Reviewerly/exHarmony huggingface dataset:
Research on the Detection Method of Coal Mine Roadway Bolt Mesh Based on Improved YOLOv7

Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s.
Semantic Information Recovery in Wireless Networks

Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and, thus, enables savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved. We consider this task as an end-to-end Information Bottleneck problem, enabling compression while preserving relevant information. As a solution approach, we propose the ML-based semantic communication system SINFONY and use it for a distributed multipoint scenario; SINFONY communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic recovery. We analyze SINFONY by processing images as message examples. Numerical results reveal a tremendous rate-normalized SNR shift up to 20 dB compared to classically designed communication systems.
Privacy-preserving continuous authentication using behavioral biometrics

Abstract Continuous authentication modalities collect and utilize users’ sensitive data to authenticate them continuously. Such data contain information about user activities, behaviors, and other demographic information, which causes privacy concerns. In this paper, we propose two privacy-preserving protocols that enable continuous authentication while preventing the disclosure of user-sensitive information to an authentication server. We utilize homomorphic cryptographic primitives that protect the privacy of biometric features with an oblivious transfer protocol that enables privacy-preserving information retrieval. We performed the biometric evaluation of the proposed protocols on two datasets, a swipe gesture dataset and a keystroke dynamics dataset. The biometric evaluation shows that the protocols have very good performance. The execution time of the protocols is measured by considering continuous authentication using: only swipe gestures, keystroke dynamics, and hybrid modalities. The execution time proves the protocols are very efficient, even on high-security levels.
Minimalistic estimation with stochastic constraints for closed-type tethered satellite formations

This paper aims to propose a constrained estimation scheme with minimalistic sensor settings for a class of closed-type tethered satellite formation (TSF). The estimation scheme is built on the fusion of dynamic model, priori tether length constraints, and position observation through a learning-based constrained particle filtering (CPF) design. The CPF characterizes the tether lengths as stochastic constraints and integrates these constraints within Bayesian framework based on pseudo observation and radial basis function (RBF) neural network. The RBF neural network can learn the underlying relationship between the system states and uncertain tether oscillations to establish a probability distribution function (PDF) of stochastic constraints. Nonlinear observability analysis using observability rank criterion is performed to yield insight into the minimum number of positioning sensors needed for n-body (n≥3) closed-type TSF. Furthermore, it is found that the use of pseudo observation for filtering can reduce the number of needed positioning sensors. Interestingly, the minimum number of positioning sensors required for determining all position information of n-body (n≥3) closed-type TSF is 1. Finally, the effectiveness of proposed estimation scheme is verified by posterior Cramér-Rao lower bound (PCRLB) calculation and extensive simulations. The proposed estimation scheme can be readily extended to other types of TSF. For example, the chain-type TSF.
On the geometry of a (q + 1)-arc of PG(3,q), q even

In PG(3,q), q=2n, n≥3, let Ah,q={(1,t,t2h,t2h+1)|t∈Fq}∪{(0,0,0,1)}, with gcd⁡(n,h)=1, be a (q+1)-arc and let Gh≃PGL(2,q) be the stabilizer of Ah,q in PGL(4,q). The Gh-orbits on points, lines and planes of PG(3,q), together with the point-plane incidence matrix with respect to the Gh-orbits on points and planes of PG(3,q) are determined. The point-line incidence matrix with respect to the G1-orbits on points and lines of PG(3,q) is also considered. In particular, for a line ℓ belonging to a given line G1-orbit, say L, the point G1-orbit distribution of ℓ is either explicitly computed or it is shown to depend on the number of elements x in Fq (or in a subset of Fq) such that Trq|2(g(x))=0, where g is an Fq-map determined by L.
Neighborhood overlap-aware heterogeneous hypergraph neural network for link prediction

In real world, a large number of networks are heterogeneous, containing different types of semantics and connections. Existing studies typically only consider lower-order pairwise relations rather than higher-order group interactions. Furthermore, they tend to focus more on node attributes rather than graph structural information. This results models failing to maintain graph topology effectively, which reduces the effectiveness on link prediction. To address these limitations, we propose Neighborhood Overlap-aware Heterogeneous hypergraph neural network (NOH) that learns useful structural information from the heterogeneous graph and estimates overlapped neighborhood for link prediction. Our model fuses the heterogeneity of graphs with structural information so that the model maintains both lower-order pairwise relations and higher-order complex semantics. Our extensive experiments on four real-world datasets show that NOH consistently achieves state-of-the-art performance on link prediction.
Real-Valued Optical Matrix Computing with Simplified MZI Mesh

The Mach–Zehnder interferometer (MZI) mesh, a mainstream structure for optical matrix-vector multiplication (MVM), has been widely employed in recently developed optical neural networks (ONNs) and combination optimization problem solvers. The conventional MZI mesh was designed specifically for complex-valued optical MVM. The network includes 2 N 2 phase shifters, and coherent detection is indispensable for retrieving the output complex-valued vectors. Nonetheless, the majority of applications, including ONNs, merely require real-valued optical matrices with N 2 degrees of freedom (DOFs). The DOF gap between the 2 types of matrices results in a severe redundancy in the number of phase shifters when the conventional MZI mesh is applied to implement real-valued optical MVM. In this study, we propose a simplified MZI mesh for performing real-valued incoherent optical MVM. It has N 2 phase shifters and an optical depth of N + 1, and it outperforms the conventional MZI mesh. Furthermore, we constructed an ONN with the proposed MZI mesh and successfully performed the iris classification task via in situ training of particle swarm optimization. More importantly, we introduced a matched on-chip nonlinear activation function, so the proposed MZI mesh can be cascaded onto a single chip. Overall, the proposed real-valued MZI mesh and in situ training method are space efficient, energy efficient, scalable, and robust to fabrication errors. Therefore, they are suitable for large-scale ONNs.
Disentangle First, Then Distill: A Unified Framework for Missing Modality Imputation and Alzheimer’s Disease Diagnosis

Multi-modality medical data provide complementary information, and hence have been widely explored for computer-aided AD diagnosis. However, the research is hindered by the unavoidable missing-data problem, i.e., one data modality was not acquired on some subjects due to various reasons. Although the missing data can be imputed using generative models, the imputation process may introduce unrealistic information to the classification process, leading to poor performance. In this paper, we propose the Disentangle First, Then Distill (DFTD) framework for AD diagnosis using incomplete multi-modality medical images. First, we design a region-aware disentanglement module to disentangle each image into inter-modality relevant representation and intra-modality specific representation with emphasis on disease-related regions. To progressively integrate multi-modality knowledge, we then construct an imputation-induced distillation module, in which a lateral inter-modality transition unit is created to impute representation of the missing modality. The proposed DFTD framework has been evaluated against six existing methods on an ADNI dataset with 1248 subjects. The results show that our method has superior performance in both AD-CN classification and MCI-to-AD prediction tasks, substantially over-performing all competing methods.
Deep Learning-Based RF Fingerprint Identification With Channel Effects Mitigation

The radio frequency fingerprint (RFF)-based device identification is a promising physical layer authentication technique.However, the wireless channel significantly affects the RFF features of the wideband wireless devices.In this paper, we extensively investigate the impact of channel variation on RFF identification using 20 MHz IEEE 802.11 signal.A time domain least mean square (LMS) equalizationbased feature extraction method has been proposed.This method progressively restores the transmitted signal and preserves more details of RFF features than the classical frequency domain equalization (FDE) method.Moreover, a hybrid identifier is proposed to take advantage of both LMS-based and FDE-based methods.With the equalized samples, a four-layer convolutional neural network is designed for device identification.An experimental system has been set up to capture the waveform of 68 802.11 devices at different positions.The experimental results show that the LMS-based method outperforms others when the acquisition positions of the training dataset are the same as those of the testing dataset.On the other hand, the FDE-based method is shown to be more effective when the acquisition positions of the training dataset do not fully include those of the testing dataset.Moreover, the hybrid identifier achieves an improvement of 2% for overall identification accuracy.
Fully Abstract Normal Form Bisimulation for Call-by-Value PCF

We present the first fully abstract normal form bisimulation for call-by-value PCF (PCFv).Our model is based on a labelled transition system (LTS) that combines elements from applicative bisimulation, environmental bisimulation and game semantics.In order to obtain completeness while avoiding the use of semantic quotiening, the LTS constructs traces corresponding to interactions with possible functional contexts.The model gives rise to a sound and complete technique for checking of PCFv program equivalence, which we implement in a bounded bisimulation checking tool.We test our tool on known equivalences from the literature and new examples.
Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece

Energy nowcasting is a valuable asset in managing energy loads and having real-time information on solar irradiation availability. In this study, we evaluate the spectrally integrated outputs of the SENSE system for solar irradiance nowcasting for the period of the ASPIRE (atmospheric parameters affecting spectral solar irradiance and solar energy) campaign (December 2020–December 2021) held in Athens, Greece. For the needs of the campaign, several ground-based instruments were operating, including two pyranometers, a pyrheliometer, a cloud camera, a CIMEL sunphotometer, and a precision spectral radiometer (PSR). Global horizontal irradiance (GHI) estimations were more accurate than direct normal irradiance (DNI). SENSE estimations are provided every 15 min, but when comparing bigger time intervals (hours-days), the statistics improved. A dedicated assessment of the SENSE’s inputs is performed in respect to ground-based retrievals, considering cloud conditions (from a sky imager), AOD, and precipitable water vapor from AERONET. The factor that established the larger errors was the visibility of the solar disc, which cannot be defined by the available sources of model inputs. Additionally, there were discrepancies between the satellite estimation of the clouds and the ground picture, which caused deviations in results. AOD differences affected more the DNI.
Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

Abstract Solar photovoltaic (PV) deployment plays a crucial role in the transition to renewable energy. However, comprehensive models that can effectively explain the variations in solar PV deployment are lacking. This study aims to address this gap by introducing two innovative models: (i) a computer vision model that can estimate spatial distribution of solar PV deployment across neighborhoods using satellite images and (ii) a machine learning (ML) model predicting such distribution based on 43 factors. Our computer vision model using Faster Regions with Convolutional Neural Network (Faster RCNN) achieved a mean Average Precision (mAP) of 81% for identifying solar panels and 95% for identifying roofs. Using this model, we analyzed 652,795 satellite images from Colorado, USA, and found that approximately 7% of households in Colorado have rooftop PV systems, while solar panels cover around 2.5% of roof areas in the state as of early 2021. Of our 16 predictive models, the XGBoost models performed the best, explaining approximately 70% of the variance in rooftop solar deployment. We also found that the share of Democratic party votes, hail and strong wind risks, median home value, the percentage of renters, and solar PV permitting timelines are the key predictors of rooftop solar deployment in Colorado. This study provides insights for business and policy decision making to support more efficient and equitable grid infrastructure investment and distributed energy resource management.
Infrared and Visible Image Homography Estimation Based on Feature Correlation Transformers for Enhanced 6G Space–Air–Ground Integrated Network Perception

The homography estimation of infrared and visible images, a key technique for assisting perception, is an integral element within the 6G Space–Air–Ground Integrated Network (6G SAGIN) framework. It is widely applied in the registration of these two image types, leading to enhanced environmental perception and improved efficiency in perception computation. However, the traditional estimation methods are frequently challenged by insufficient feature points and the low similarity in features when dealing with these images, which results in poor performance. Deep-learning-based methods have attempted to address these issues by leveraging strong deep feature extraction capabilities but often overlook the importance of precisely guided feature matching in regression networks. Consequently, exactly acquiring feature correlations between multi-modal images remains a complex task. In this study, we propose a feature correlation transformer method, devised to offer explicit guidance for feature matching for the task of homography estimation between infrared and visible images. First, we propose a feature patch, which is used as a basic unit for correlation computation, thus effectively coping with modal differences in infrared and visible images. Additionally, we propose a novel cross-image attention mechanism to identify correlations between varied modal images, thus transforming the multi-source images homography estimation problem into a single-source images problem by achieving source-to-target image mapping in the feature dimension. Lastly, we propose a feature correlation loss (FCL) to induce the network into learning a distinctive target feature map, further enhancing source-to-target image mapping. To validate the effectiveness of the newly proposed components, we conducted extensive experiments to demonstrate the superiority of our method compared with existing methods in both quantitative and qualitative aspects.
Logistics Model Based on Smart Contracts on Blockchain and IoT

Many goods require certain conditions for their storage, such as temperature, humidity and arrangement, which are strictly specific.These include dangerous goods, medicines and goods with a limited shelf life.Servicing them in warehouses is an important task in order to be able to establish and monitor the flow of their transportation from the manufacturer's warehouse to the end user.In this context, the use of modern technologies, such as IoT, makes it possible to facilitate the activity of logistics centers on the storage, receipt and loading of goods, tracking their location and condition, as well as the inventory of their availability.This paper proposes an integration model of blockchain technology and IoT for logistics business.This model uses smart contracts on the blockchain, and it is suitable for managing cross-docking warehouses and transportation.
Towards a Conceptual Typology of Darknet Risks

ABSTRACTABSTRACTIncreased rewards and reduced risks drive illicit networks. Cybercriminals seek to avoid risks, including detection, arrests, sanctions, and violence. Hence, they employ several strategies to avoid operational risks when accessing darknet sites, communicating, making payments, shipping and delivery, and resolving disputes. Each operation involves transactions that should work together, such that when a transaction is disrupted, it increases its risk and reduces the crime's benefits. In this study, we examine these risks, and their avoidance strategies, in an attempt to highlight transactions that could be disrupted. We develop a typology of darknet risks, a parsimonious conceptual model abstracted from the literature on risk originators (internal or external) and risk targets (vendors or buyers). This study further clarifies how risk avoidance strategies may be disrupted to increase the risks and reduce the reward of darknet crime.KEYWORDS: Cybercrimeillicit networksdarknet marketplacevisibility reductionrisk avoidancecrime script Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the U.S. Department of Homeland Security (DHS) by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between DHS and the U.S. Department of Energy (DOE). ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. This document has not been formally reviewed by DHS. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DHS, DOE, or ORAU/ORISE. DHS, DOE and ORAU/ORISE do not endorse any products or commercial services mentioned in this publication.
Fine-grained Commit-level Vulnerability Type Prediction by CWE Tree Structure

Identifying security patches via code commits to allow early warnings and timely fixes for Open Source Software (OSS) has received increasing attention. However, the existing detection methods can only identify the presence of a patch (i.e., a binary classification) but fail to pinpoint the vulnerability type. In this work, we take the first step to categorize the security patches into fine-grained vulnerability types. Specifically, we use the Common Weakness Enumeration (CWE) as the label and perform fine-grained classification using categories at the third level of the CWE tree. We first formulate the task as a Hierarchical Multi-label Classification (HMC) problem, i.e., inferring a path (a sequence of CWE nodes) from the root of the CWE tree to the node at the target depth. We then propose an approach named TreeVul with a hierarchical and chained architecture, which manages to utilize the structure information of the CWE tree as prior knowledge of the classification task. We further propose a tree structure aware and beam search based inference algorithm for retrieving the optimal path with the highest merged probability. We collect a large security patch dataset from NVD, consisting of 6,541 commits from 1,560 GitHub OSS repositories. Experimental results show that Tree-vulsignificantly outperforms the best performing baselines, with improvements of 5.9%, 25.0%, and 7.7% in terms of weighted F1-score, macro F1-score, and MCC, respectively. We further conduct a user study and a case study to verify the practical value of TreeVul in enriching the binary patch detection results and improving the data quality of NVD, respectively.