Reviewerly/exHarmony huggingface dataset:
Hybrid learning-oriented approaches for predicting Covid-19 time series data: A comparative analytical study

Using medical science alongside time series data analysis can be given a strong tool to develop efficient decision support systems in Corona pandemic. In this regard, many hybrid learning-oriented (HL) approaches have been presented, which rely on modeling the linear and non-linear components of the time series. However, there is a lack of comprehensive study of such approaches to achieve a macro vision of Covid-19 data prediction models in an unified reference. We conducted a comparative analytical study on (HL) approaches for predicting Covid-19 data. The main scope of current study is the investigate of such approaches. The original contribution of the paper is to present a reference-point and roadmap for future studies, which is provided in three forms. First, we experimentally evaluated the efficiency of all learning-based combinations on types of Covid-19 data in a similar context. Second, we tried to provide a guidance for choosing a more proper hybrid through valid empirical and statistical evaluations. Third, we presented an efficient and generalizable approach called HL-ALL (Hybrid Learning ARIMA LSTM LSTM). Evaluation results show high potential of HL-ALL in dealing Covid-19 data when prediction.
Tunable nonlinear optical mapping in a multiple-scattering cavity

Nonlinear disordered systems are not only a model system for fundamental studies but also in high demand for practical applications. However, optical nonlinearity based on intrinsic material response is weak in random scattering systems. Here, we propose and experimentally realize a highly nonlinear mapping between the scattering potential and the emerging light of a reconfigurable multiple-scattering cavity. A quantitative analysis of the degree of nonlinearity reveals its dependence on the number of scattering events. The effective order of nonlinear mapping can be tuned over a wide range at low optical lower. The strong nonlinear mapping enhances output intensity fluctuations and long-range correlations. The flexibility, robustness, and energy efficiency of our approach provides a versatile platform for exploring such nonlinear mappings for various applications.
Computing on Encrypted Data

The ability to compute on encrypted data is fast becoming a practical reality. We discuss the progress in four technologies which enable this: Trusted Execution Environments, Fully Homomorphic Encryption, Multi-Party Computation and Zero-Knowledge Proofs.
ChatGPT: The Curious Case of Attack Vectors' Supply Chain Management Improvement

The field of Natural Language Processing has observed significant advancements in the development of sophisticated conversational Artificial Intelligence systems. ChatGPT is one such state-of-the-art conversational system that has attracted considerable interest and adoption. It enables developers to create highly interactive and engaging conversational applications using deep neural networks to produce human-like responses to user inputs. Such capabilities have made it popular in the threat actors' world. However, threat actors can abuse this chatbot to generate attack vectors as part of an operation. ChatGPT can be abused to produce practical and realistic communications that can be used in phishing attacks. These communications help the attack vectors distribution, i.e., prompt users to download and set up malware or disclose confidential information. ChatGPT has security measures to prevent malicious queries from generating attack vectors. However, the threat actors can circumvent such security controls through deception. This abusive use of ChatGPT makes the supply chain management of attack vectors effective and efficient. In this study, we presented evidence from various sources, showing how ChatGPT is abused to help the threat actors to improve each step of the attack vectors' supply chain management.
ChatGPT: A Threat Against the CIA Triad of Cyber Security

The AI revolution has brought significant changes to society. AI-powered systems can analyze enormous amounts of data to optimize processes, improve accuracy, and cut costs. Nevertheless, addressing potential hazards and ethical issues related to AI enabled technologies, such as bias and job displacement, is essential. This paper presented an example of an AI revolution threatening cyber security, the ChatGPT. ChatGPT, a chatbot, can generate essays or code on demand. However, ChatGPT's security system can be circumvented or deceived to generate malicious content. Moreover, these AI enabled tools to have design issues, e.g., accuracy issues. As a result, ChatGPT can be accused of violating the confidentiality of information (privacy invasion), producing inaccurate information, and potentially facilitating attack tool generation that can compromise the availability principle of the CIA triad. This paper presents ChatGPT as a threat against the CIA triad principle by focusing on violating these principles.
Revisiting the transferability of adversarial examples via source-agnostic adversarial feature inducing method

Though deep neural networks (DNNs) have revealed their extraordinary performance in the fields of computer vision, it is evident that the vulnerability of DNNs to adversarial attacks with crafted human-imperceptible perturbations. Most existing adversarial attacks draw their attention to invading target deep task models by enhancing input-diagnostic features via image rotation, warp, or transformation to improve adversarial transferability. Such manners pay close concentration to operation on original inputs regardless of the properties from different source information. Research has inspired us to consider utilizing source-agnostic information and integrating generated features with raw inputs to enrich adversarial properties. For such needs, we propose a simple and flexible adversarial attack method with source-agnostic Feature Inducing Method (FIM) for improving the transferability of adversarial examples (AEs). FIM first focuses on generating perturbed features by imitating diverse patterns from multi-domain sources. Instead of exploiting the original inputs' diversity, such proposed work gains the various properties by random feature imitation referring to different source distributions. By optimizing the generated features with norm bounds, FIM then integrates original inputs with imitative features. Such manner can diverse row positive class-general features, which reduce the capability of class-specific patterns on cross-model transferability. Based on the crafted property, FIM employs the adaptive gradient-based strategy on such information to generate perturbations, which helps to decrease probability dropping into local optimal when searching for the decision boundary of source and target models. We conduct detailed experiments to evaluate the performance of our proposed approach with existing baselines on three public datasets. The experimental results reveal the better performance of the proposed works on fooling source and target task models leading to a considerable margin in most adversarial scenarios. We further investigate adversarial attacks on adversarial defense models (with adversarial training and trades). Such a proposed attack strategy achieves better attack quality by a margin over 3.00% on CIFAR10 and reduces the robust accuracy of adversarially trained models by a large margin near 9.00% on MNIST. Furthermore, we exploit the performance of the proposed attack strategy applied to feature-level adversarial domains and conduct evaluations to demonstrate its adversarial feasibility in integrating with various attack mechanisms, which gains better adversarial effectiveness over 20.00% than the base attacks on studied deep task models.
A New Anisotropic Singularity Algorithm to Characterize Geo-Chemical Anomalies in the Duolong Mineral District, Tibet, China

With the increasing exploitation of mineral resources by humans, exploring non-traditional areas for hidden resources such as deep earth and sediment-covered regions has become a significant challenge in the field of mineral exploration. Geochemical data, as a crucial information carrier of geological bodies, serves as one of the direct and effective sources for quantitative analysis of regional geological evolution and mineralization prediction studies. It plays an indispensable role in geographic information system (GIS)-based mineral exploration. Due to the neglect of spatial distribution characteristics and the variability of statistical features with spatial metrics in traditional statistical methods, this paper employs fractal/multifractal and the local singularity analysis to identify geochemical anomalies from background and characterize geochemical distributions associated with porphyry Cu-Au mineralization in the Duolong mineral district, Tibet, China. A novel algorithm for estimating the singularity index, which takes anisotropy into consideration, is proposed and practically applied to the Duolong district. By comparing with the isotropic singularity index, this new method objectively identifies anisotropic geochemical signatures and investigates non-linear behaviors of ore-forming elements, making it more practical and effective in geo-anomaly extraction. Furthermore, the current method is capable of indicating variations in geochemical distributions at different scales through directional arrows marking analytical windows. The summed-up direction of these multi-scale vectors effectively demonstrates migration trends of ore materials at each location within the study area. The new method can pinpoint the location of ore-forming element accumulation and migration directions, unlocking valuable insights from complex datasets. This promises to revolutionize our understanding of how minerals are formed and distributed within the Earth’s crust.
Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: A Systematic Literature Review

Objectives: An SLR is presented focusing on text mining-based automation of SLR creation. The present review identifies the objectives of the automation studies and the aspects of those steps that were automated. In so doing, the various ML techniques used challenges, limitations, and scope of further research are explained. Methods: Accessible published literature studies primarily focus on automation of study selection, study quality assessment, data extraction, and data synthesis portions of SLR. Twenty-nine studies were analyzed. Results: This review identifies the objectives of the automation studies, steps within the study selection, study quality assessment, data extraction, and data synthesis portions that were automated, and the various ML techniques used challenges, limitations, and scope of further research. Discussion: We describe uses of NLP/TM techniques to support increased automation of systematic literature reviews. This area has attracted increase attention in the last decade due to significant gaps in the applicability of TM to automate steps in the SLR process. There are significant gaps in the application of TM and related automation techniques in the areas of data extraction, monitoring, quality assessment, and data synthesis. There is, thus, a need for continued progress in this area, and this is expected to ultimately significantly facilitate the construction of systematic literature reviews.
Impact force reconstruction and localization using Distance-assisted Graph Neural Network

In this paper, a novel impact force identification method named Distance-assisted Graph Neural Network (DAGNN) is proposed for simultaneous force localization and force history reconstruction. The DAGNN model is a graph-based supervised learning method. A graph is formed by treating the sensors as nodes and the connections between them as edges. The relationships of the nodes and edges contained in the graph are mathematically characterized into two matrices, an adjacency matrix and a degree matrix, that form the basis of the Graph Neural Network (GNN). Meanwhile, in order to improve the learning of the DAGNN model and realize impact force localization, physical distances between the position of the impact force and sensors are infused into the training process as physics-informed constraints. An experiment on a metal plate is conducted to verify the effectiveness and practicability of DAGNN. A sparse grid is arranged on the plate to collect the force and response signals to form a dataset for the DAGNN model. The results indicate that the accuracy of time history reconstruction of DAGNN is higher than a conventional GNN model, a typical Graph Convolutional Network (GCN) model and an ANN model, and also remain high accuracy with different locations of impact. In addition, due to the infused distance information, a simple relation regarding the distances between the impact location and the sensor location can be constructed to realize localization of the impact with good accuracy.
Two new classes of permutation trinomials over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math> with odd characteristic

Let q be an odd prime power and Fq3 be the finite field with q3 elements. In this paper, we propose two classes of permutation trinomials of Fq3 for an arbitrary odd characteristic based on the multivariate method and some subtle manipulation of solving equations with low degrees over finite fields. Moreover, we demonstrate that these two classes of permutation trinomials are QM inequivalent to all known permutation polynomials over Fq3. To the best of our knowledge, this paper is the first to study the construction of nonlinearized permutation trinomials of Fq3 with at least one coefficient lying in Fq3﹨Fq.
Avoiding Undesirable Equilibria in Control Barrier Function Approaches for Multi-Robot Planar Systems

Control Barrier Functions (CBFs) when paired with Quadratic Programming offer an efficient way to generate safety-critical controllers. In this paper, we utilize CBFs for guiding multiple robots to their goals while avoiding collisions with the environment and among themselves. However, in more complex scenarios, with many robots and non-convex obstacles, these approaches often fail to guide the robots towards their desired goals because there can be other stable and undesirable equilibrium points in the system other than the desired one (reaching the goal). The proposed approach in this paper mitigates this issue by including constraints in the formulation that force the robots to circulate the boundary of the obstacles as well as each other when in close proximity. This ensures that the system does not get stuck in an undesirable equilibrium. Simulation studies show the efficacy of the proposed approach for a multi-agent problem.
Digital hate speech and othering: The construction of hate speech from Malaysian perspectives

Hate speech is a phenomenon that affects communication and undermines multicultural society by disrupting intercultural engagement. The internet's euphoria allows media around the world to witness the troubling rise of vitriolic language. Moving beyond the Euro-America of hate speech phenomenon, cross-cultural nuanced by ethnography has evolved in Malaysia. Therefore, the challenge of protecting against discrimination, dehumanisation, and incitement to violence while preserving individual freedom of expression seems to be contextual when defining hate speech. A series of three different online focus groups which consist of a total of 22 participants were conducted to gather insights from civil society and experts to build concrete definitions and understand the construction of hate speech from a Malaysian perspective. Hence, it tries to explore the extent how social media act as a platform that exacerbates the othering discourse in the online sphere. This study contributes to the academic discussions about the public sphere, and social media in the creation of meaning, discourses, and ensuing cultural and social change, as well as understanding the trajectories across digital spheres by utilising online focus groups for discussion.
Semi-supervised transfer learning with hierarchical self-regularization

Both semi-supervised learning and transfer learning aim at lowering the annotation burden for training models. However, such two tasks are usually studied separately, i.e. most semi-supervised learning algorithms train models from scratch while transfer learning assumes pre-trained models as the initialization. In this work, we focus on a previously-less-concerned setting that further reduces the annotation efforts through incorporating both semi-supervised and transfer learning, where specifically a pre-trained source model is used as the initialization of semi-supervised learning. As those powerful pre-trained models are ubiquitously available nowadays and can considerably benefit various down-streaming tasks, such a setting is relevant to real-world applications yet challenging to design effective algorithms. Aiming at enabling transfer learning under semi-supervised settings, we propose a hierarchical self-regularization mechanism to exploit unlabeled samples more effectively, where a novel self-regularizer has been introduced to incorporate both individual-level and population-level regularization terms. The former term employs self-distillation to regularize learned deep features for each individual sample, and the latter one enforces self-consistency on feature distributions between labeled and unlabeled samples. Samples involved in both regularizers are weighted by an adaptive strategy, where self-regularization effects of both terms are adaptively controlled by the confidence of every sample. To validate our algorithm, exhaustive experiments have been conducted on diverse datasets such as CIFAR-10 for general object recognition, CUB-200-2011/MIT-indoor-67 for fine-grained classification and MURA for medical image classification. Compared with state-of-the-art semi-supervised learning methods including Pseudo Label, Mean Teacher, MixMatch and FixMatch, our algorithm demonstrates two advantages: first of all, the proposed approach adopts a new point of view to tackle problems caused by inadequate supervision and achieves very competitive results; then, it is complementary to these state-of-the-art methods and thus can be combined with them to get additional improvements. Furthermore, our method can also be applied to fully supervised transfer learning and self-supervised learning. We have published our code at https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning.
Vulnerability assessment in urban metro systems based on an improved cloud model and a Bayesian network

To effectively evaluate the vulnerability of urban rail transit operations, a hybrid method that combines an improved cloud model and Bayesian network is proposed. This research consists of four parts: evaluation system development, model establishment, result aggregation, and analysis. The developed improved cloud-Bayesian network model is composed of 13 root factors and 4 s-level factors. The Wuhan Metro is adopted as a case study to provide instructions and verify the proposed method. The results indicate the following: (1) Line 2 is the most vulnerable line in the case; (2) The equipment-related factor (D2) is the most significant second-level variable in the vulnerability management of urban rail transit operation; (3) The employee professional level (X2), equipment anti-interference ability (X6), and urban rail transit line density (X11) display high correlations with the corresponding second-level factors; and (4) Peak duration rate (X3), platform passenger density (X4), urban rail transit line density (X11) and disaster seriousness level (X12), especially X4, are considered the key factors when urban rail transit reaches a high vulnerability level. Accordingly, corresponding countermeasures are proposed. The results show that the research conclusion is consistent with the actual situation, and the proposed method can provide a reference for other similar circuits.
Planning the installation of building-integrated photovoltaic shading devices: A GIS-based spatiotemporal analysis and optimization approach

Building-integrated photovoltaics (BIPV) can produce power while occupying little urban space. Photovoltaic-integrated shading devices (PVSDs) are a key component of BIPV that can generate electricity while blocking excess daylight. However, previous studies have lacked a systematic design of PVSDs that accurately estimates the trade-offs between indoor sunshade duration and electricity generation. This study proposes a multi-objective optimization framework for maximizing PV potential, minimizing PV area, and enabling proper sunshade duration in complex urban surfaces. A GIS-based spatiotemporal analysis and optimization approach was applied to three PVSD planning scenarios: (i) parallel to the horizontal land surface, (ii) inclined at an angle equal to the local latitude, and (iii) rotated in real-time to keep the PV surface perpendicular to the solar radiation. Different PV widths are determined under different scenarios considering power generation and solar shading duration. In the real-time rotating scenario, the optimized 0.7-m-wide PVSDs can generate 0.861 GWh of electricity annually, with a competitive average power generation efficiency of 0.811 kWh/m2/day and a solar shading duration of 6.61 h/day. Flexible installation scenarios are suggested to account for shading from other upper PVSDs and surrounding buildings. This study can facilitate solar farming in global cities and contribute to renewable energy penetration.
Jointly Attacking Graph Neural Network and its Explanations

Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are still vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs. On the other hand, the explanation of GNNs (GnnExplainer for short) provides a better understanding of a trained GNN model by generating a small subgraph and features that are most influential for its prediction. In this paper, we first perform empirical studies to validate that GnnExplainer can act as an inspection tool and have the potential to detect the adversarial perturbations for graphs. This finding motivates us to further investigate a new problem: Whether a graph neural network and its explanations can be jointly attacked by modifying graphs with malicious desires? It is challenging to answer this question since the goals of adversarial attack and bypassing the GnnExplainer essentially contradict with each other. In this work, we give a confirmative answer for this question by proposing a novel attack framework (GEAttack) for graphs, which can attack both a GNN model and its explanations by exploiting their vulnerabilities simultaneously. To the best of our knowledge, this is the very first effort to attack both GNNs and explanations on graph-structured data for the trustworthiness of GNNs. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of the proposed method.