemes such that they support deletions securely. Based on the hybrid index, we propose a BP-DEL construction named LUNA and design its protocols with a trusted execution environment (TEE) to maintain the index efficiently. Finally, we implement LUNA in the MySQL database by encapsulating it in UDFs. The experimental results show that LUNA has a performance much better than previous works satisfying BP-DEL.
Optimal Convergence for Agnostic Kernel Learning With Random Features

Owing to their solid theoretical guarantees and flexible learning framework, random features (RFs) methods have drawn increasing attention in the field of nonparametric statistical learning. However, existing studies on RFs assume that the target function lies exactly in the associated kernel space, which may not hold true in practical applications. In this article, we investigate the effectiveness of RFs in an agnostic setting that the target regression may be out of the kernel space and prove that they can still achieve capacity-dependent statistical optimality. To achieve this, we provide a finer grained estimate for the capacity of the hypothesis space, and conduct a refined analysis of error terms after a concise error decomposition. Our results show that RF with uniform sampling can guarantee optimality in half of the agnostic situations, while RF with data-dependent sampling can achieve optimal rates in the entire agnostic setting. This finding suggests that using data-dependent sampling not only reduces the number of RFs but also improves their applicability in agnostic settings. Finally, we compare the performance of RFs with different sampling strategies on several real-world datasets. The experimental results provide supports for our theoretical findings.
KalmanAE: Deep Embedding Optimized Kalman Filter for Time Series Anomaly Detection

The Kalman filter performs well in system state estimation by inferring a joint probability distribution over time variables, which has numerous technological applications in time series analysis. However, the complex Kalman filter parameter settings prevent it from optimally estimating the system state, and this suboptimal estimation makes it difficult to effectively distinguish between normal and anomalous system states in anomaly detection. In this paper, we propose a deep embedding-optimized Kalman filter for unsupervised time series anomaly detection, where the system state of a normal time series can be fitted by the embedding-optimized Kalman filter in an unsupervised manner and anomalies can be detected from data points that deviate from the normal system state. Specifically, we use an autoencoder-enhanced Kalman filter to capture the normal pattern of the time series, where the original time series signal is first fed into the Kalman filter, then the autoencoder encodes the filtered signal and reconstructs the original signal, and the learned embedding from encoder is used to sequentially optimize the Kalman filter. Finally, the optimized filter captures the normal pattern of the time series and the reconstruction error from the filtered signal can be measured to detect anomalies. The validity of the method is verified on real-world time series datasets.
Continuous Message Transmission for Covert Communication

In this letter, we investigate continuous message transmission schemes for covert communication under the long-term detection of an adversary. The amount of information is employed to evaluate the performance of communication and analyzed in two cases. For the first case where the number of messages is predetermined, we derive that equal-power allocation is the optimal scheme to maximize the amount of information. For the second case where the number of messages is not predetermined, we propose a power-series based power allocation scheme to satisfy the covert constraint. Moreover, the amount of information in second case is analyzed and shown to be arbitrarily close to that in first case.