Reviewerly/exHarmony huggingface dataset:
Teaming behavior in adversarial scenarios

Interesting and effective team behaviors arise when a group of agents contend with adversaries. Examples range from animal group behaviors observed in nature to strategies used in team sports. This mini review goes over literature in multiagent systems that study group control in adversarial scenarios. We identify different ways of formulating adversaries and discuss various types of teaming behavior that arise. Specifically from the perspective of multiagent task assignment, the types of tasks and the nature of assignments brought by the adversary are categorized. The frontiers of the current literature and the direction for future research are discussed at the end.
Note al testo e alla traduzione

The aim of this article is to offer some useful notes about the two collections of sonnets here translated, together with some commentaries concerning the translation work undertaken.
Quantum dots interacting with a non-classical field under Kerr-phase modulation as a resource for repeatable quantum algorithms

Abstract An analytical solution describing the dynamics of a hybrid system comprising an interacting quantum dot and a quantum electromagnetic field in the presence of Kerr nonlinearity is obtained. A completely new regime of interaction is found. It provides stable periodically repeating dynamics for the total bipartite system and for the quantum dot excitation, accompanied by full and pure revivals. This is even observed when the initial field state is in a squeezed vacuum. Periodical strong entanglement and full disentanglement between the interacting subsystems are demonstrated. The obtained regime forms the basis for the development of quantum information algorithms and photon–matter interfaces involving these hybrid systems. The formation of non-Gaussian field states is revealed. Methods to control and manage the specific features of the found pure and mixed states of the considered subsystems are developed.
Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

There is a lack of high correlation and reuse potential among multiple manufacturing data for textiles and apparel. Moreover, the material flow traceability between production workstations is not clear, making it difficult to detect potential production bottlenecks. This paper proposes a knowledge graph embedded time serial data-driven bottleneck analysis of textile and apparel production processes. Firstly, a dynamic information association model is established to organize global manufacturing information, including the static data and time-series data features. Also, a textile-corpus-oriented knowledge extraction model is designed to construct a time-series knowledge graph for textile and apparel production (TKG4TA). Then, a temporal knowledge-driven production process bottleneck prediction model is presented based on manufacturing knowledge in the textile and apparel industry. Of these, textile knowledge is transformed into embeddings using a graph convolutional network (GCN). In turn, the context-associated information features are learned by the long short-term memory (LSTM) to predict the bottlenecks in the textile and apparel production process. Finally, a typical process flow in a shirt manufacturing workshop is used as a case study. It shows that the F1 value of the proposed method for named entity recognition and relationship extraction is up to 80.3%, and 50.6%, respectively. The performance of the proposed model for bottleneck prediction is improved by 8.2% and 14.92% compared to only the use of GCN or LSTM in the mean absolute error. This model may provide a solid foundation for the temporal knowledge-graph-driven bottleneck analysis of shirt manufacturing.
Universal and complementary representation learning for automatic modulation recognition

Abstract Automatic modulation recognition (AMR) is a fundamental research topic in the field of signal processing and wireless communication, which has widespread applications in cognitive radio, non‐collaborative communication etc. In this paper, the focus is on the multi‐modal utilization in AMR. Specifically, the universal and complementary characteristics of multiple modality data in the domain‐agnostic and domain‐specific aspects are mined, yielding the universal and complementary subspaces network accordingly (dubbed as UCNet). To facilitate the subspace construction, universal and complementary losses are proposed accordingly. The proposed UCNet has achieved the highest recognition accuracy of 93.2% at 10 dB on the RadioML2016.10A dataset, and the average accuracy is 92.6% at high SNR greater than zero.
Families of multi-level Legendre-like arrays

Abstract Families of new, multi-level integer 2 D arrays are introduced here as an extension of the well-known binary Legendre sequences that are derived from quadratic residues. We present a construction, based on Fourier and Finite Radon Transforms, for families of periodic perfect arrays, each of size $$p\times p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>×</mml:mo> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> for many prime values p . Previously delta functions were used as the discrete projections which, when back-projected, build 2 D perfect arrays. Here we employ perfect sequences as the discrete projected views. The base family size is $$p+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . All members of these multi-level array families have perfect autocorrelation and constant, minimal cross-correlation. Proofs are given for four useful and general properties of these new arrays. 1) They are comprised of odd integers, with values between at most $$-p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>-</mml:mo> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> and $$+p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>+</mml:mo> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> , with a zero value at just one location. 2) They have the property of ‘conjugate’ spatial symmetry, where the value at location ( i , j ) is always the negative of the value at location $$(p-i, p-j)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>p</mml:mi> <mml:mo>-</mml:mo> <mml:mi>i</mml:mi> <mml:mo>,</mml:mo> <mml:mi>p</mml:mi> <mml:mo>-</mml:mo> <mml:mi>j</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . 3) Any change in the value assigned to the array’s origin leaves all of its off-peak autocorrelation values unchanged. 4) A family of $$p+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> , $$p\times p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>×</mml:mo> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> arrays can be compressed to size $$(p+1)^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>p</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:math> and each family member can be exactly and rapidly unpacked in a single $$p\times p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>×</mml:mo> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> decompression pass.
Crossover in Cartesian Genetic Programming: Evaluation of Two Phenotypic Methods

The tree-based representation model of Genetic Programming is commonly used with subtree crossover as the predominant genetic operator. In contrast, Cartesian Genetic Programming (CGP) is widely used merely with mutation for genetic variation. Compared to comprehensive and advanced knowledge about crossover in the field of tree-based Genetic Programming, the state of knowledge in CGP appears to be generally weak. Even if CGP was officially introduced over twenty years ago, the role of recombination in CGP has been recently considered to be still an open question. Promising steps have been taken in the last years, but the outcomes show that more studies are needed to evaluate crossover operators in CGP on a wider set of benchmark problems. In this work, we compare algorithms that utilize two phenotypic crossover operators for CGP, called subgraph and block crossover, to the traditional mutation-only approach. The results of our experiments on well-known symbolic regression, Boolean function, and images operator design problems demonstrate that the use of crossover-based algorithms can outperform the mutation-only CGP approach on well-known benchmark problems.
Automated feedback for participants of hands-on cybersecurity training

Abstract Computer-supported learning technologies are essential for conducting hands-on cybersecurity training. These technologies create environments that emulate a realistic IT infrastructure for the training. Within the environment, training participants use various software tools to perform offensive or defensive actions. Usage of these tools generates data that can be employed to support learning. This paper investigates innovative methods for leveraging the trainee data to provide automated feedback about the performed actions. We proposed and implemented feedback software with four modules that are based on analyzing command-line data captured during the training. The modules feature progress graphs, conformance analysis, activity timeline, and error analysis. Then, we performed field studies with 58 trainees who completed cybersecurity training, used the feedback modules, and rated them in a survey. Quantitative evaluation of responses from 45 trainees showed that the feedback is valuable and supports the training process, even though some features are not fine-tuned yet. The graph visualizations were perceived as the most understandable and useful. Qualitative evaluation of trainees’ comments revealed specific aspects of feedback that can be improved. We publish the software as an open-source component of the KYPO Cyber Range Platform. Moreover, the principles of the automated feedback generalize to different learning contexts, such as operating systems, networking, databases, and other areas of computing. Our results contribute to applied research, the development of learning technologies, and the current teaching practice.
Evaluation of Gated Recurrent Neural Networks for Embedded Systems Applications

Artificial Neural Networks (ANNs), based on the concept of neuron cells, are widely used nowadays for multiple applications. Recurrent Neural Networks (RNNs) are special types of ANNs capable of capturing temporal relationship into a set of data and offer interesting features. However, RNN architectures are complex and their integration on embedded systems implies a proper understanding of inherent structures and algorithms. This paper describes and analyses four types of gated RNN cells: Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Minimal Gated Unit (MGU) and STAckable Recurrent cell (STAR). We explain the algorithmic details and how these cells can be combined and integrated to build complex structures. We illustrate our approach with experimental results using different architectures and implementations on dedicated test cases.
Positional Encoding-based Resident Identification in Multi-resident Smart Homes

We propose a novel resident identification framework to identify residents in a multi-occupant smart environment. The proposed framework employs a feature extraction model based on the concepts of positional encoding. The feature extraction model considers the locations of homes as a graph. We design a novel algorithm to build such graphs from layout maps of smart environments. The Node2Vec algorithm is used to transform the graph into high-dimensional node embeddings. A Long Short-Term Memory model is introduced to predict the identities of residents using temporal sequences of sensor events with the node embeddings. Extensive experiments show that our proposed scheme effectively identifies residents in a multi-occupant environment. Evaluation results on two real-world datasets demonstrate that our proposed approach achieves 94.5% and 87.9% accuracy, respectively.
Requirement elicitation model for global software development vendors

Abstract Requirements elicitation is critical phase of software development that involve direct communication of clients and vendors. In global software development (GSD), importance of requirements elicitation becomes more critical due to geographical distance. The aim of this research is to design and implement requirement elicitation model (REM) where GSD organization can evaluate their organizations and can classify their level of implementation where organization lies. Systematic literature review (SLR) was conducted for the identification of critical success factors (CSFs) from a sample of 70 research articles. Furthermore, the identified CSFs are organized into five levels based on Capability Maturity Model Integration (CMMI) and Software Outsourcing Vendors' Readiness Model (SOVRM). A second SLR was conducted for identifying all possible practices for the implementation of CSFs. To validate the findings of SLR and to find more practices in the view of industrial practitioners for the identified CSFs, a questionnaire survey was conducted in the GSD organizations in which 35 experts, from different countries, contributed. For REM evaluation, we have conducted two case studies. The outcome analysis after conducting case studies confirms that our assessment framework is easier to understand, apply, and successfully critic the strengths and weaknesses of organizations during requirements elicitation in GSD.
DeepMark: A Scalable and Robust Framework for DeepFake Video Detection

With the rapid growth of DeepFake video techniques, it becomes increasingly challenging to identify them visually, posing a huge threat to our society. Unfortunately, existing detection schemes are limited to exploiting the artifacts left by DeepFake manipulations, so they struggle to keep pace with the ever-improving DeepFake models. In this work, we propose DeepMark, a scalable and robust framework for detecting DeepFakes. It imprints essential visual features of a video into DeepMark Meta (DMM), and uses it to detect DeepFake manipulations by comparing the extracted visual features with the ground truth in DMM. Therefore, DeepMark is future-proof because a DeepFake video must aim to alter some visual feature, no matter how “natural” it looks. Furthermore, DMM also contains a signature for verifying the integrity of the above features. And an essential link to the features as well as their signature is attached with error correction codes and embedded in the video watermark. To improve the efficiency of DMM creation, we also present a threshold-based feature selection scheme and a deduced face detection scheme. Experimental results demonstrate the effectiveness and efficiency of DeepMark on DeepFake video detection under various datasets and parameter settings.
Evolving in the Shadows: A Media Ecology Study of Dark Web Social Networks

This study conceptualizes dark web social networks (DWSNs) through the lens of media ecology theory. We synthesize existing literature to problematize a lack of understanding of DWSNs as a communicatively organizing system. The discussion then focuses on how DWSNs complement, compete, and hybridize with surface web social networks (SWSNs). This Interaction shapes DWSNs as communities of practice that both serve and evolve with the communicative and informational needs of their users. We introduce and elaborate two media-ecological concepts of DWSNs: (1) a medium that has become a message of antithesis to Web 2.0 and (2) an organism that has coevolved with SWSNs. An empirical indicator to explicate these two concepts is The Hub, one of the long-lasting DWSNs. The Hub serves as an example to juxtapose DWSNs with SWSNs, with a focus on their intermedia relationship, and characterize the symbiosis between DWSNs as hosts and their users as living organisms.
Topic Modeling based Text Classification Regarding Islamophobia using Word Embedding and Transformers Techniques

Islamophobia is a rising area of concern in the current era where Muslims face discrimination and receive negative perspectives towards their religion, Islam. Islamophobia is a type of racism that is being practiced by individuals, groups, and organizations worldwide. Moreover, the ease of access to social media platforms and their augmented usage has also contributed to spreading hate speech, false information, and negative opinions about Islam. In this research study, we focused to detect Islamophobic textual content shared on various social media platforms. We explored the state-of-the-art techniques being followed in text data mining and Natural Language Processing (NLP). Topic modelling algorithm Latent Dirichlet Allocation is used to find top topics. Then, word embedding approaches such as Word2Vec and Global Vectors for word representation (GloVe) are used as feature extraction techniques. For text classification, we utilized modern text analysis techniques of transformers-based Deep Learning algorithms named Bidirectional Encoders Representation from Transformers (BERT) and Generative Pre-Trained Transformer (GPT). For results comparison, we conducted an extensive empirical analysis of Machine Learning algorithms and Deep Learning using conventional textual features such as the Term Frequency-Inverse Document Frequency, N-gram, and Bag of words (BoW). The empirical based results evaluated using standard performance evaluation measures show that the proposed approach effectively detects the textual content related to Islamophobia. In the corpus of the study under Machine Learning models Support Vector Machine (SVM) performed best with an F1 score of 91%. The Transformer based core NLP models and the Deep Learning model Convolutional Neural Network (CNN) when combined with GloVe performed best among all the techniques except SVM with BoW. GPT, SVM when combined with BoW and BERT yielded the best F1 score of 92%, 92% and 91.9% respectively, while CNN performed slightly poor with an F1 score of 91%.
Cross-lingual Sentiment Analysis of Tamil Language Using a Multi-stage Deep Learning Architecture

In recent years, sentiment analysis has become a focal point in natural language processing. Cross-lingual sentiment analysis is a particularly demanding yet essential task that seeks to construct models capable of effectively analyzing sentiments across a variety of languages. The primary motivation behind this research is to bridge the gap in current techniques that often struggle to perform well with low-resource languages, due to the scarcity of large, annotated datasets, and their unique linguistic characteristics. In light of these challenges, we propose a novel Multi-Stage Deep Learning Architecture (MSDLA) for cross-lingual sentiment analysis of the Tamil language, a low-resource language. Our approach utilizes transfer learning from a source language with abundant resources to overcome data limitations. Our proposed model significantly outperforms existing methods on the Tamil Movie Review dataset, achieving an accuracy, precision, recall, and F1-score of 0.8772, 0.8614, 0.8825, and 0.8718, respectively. ANOVA statistical comparison demonstrates that the MSDLA’s improvements over other models, including mT5, XLM, mBERT, ULMFiT, BiLSTM, LSTM with Attention, and ALBERT with Hugging Face English Embedding are significant, with p-values all less than 0.005. Ablation studies confirm the importance of both cross-lingual semantic attention and domain adaptation in our architecture. Without these components, the model’s performance drops to 0.8342 and 0.8043 in accuracy, respectively. Furthermore, MSDLA demonstrates robust cross-domain performance on the Tamil News Classification and Thirukkural datasets, achieving an accuracy of 0.8551 and 0.8624, respectively, significantly outperforming the baseline models. These findings illustrate the robustness and efficacy of our approach, making a significant contribution to cross-lingual sentiment analysis techniques, especially for low-resource languages.
Blockchain-Powered Education: A Sustainable Approach for Secured and Connected University Systems

The collection and examination of student data, encompassing academic achievements, awards, and certifications, assume an essential function within the field of education as a means of showing students’ capabilities. Nevertheless, it is crucial to note that regular paper-based records are vulnerable to both physical destruction and the act of fabrication, while standard databases can have security holes. Moreover, the process of manually gathering physical papers from centralized organizations is both laborious and complicated. To address the concerns above and foster sustainability in the field of education, this study first suggests using Scorechain. This innovative solution integrates blockchain technology into a comprehensive data-management system for managing all student-related data. Secondly, by utilizing the inherent security features of blockchain technology, Scorechain develops a stable multi-role hierarchy, increasing the integrity and reliability of data. This also facilitates the efficient transfer of information among various stakeholders, including parents, recruiters, and educational institutions, thus fostering transparency and accountability. Lastly, the Scorechain system facilitates collaboration and data exchange among universities inside a shared network. Scorechain was constructed using the Rust programming language and is based on the Substrate blockchain architecture. It underwent careful development, testing, and analysis to ensure operational efficiency. The feasibility and long-term viability of Scorechain in genuine educational contexts are highlighted as blockchain technology facilitates seamless integration into the education sector.
Possible Influence of Brittle Tectonics on the Main Road Network Built in the Central African Environment Using Remote Sensing and GIS

The construction of sustainable road and highway networks in the world, despite numerous feasibility, pre-feasibility and execution studies, are always confronted with the demands and vagaries of foreseeable and unforeseeable natural disasters. Studying cyclones, earthquakes, fracturing and landslide zones along roads is therefore a challenge for the sustainability of these infrastructures. In many countries around the world, the methods generally used for these studies are not only expensive and time-consuming, but also the results obtained are not always efficient. This work examines whether Landsat 8 (with a high cloud level) and SRTM data can be used in both equatorial and coastal Central Africa zones to produce relevant mapping, locating fracture and landslide zones, in order to contribute not only to a better road layout at lower cost and in a relatively short time, but also to a better prevention of geological disasters that may occur on its network. To this end, a map of the main road network was produced and validated with field data, as well as the maps of the main unstable slopes, faults and fractures zones intersecting the road or highway network. These approaches are useful for sustainable planning, management, monitoring and extension of roads worldwide, especially in Central Africa.