Reviewerly/exHarmony huggingface dataset:
An Exploration of Attack Patterns and Protection Approaches Using Penetration Testing

The purpose of penetration testing is to assess the vulnerabilities present in communication networks/Digital Devices. Penetration testing analyses the strength of protection techniques in the digital environment. This test is conducted at periodic intervals to analyze risks and control to accomplish more distinguished security standards. The proposed work discusses factors and components while preparing a penetration test. Various penetration tests are performed on private networks using different tools on the Kali Linux platform. The types of attack considered for this study are credential harvester, web jacking, and smartphone device penetration in secured penetration testing laboratory setup. The tests are performed in detail with various criteria like successful, partially successful, and failure. Recent studies show how organizations suffered because of security incidents. Finally, some mitigation strategies are pointed out to counteract these threats to develop awareness among users.
EmPointMovSeg: Sparse Tensor-Based Moving-Object Segmentation in 3-D LiDAR Point Clouds for Autonomous Driving-Embedded System

Object segmentation is a per-pixel label prediction task that targets at providing context analysis for autonomous driving. Moving-object segmentation (MOS) serves as a subbranch of object segmentation, targeting to separating the surrounding objects into binary options: dynamic and static. MOS is vital for the safety-critical task in autonomous driving because dynamic objects are often a true potential threat to self-driving cars compared to static ones. Current methods typically address the MOS problem as a category feature to label the mapping task, which is not rational in reality. For example, a parking car should be considered as static instead of a moving-object category. There is a little systematic theory to differentiate object moving characteristics from nonmoving characteristics in MOS. Furthermore, restricted by limited resources in the embedded system, MOS is often in an offline manner due to huge computational requirements. An online and low computational cost MOS is an urgent demand for the practical safety-critical mission which takes immediate reaction as compulsory. In this article, we propose EmPointMovSeg, an efficient and practical 3-D LiDAR MOS solution for autonomous driving. Leveraging the power of the well-adapted autoregressive system identification (AR-SI) theory, EmPointMovSeg theoretically explains the moving-object feature in large-scale 3-D LiDAR semantic segmentation. An end-to-end sparse tensor-based CNN which balances segmentation accuracy and online process ability is proposed. We construct our experiment on both representative dataset benchmarks and practical embedded systems. The evaluation result shows the effectiveness and accuracy of our proposed solution, conquering the bottleneck in the online large-scale 3-D LiDAR semantic segmentation.
A Guide for Quantum Web Services Deployment

Quantum computing is a new paradigm for solving problems that classical computers cannot reach. To the point that it is already generating interest in the scientific and industrial communities. Currently, quantum computers and technology are being developed to support the execution of quantum software. Several large computer companies have already built functional quantum computers, and developed several programming languages and quantum simulators that can be used by the general public. All this infrastructure for quantum computing is offered to quantum developers through the cloud, following a model similar to the familiar Infrastructure as a Service. However, due to the early stages of quantum computing taking advantage of the capabilities of these computers requires a very in depth knowledge of quantum programming and quantum hardware that is far from what cloud developers are used to in classical cloud offerings. Although the future of quantum computing is still unknown, it is highly certain that there must be a time when quantum computing coexists with classical computing. At the same time, one of the most well-known and tested solutions for the communication of heterogeneous computing systems are web services. In this tutorial we will offer an introductory view on how quantum algorithms can be converted into web services, how this web services can be deployed, using the Amazon Braket platform for quantum computing, and invoked through classical web services endpoints. Finally, we will propose a way in which a disadvantage of current quantum computers in terms of web services can be transformed into an advantage for web services through the use of a Quantum API Gateway.
Multi-Designated Receiver Signed Public Key Encryption

This paper introduces a new type of public-key encryption scheme, called Multi-Designated Receiver Signed Public Key Encryption ( $$\text {M}{\text {DRS-PKE}}$$ ), which allows a sender to select a set of designated receivers and both encrypt and sign a message that only these receivers will be able to read and authenticate (confidentiality and authenticity). An $$\text {M}{\text {DRS-PKE}}$$ scheme provides several additional security properties which allow for a fundamentally new type of communication not considered before. Namely, it satisfies consistency—a dishonest sender cannot make different receivers receive different messages—off-the-record—a dishonest receiver cannot convince a third party of what message was sent (e.g., by selling their secret key), because dishonest receivers have the ability to forge signatures—and anonymity—parties that are not in the set of designated receivers cannot identify who the sender and designated receivers are. We give a construction of an $$\text {M}{\text {DRS-PKE}}$$ scheme from standard assumptions. At the core of our construction lies yet another new type of public-key encryption scheme, which is of independent interest: Public Key Encryption for Broadcast ( $$\text {PKEBC}$$ ) which provides all the security guarantees of $$\text {M}{\text {DRS-PKE}}$$ schemes, except authenticity. We note that $$\text {M}{\text {DRS-PKE}}$$ schemes give strictly more guarantees than Multi-Designated Verifier Signature ( $$\text {M}{\text {DVS}}$$ ) schemes with privacy of identities. This in particular means that our $$\text {M}{\text {DRS-PKE}}$$ construction yields the first $$\text {M}{\text {DVS}}$$ scheme with privacy of identities from standard assumptions. The only prior construction of such schemes was based on Verifiable Functional Encryption for general circuits (Damgård et al., TCC ’20).
A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images

Semantic segmentation in high-resolution aerial images is a fundamental and challenging task with a wide range of applications. Although many segmentation methods with convolutional neural networks have achieved inspiring results, it is still difficult to distinguish regions with similar spectral features only using high-resolution data. Besides, the traditional data-independent upsampling methods may lead to sub-optimal results. This letter proposes a multisensor data fusion model (MSDFM). Following the classical encoder-decoder structure, MSDFM regards colored digital surface models (colored-DSM) data as a complementary input for further detailed feature extraction. A data-dependent upsampling (DUpsampling) method is adopted in the decoder stage instead of the common up-sampling approaches to improve the classification accuracy of pixels of the small objects. Extensive experiments on Vaihingen and Potsdam datasets demonstrate that our proposed MSDFM outperforms most related models. Significantly, segmentation performance for the car category surpasses state-of-the-art methods over the ISPRS Vaihingen dataset.
Collaborative Cybersecurity Learning: Establishing Educator and Learner Expectations and Requirements

Effective provision of cybersecurity requires practitioners to work collaboratively to solve practical real-world problems. However, the extent to which these skills are supported by current higher education programmes is potentially limited. This paper presents an investigation into the needs of related learners and the educators who support them, examining the provisions within current cybersecurity education at degree level, and the extent to which they go beyond traditional knowledge transmission approaches. The findings illustrate a broad appreciation of the interdisciplinary nature of cybersecurity and recognition of the value of using collaborative learning approaches. At the same time, however, these aspects are not represented strongly enough within the current provision.
Rethinking Panoptic Segmentation in Remote Sensing: A Hybrid Approach Using Semantic Segmentation and Non-Learning Methods

This letter proposes a novel method to obtain panoptic predictions by extending the semantic segmentation task with a few non-learning image processing steps, presenting the following benefits: 1) annotations do not require a specific format [e.g., common objects in context (COCO)]; 2) fewer parameters (e.g., single loss function and no need for object detection parameters); and 3) a more straightforward sliding windows implementation for large image classification (still unexplored for panoptic segmentation). Semantic segmentation models do not individualize touching objects, as their predictions can merge; i.e., a single polygon represents many targets. Our method overcomes this problem by isolating the objects using borders on the polygons that may merge. The data preparation requires generating a one-pixel border, and for unique object identification, we create a list with the isolated polygons, attribute a different value to each one, and use the expanding border (EB) algorithm for those with borders. Although any semantic segmentation model applies, we used the U-Net with three backbones (EfficientNet-B5, EfficientNet-B3, and EfficientNet-B0). The results show that the following hold: 1) the EfficientNet-B5 had the best results with 70% mean intersection over union (mIoU); 2) the EB algorithm presented better results for better models; 3) the panoptic metrics show a high capability of identifying things and stuff with 65 panoptic quality (PQ); and 4) the sliding windows on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2560\times 2560$ </tex-math></inline-formula> -pixel area has shown promising results, in which the ratio of merged objects by correct predictions was lower than 1% for all classes.
Variational Hyperparameter Inference for Few-Shot Learning Across Domains

The focus of few shot learning research has been on the development of meta-learning recently, where a meta-learner is trained on a variety of tasks in hopes of being generalizable to new tasks. Tasks in meta training and meta test are usually assumed to be from the same domain, which would not necessarily hold in real world scenarios. In this paper, we propose variational hyperparameter inference for few-shot learning across domains. Based on an especially successful algorithm named model agnostic meta learning, the proposed variational hyperparameter inference integrates meta learning and variational inference into the optimization of hyperparameters, which enables the meta-learner with adaptivity for generalization across domains. In particular, we choose to learn adaptive hyperparameters including the learning rate and weight decay to avoid the failure in the face of few labeled examples across domain. Moreover, we model hyperparameters as distributions instead of fixed values, which will further enhance the generalization ability by capturing the uncertainty. Extensive experiments are conducted on two benchmark datasets including few shot learning dataset within-domain and across-domain. The results demonstrate that our methods outperforms previous approaches consistently, and comprehensive ablation studies further validate its effectiveness on few shot learning both within domains and across domains.
Lessons Learned from COVID-19 Emergency Remote Education. Adaptation to Crisis Distance Education of Teachers by Developing New or Modified Digital Competences

Emergency remote education caused by the COVID-19 pandemic brought numerous challenges to both educational systems and educational professionals. All teachers, including those reluctant to use ICT in their classrooms, had to implement it under specific social and psychological circumstances. That situation brought a variety of challenges and demanded from teachers appropriate skills to carry out effective educational activities. All of this put a new light on the problems connected with e-education such as digital inequalities, digital well-being, or development of digital skills by students and teachers. The text analyses in a coherent way the commonly accepted European Framework for the Digital Competence of Educators (DigCompEdu) in the context of its usefulness for the specific situation during school closure. We refer to the research data pointing out which teacher competences are significantly more important or which should be defined in a different way or adjusted based on the pandemic experience. The text confirms the adequacy of the European Framework for the Digital Competence of Educators (DigCompEdu) but brings attention to important details that may be important for its implementation after the COVID-19 period.
A Primer for tinyML Predictive Maintenance: Input and Model Optimisation

In this paper, we investigate techniques used to optimise tinyML based Predictive Maintenance (PdM). We first describe PdM and tinyML and how they can provide an alternative to cloud-based PdM. We present the background behind deploying PdM using tinyML, including commonly used libraries, hardware, datasets and models. Furthermore, we show known techniques for optimizing tinyML models. We argue that an optimisation of the entire tinyML pipeline, not just the actual models, is required to deploy tinyML based PdM in an industrial setting. To provide an example, we create a tinyML model and provide early results of optimising the input given to the model.
Employing Natural Language Processing Techniques for Online Job Vacancies Classification

With the advances in natural language processing and big data analytics, the labor market community has introduced the emerging field of Labor Market Intelligence (LMI). This field aims to design and utilize Artificial Intelligence (AI) algorithms and frameworks to analyze data related to the labor market information for supporting policy and decision-making. This paper elaborates on the automatic classification of free-text Web job vacancies on a standard taxonomy of occupations. In achieving this, we draw on well-established approaches for extracting textual features, which subsequently are employed for training machine learning algorithms. The training and evaluation of our machine learning models were performed with data extracted from online sources, pre-processed, and hand-annotated following the ISCO taxonomy. The results showed that the proposed model is very promising. The advantage is its simplicity. After its application to a relatively small and difficult to clean dataset, it achieved a good accuracy. Furthermore, in this paper we discuss how real-life applications for skill anticipation and matching could benefit from our approach.
Mc2g: An Efficient Algorithm for Matrix Completion With Social and Item Similarity Graphs

In this paper, we design and analyze MC2G (Matrix Completion with 2 Graphs), an algorithm that performs matrix completion in the presence of social and item similarity graphs. MC2G runs in quasilinear time and is parameter free. It is based on spectral clustering and local refinement steps. For the matrix completion problem which possesses additional block structures in its rows and columns, we derive the expected number of sampled entries required for MC2G to succeed, and further show that it matches an information-theoretic lower bound up to a constant factor for a wide range of parameters. We perform extensive experiments on both synthetic datasets and a semi-real dataset inspired by real graphs. The experimental results show that MC2G outperforms other state-of-the-art matrix completion algorithms.
INS’s Error Compensation via Measurement Differencing Kalman Filter

The Inertial Navigation System (INS) error compensation for the case of time-correlated measurement errors is proposed in this study. Measurement differences are derived in the filter for the solution of the state estimation problem. In this case, the measurement noise for the derived measurements is no longer correlated in time, but it is correlated with the process noise. Therefore, in this study, the measurement differencing approach based Kalman filter (KF) is designed for the case of correlated system and measurement noise. The innovation properties of the proposed Measurement Differencing Kalman filter (MDKF) are investigated. Conventional KF, robust adaptive KF, and proposed MDKF were applied to estimate the states of a multi-input /output aircraft model in the presence of time-correlated INS measurements, and the obtained results were compared. It is shown that MDKF is also robust to the noise increment type INS errors.
Estimation of light-matter coupling constant under dispersive interaction based on quantum Fisher information

Quantum parameter estimation is one of the most important applications in quantum metrology. The basic theory of quantum parameter estimation－quantum Cramer-Rao bound－shows that the precision limit of quantum parameter estimation is directly related to quantum Fisher information. Therefore quantum Fisher information is extremely important in the quantum parameter estimation. In this paper we use quantum parameter estimation theory to estimate the coupling constant of the Jaynes-Cummings model with large detuning. The initial probing state is the direct product state of qubit and radiation field in which Fock state, thermal state and coherent state are taken into account respectively. We calculate the quantum Fisher information of the hybrid system as well as qubit and radiation field for each probing state after the parameter evolution under the Hamiltonian of the Jaynes-Cummings model with large detuning. The results show that the quantum Fisher information increases monotonically with the average photon number increasing. The optimal detection state is that when the qubit system is in the equal weight superposition of the ground and the excited state, at this time the quantum Fisher information always reaches a maximum value, When the radiation field of probing state is Fock state or the thermal state, the information about the estimated parameter is included only in the qubit. The estimation accuracy of the coupling constant with thermal state or coherent state is higher than that with Fock state.
Cross-Modal Enhancement Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) plays an important role in many applications, such as intelligent question-answering, computer-assisted psychotherapy and video understanding, and has attracted considerable attention in recent years. It leverages multimodal signals including verbal language, facial gestures, and acoustic behaviors to identify sentiments in videos. Language modality typically outperforms nonverbal modalities in MSA. Therefore, strengthening the significance of language in MSA will be a vital way to promote recognition accuracy. Considering that the meaning of a sentence often varies in different nonverbal contexts, combining nonverbal information with text representations is conducive to understanding the exact emotion conveyed by an utterance. In this paper, we propose a Cross-modal Enhancement Network (CENet) model to enhance text representations by integrating visual and acoustic information into a language model. Specifically, it embeds a Cross-modal Enhancement (CE) module, which enhances each word representation according to long-range emotional cues implied in unaligned nonverbal data, into a transformer-based pre-trained language model. Moreover, a feature transformation strategy is introduced for acoustic and visual modalities to reduce the distribution differences between the initial representations of verbal and nonverbal modalities, thereby facilitating the fusion of distinct modalities. Extensive experiments on benchmark datasets demonstrate the significant gains of CENet over state-of-the-art methods.
Multidimensional Prototype Refactor Enhanced Network for Few-Shot Action Recognition

Few-shot action recognition classifies new actions with only few training samples, of which the mainstream methods adopt class means to obtain prototypes as the representations of each category. However, affected by sample capacity and extreme samples, mean-of-class prototypes can’t well represent the average level of samples. In this paper, we enhance the prototypes from multiple dimensions for better classification. We firstly propose a novel similarity optimization mechanism where Prototype Aggregation Adaptive Loss (PAAL) is designed to deeply mine the similarity between samples and prototypes for enhancing the ability of inter-class differential detail identification. Secondly, for mitigating the impact of the samples on class prototypes, we refactor the prototype calculation formula with Cross-Enhanced Prototype (CEP) to narrow intra-class differences in which Reweighted Similarity Attention (RSA) is designed to update prototypes. Finally, Dynamic Temporal Transformation (DTT) is proposed to alleviate inconsistent distribution of temporal information for obtaining better video-level descriptors. Extensive experiments on standard benchmark datasets demonstrate that our proposed method achieves the state-of-the-art results.
Private Federated Learning With Misaligned Power Allocation via Over-the-Air Computation

To further preserve the data privacy of federated learning (FL), we propose a differentially private FL (DPFL) scheme with misaligned power allocation (MPA-DPFL). Unlike most existing over-the-air FL studies, in MPA-DPFL, the gradients are aggregated through over-the-air computation (Aircomp) but do not need to be aligned in the transmission. Therefore, MPA-DPFL can avoid the problem that the signal-to-noise ratio (SNR) of the system is limited by the device with the worst channel condition. We formulate an optimization problem to minimize the optimality gap of MPA-DPFL while guaranteeing a certain degree of privacy protection. Additionally, we demonstrate that the MPA-DPFL is more suitable than the DPFL with aligned power allocation (APA-DPFL) when the channel condition of a device in the system is lower than a threshold. The analytical results are validated through simulation.