Reviewerly/exHarmony huggingface dataset:
Robust Kalman Predictors With Asymptotic Equivalence and Absolute Asymptotic Stability for Networked Uncertain System With Hard and Soft Sensors

For networked uncertain systems (NUSs), an improved unified sensor model with “hard” and “soft” sensors including five uncertainties is presented, where two-step random measurement delays and colored measurement noise are considered. The minimax robust time-varying and steady-state Kalman predictors are presented in the sense that their actual prediction error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. New concepts of their asymptotic equivalence and absolute asymptotic stability are presented and proved by the dynamic error system analysis (DESA) method and the dynamic variance error system analysis (DVESA) method. They constitute a novel robust Kalman filtering and asymptotic stability theory for NUSs, and overcome the limitation of the classical Kalman filtering. One simulation example applied to the vehicle suspension system verifies the effectiveness of the proposed theory.
Quantum roughness as sensitivity to diffusion noise: Quantifying non-classicality of multi-mode systems

The quantumness of a physical system is context-sensitive, contingent on the particular quantum behavior selected for quantification. One such measure, roughness, recently introduced to assess nonclassicality, gauges the waviness of the Wigner function of a quantum state. This work explores some properties of roughness, generalizes its definition to include multimode systems, and presents a new geometric interpretation of this measure of nonclassicality. Furthermore, the roughness of several families of multimode states is reported, shedding light on their nonclassical features.
Exploiting Reward Machines with Deep Reinforcement Learning in Continuous Action Domains

In this paper, we address the challenges of non-Markovian rewards and learning efficiency in deep reinforcement learning (DRL) in continuous action domains by exploiting reward machines (RMs) and counterfactual experiences for reward machines (CRM). RM and CRM were proposed by Toro Icarte et al. A reward machine can decompose a task, convey its high-level structure to an agent, and support certain non-Markovian task specifications. In this paper, we integrate state-of-the-art DRL algorithms with RMs to enhance learning efficiency. Our experimental results demonstrate that Soft Actor-Critic with counterfactual experiences for RMs (SAC-CRM) facilitates faster learning of better policies, while Deep Deterministic Policy Gradient with counterfactual experiences for RMs (DDPG-CRM) is slower, achieves lower rewards, but is more stable. Option-based Hierarchical Reinforcement Learning for reward machines (HRM) and Twin Delayed Deep Deterministic (TD3) with CRM generally underperform compared to SAC-CRM and DDPG-CRM. This work contributes to the ongoing development of more efficient and robust DRL approaches by leveraging the potential of RMs in practical problem-solving scenarios.
Election Manipulation on Social Networks with Abstention

The Election Manipulation through the diffusion of (fake) news on social networks has been a subject that recently attracted the interest of many works in both in the communities of AI and social choice theory. However, all these works assume that each voter has to express her vote, not considering the possibility that she could abstain. One of the reasons of this omission is the lack of a satisfying modeling of how people choose to abstain. In this work, we try to fill this gap by presenting an innovative model for abstention that will match most of the real-world observations about the topic. Next, we will provide experimental evidence that abstention opportunity will help the manipulator to control the elections, by comparing how well-known algorithms and heuristics behave in the setting with and without abstention.
Improving Bitswap Privacy with Forwarding and Source Obfuscation

IPFS is a content-addressed decentralized peer-to-peer data network, using the Bitswap protocol for exchanging data. The data exchange leaks the information to all neighbors, compromising a user’s privacy. This paper investigates the suitability of forwarding with source obfuscation techniques for improving the privacy of the Bitswap protocol. The usage of forwarding can add plausible deniability and the source obfuscation provides additional protection against passive observers. First results showed that through trickle-spreading the source prediction could decrease to 40%, at the cost of an increased content fetching time. However, assuming short distances between content provider and consumer the content fetching time can be faster even with the additional source obfuscation.
SyNIG: Synthetic Network Traffic Generation through Time Series Imaging

Immense growth of network usage and the associated proliferation of network, traffic, traffic classes, and diverse QoS requirements pose numerous challenges for network operators. Though data-driven approaches can provide better solutions for these challenges, limited data has been a barrier to developing those methods with high resiliency. In this work, we propose SyNIG (Synthetic Network Traffic Generation through Time Series Imaging), which utilizes Generative Adversarial Networks (GANs) for network traffic synthesis by converting time series data to a specific image format called GASF (Gramian Angular Summation Field). With GASF images we encode correlation between samples in 1D signals on a single 2D pixel map. Taking three types of network traffic; video streaming, accessing websites and IoT, we synthesize over 200,000 traces using over 40,000 original traces generalizing our method for different network traffic. We validate our method by demonstrating the fidelity of the synthetic data and applying them to several network related use cases showing improved performance.
Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications

This paper develops an optimal data aggregation policy for learning-based traffic control systems based on imagery collected from Road Side Units (RSUs) under imperfect communications. Our focus is optimizing semantic information flow from RSUs to a nearby edge server or cloud-based processing units by maximizing data diversity based on the target machine learning application while taking into account heterogeneous channel conditions and constrained total transmission rate. To this end, we enforce fairness among class labels to increase data diversity for classification problems. Furthermore, we propose a greedy interval-by-interval scheduling policy powered by coalition game theory to reduce the computation complexity. Once, RSUs are selected, we employ a maximum uncertainty method to handpick data samples that contribute the most to the learning performance. Our method yields higher learning accuracy compared to random selection, uniform selection, and network-based optimization methods (e.g., FedCS) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Cyberbullying Messages Detection: A Comparative Study of Machine Learning Algorithms

Cyberbullying is a growing concern, with serious consequences especially for children. In this paper, we propose a machine learning approach to detect cyberbullying messages accurately and distinguish them from regular ones. We used a public dataset of social media messages to fit a binary classification of either cyberbullying or non-bullying messages. We compared 15 classifiers using two methods: Term Frequency-Inverse Document Frequency (TF-IDF) for traditional algorithms and word embedding for deep learning algorithms. The voting classifier, a combination of the best algorithms from the first method, achieved the highest accuracy of 96.5% during tests. This approach can be used in social media or chat applications to detect and prevent cyberbullying.
DITN: User’s indirect side-information involved domain-invariant feature transfer network for cross-domain recommendation

The cross-domain recommendation aims to make more accurate personalized recommendations by transferring user preferences from a relatively rich interactive information domain to a sparse data domain. Traditional methods mostly only use direct side-information to improve the generalization of user and item representations which is usually limited or introduce indirect side-information without fully considering the problems of noise reduction and balanced fusion. For these two considerations, we propose a User's Indirect Side-information Involved Domain-invariant Feature Transfer Network for Cross-Domain Recommendation (DITN) to mutually suppress and balanced fuse direct and indirect side-information. To effectively suppress the noise caused by adding side-information, we define a group of global property representations to guide the user's feature extraction of direct and indirect side-information and adopt the co-attention mechanism to balanced fuse two kinds of side-information features, which improves the quality of the user's preferences learning. Then we propose a novel user's feature disentangle module based on the VAE framework to extract the invariant and the specific features between domains and enhance feature disentangling by utilizing mutual information to reduce the negative transfer problem. We have conducted sufficient experiments on 6 domains and 3 scenarios which include 2,408,007 interactive information from the Amazon dataset to demonstrate the significant performance of our proposed model. The results show that DITN effectively enhances the rating prediction accuracy and outperforms state-of-the-art recommendation methods.
KisanQRS: A deep learning-based automated query-response system for agricultural decision-making

Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers’ helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (KisanQRS), a Deep Learning-based robust query-response framework for the agriculture sector. KisanQRS integrates semantic and lexical similarities of farmers’ queries and employs a rapid threshold-based clustering method. Before clustering, KisanQRS employs data cleaning by performing steps such as the removal of special characters and extra white spaces. It also removes crop names from the queries, which helps in putting similar queries to the same clusters irrespective of the crop. Subsequently, semantic and lexical similarities of queries are computed, and a final similarity matrix is generated based on the weighted sum of these two similarities. Next, a clustering algorithm is applied to organize the queries into clusters according to their similarity. A query mapping module is trained on these cluster labels as the target. For this query mapping module, LSTM is found to be the optimal method. Next, the answer retrieval method clusters candidate answers for a crop, ranks these answer clusters based on the number of answers in a cluster, and selects the leader of each cluster, finally providing the top-K answers. The dataset used in our analysis consists of a subset of 34 million call logs from the Kisan Call Centre (KCC), operated under the Government of India. We evaluated the performance of the query mapping module on the data of five major states of India with 3,00,000 samples and the quantifiable outcomes demonstrate that KisanQRS significantly outperforms traditional techniques by achieving 96.58% top F1-score for a state. The answer retrieval module is evaluated on 10,000 samples and achieves a competitive NDCG score of 96.20%. KisanQRS could be useful in enabling farmers to make informed decisions about their farming practices by providing quick and pertinent responses to their queries.
Advances in Optical Metrology: The Impact of Deep Learning and Quantum Photonics

There have been numerous advances in the fields of engineering, medicine, environment and so forth that involve measurements as a critical part of research and applications. Especially, when it comes to reaching efficient and precision measurements - what comes in handy are optical sensors that provide results beyond just size or location. In recent years, research have primarily relied on providing solutions by using deep learning and novel training algorithms for efficiently determining and eliciting results in the fields that optical metrology is applied. However, there is also analysed optical quantum metrology that is bringing a new finding to light. This paper will analyse the impact of deep learning in quantum metrology and will provide future suggestions on how to improve the results in terms of classical means by using the technologies of quantum photonics metrology to provide actual examples and applications of both technologies in real life.
Assessment of Digital Programing Skills based on the Competencies Model

There is a growing need to assess programming skills holistically using a competencies model. However, existing assessment tools lack a comprehensive framework mapping programming to wider digital competencies. The study explores the importance of digital skills by focusing initially on assessing the digital skills gap as case study of target students in Kosovo. The study aims to highlight the importance and the need for continuous learning and adaptation to bridge the digital skills gap of student and the potential IT industry employees in the future. The study also emphasizes the need of learning theories in the design of digital education interventions as a critical area that needs further exploration. The research methodology of the study combines quantitative and qualitative data, collected through surveys and interviews. The participants include individuals from various backgrounds, such as students, and high school pupils. The study is completed by the creation of an online assessment platform that aims to measure digital skill levels across DigComp competencies. The competencies model-based assessment of programming skills using an online platform is a novel approach. The empirical study provides new insights into the digital skills gap. The strategy should include the integration of digital skills into the assessment process, the development of unobtrusive assessment methods, and the effective utilization of learning theories in digital education. Future research should focus on these areas to bridge the digital skills gap and enhance the effectiveness of digital education. Analyses of the impacting factors has been provided and discussed.
An FPGA-Based Reconfigurable CNN Training Accelerator Using Decomposable Winograd

Convolutional neural networks (CNNs) have been widely used in computer vision in recent years. However, the huge amount of computation involved in CNN training limits its application on embedded devices. To solve the dilemma, this paper proposes an FPGA-based reconfigurable CNN training accelerator. First, we explore the possibility of using the Winograd algorithm to accelerate convolutions. An input-aligned decomposable Winograd method is proposed that broadens the scope of the application of Winograd and simplifies the implementation of Winograd on a unified processing element. Second, we propose a reconfigurable training architecture consisting of a transposable Winograd processing element array that can perform different training phases with high parallelism under limited resource costs. A series of unified data transformation units are designed to support various Winograd operations. The hierarchical barrel shift networks work for flexible and complex data access without bank conflict. Evaluated on VGG16 and ResNet18, our method reduces multiplications up to $2.4\times$ compared to conventional convolution. Additionally, our accelerator implemented on Alveo U200 achieves up to 918.57 GOPS in terms of throughput and shows a $3.18\times$ improvement in resource efficiency over the prior art.
Solving graph problems with single photons and linear optics

An important challenge for current and near-term quantum devices is finding useful tasks that can be preformed on them. We first show how to efficiently encode a bounded $n\ifmmode\times\else\texttimes\fi{}n$ matrix $A$ into a linear optical circuit with $2n$ modes. We then apply this encoding to the case where $A$ is a matrix containing information about a graph $G$. We show that a photonic quantum processor consisting of single-photon sources, a linear optical circuit encoding $A$, and single-photon detectors can solve a range of graph problems including finding the number of perfect matchings of bipartite graphs, computing permanental polynomials, determining whether two graphs are isomorphic, and the $k$-densest subgraph problem. We also propose preprocessing methods to boost the probabilities of observing the relevant detection events and thus improve performance. Finally we present both numerical simulations and implementations on Quandela's Ascella photonic quantum processor to validate our findings.
Politicization of Economic Cybercrime: Perceptions Among Ghanaian Facebook Users

ABSTRACTCybercrime remains a focal point in global and regional discussions concerning contemporary issues in West Africa. In line with this discourse, this article delves into the intricate web of viewpoints that envelop the issue of cybercrime in Ghana, with emphasis on Ghana social media users' opinion. Our study focuses on analyzing comments related to cybercrime that have been shared by Ghanaian Facebook users. Through thematic analysis, we dissect the primary narratives that revolve around cybercrime in these discussions. What we discovered in our study goes beyond the traditional portrayal of cybercrime as mere unlawful activity. Instead, we have shown a paradoxical sentiment held by Ghanaians who use Facebook. Cybercrime is evolving into a method for marginalized sections of society to push back against Western dominance, internal political hegemonic, and secure financial stabilities. This trend often unfolds within the broader context of challenging governmentalities. Our research challenges the typical focus on cybercrime as primarily a criminal matter. It prompts a reevaluation, urging us to explore cybercrime within the framework of various socioeconomic and political environments. As a result, our study contributes to a more comprehensive understanding of individuals attitude toward cybercrime in Ghana, reshaping the way we approach and discuss it. Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsYushawu AbubakariAbubakari Yushawu, is a PhD researcher at the Institute of Sociology of the University of Wroclaw, Poland. The author is also currently affiliated to the Department of Security Studies and Criminology of the Macquarie University, Australia. He is also a research fellow at the African Center for Cyberlaw and Cybercrime Prevention (ACCP). His research interest includes sociological theories, social inequalities, deviant and crime studies, criminological theories, and qualitative research methods.Mateusz BlaszczykMateusz Błaszczyk, (dr hab.) is an associate professor, Head of the Urban and Rural Sociology Department. His key areas of research interest are urban political economy, urban culture, and local and regional development in Central and Eastern Europe countries.
A buffer overflow detection and defense method based on RISC-V instruction set extension

Abstract Buffer overflow poses a serious threat to the memory security of modern operating systems. It overwrites the contents of other memory areas by breaking through the buffer capacity limit, destroys the system execution environment, and provides implementation space for various system attacks such as program control flow hijacking. That makes it a wide range of harms. A variety of security technologies have been proposed to deal with system security problems including buffer overflow. For example, No eXecute (NX for short) is a memory management technology commonly used in Harvard architecture. It can refuse the execution of code which residing in a specific memory, and can effectively suppress the abnormal impact of buffer overflow on control flow. Therefore, in recent years, it has also been used in the field of system security, deriving a series of solutions based on NX technology, such as ExecShield, DEP, StackGuard, etc. However, these security solutions often rely too much on the processor architecture so that the protection coverage is insufficient and the accuracy is limited. Especially in the emerging system architecture field represented by RISC-V, there is still a lack of effective solutions for buffer overflow vulnerabilities. With the continuous rapid development of the system architecture, it is urgent to develop defense methods that are applicable to different system application environments and oriented to all executable memory spaces to meet the needs of system security development. Therefore, we propose BOP, A new system memory security design method based on RISC-V extended instructions, to build a RISC-V buffer overflow detection and defense system and deal with the buffer overflow threat in RISC-V. According to this method, NX technology can be combined with program control flow analysis, and NX bit mechanism can be used to manage the executability of memory space, so as to achieve a more granular detection and defense of buffer overflow attacks that may occur in RISC-V system environment. In addition, The memory management and control function of BOP is not only very suitable for solving the security problems in the existing single architecture system, but also widely applicable to the combination of multiple heterogeneous systems. Graphical abstract
Cybersecurity research from a management perspective: A systematic literature review and future research agenda

Firms continue to face increasing threats of financial and reputational damage from cybersecurity events. Despite increasing scholarly attention, our 25-year review of studies published in leading academic business journals demonstrates that extant research has generally examined antecedents (e.g., technological defenses), with more limited research focusing on managerial responses to or long-term performance outcomes from these events. Accordingly, we chronicle the current state of knowledge of these threats from a management (rather than from an information systems) perspective. We then highlight important future research opportunities for management scholars interested in developing or testing theory related to critical cybersecurity issues.