Reviewerly/exHarmony huggingface dataset:
SPDTS: A Differential Privacy-Based Blockchain Scheme for Secure Power Data Trading

Currently, the conventional mode of power data transaction is mediated by Web pages. Nevertheless, there are challenging issues such as privacy protection, transaction security and data reliability in power data trading. In this paper, we present a novel secure power data trading scheme (SPDTS). Firstly, the zero-knowledge proof is employed to achieve data availability and consistency without revealing the data. Then, SPDTS takes full advantage of the dispersibility and immutability of blockchain to ensure the reliability of data transactions. To keep the transaction process efficient, the processing tasks for power data are performed under smart contract. Meanwhile, a trusted execution environment (TEE) is adopted to guarantee the security of power data. Finally, we present a differential privacy scheme to safeguard the privacy information in the power data. Our study indicates that the proposed scheme can achieve privacy protection, transaction security and data reliability. Also, we conduct security analysis and verify the privacy protection property of the scheme in real cases.
An Updated Survey on the Convergence of Distributed Ledger Technology and Artificial Intelligence: Current State, Major Challenges and Future Direction

In recent times, Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) have become two of the most discussed sectors in Information Technology, with each having made a major impact. This has generated space for further innovation to occur in the convergence of the two technologies. In this paper, we gather, analyse, and present a detailed review of the convergence of AI and DLT in a vice versa manner. We review how AI is impacts DLT by focusing on AI-based consensus algorithms, smart contract security, selfish mining, decentralized coordination, DLT fairness, non-fungible tokens, decentralized finance, decentralized exchanges, decentralized autonomous organizations, and blockchain oracles. In terms of the impact DLT has on AI, the areas covered include AI data privacy, explainable AI, smart contract-based AIs, parachains, decentralized neural networks, Internet of Things, 5G technology and data markets, and sharing. Furthermore, we identify research gaps and discuss open research challenges in developing future directions.
Analyzing and Evaluating Critical Cyber Security Challenges Faced by Vendor Organizations in Software Development: SLR Based Approach

Security is the protection from various kinds of threats and most organizations engage in the challenge of security especially cyber-attacks. The attacks are increasing rapidly, due to which cyber security does not now change on supervised and pattern-based detection algorithms which assure continuous security observing. There are many kinds of problems in vendor organizations like cyber theft, which is the most common attack in cyberspace. This research study is developing a Cyber Security Challenges Model (CSCM) that will facilitate vendors&#x2019; organizations to identify challenges of cyber security during the development of software in a vendor organization. To find cyber security issues/challenges, a Systematic Literature Review (SLR) is conducted on 44 relevant research publications by developing a search string based on research questions. As the final selected research publications were less in number and did not complete our aim, therefore, snow bowling technique is applied to 67 relevant research publications. This relevant data was comprised of different databases/sources e.g., Google Scholar, IEEE Explore, SpringerLink, ACM Digital Library, anFffid ScienceDirect. Furthermore, for the distinctive literature review, we&#x2019;ve carried out all of the steps in SLR, for example, improvement of SLR protocol, initials, and a very last collection of the applicable information, data extraction, data quality assessment, and data synthesis. Thirteen (13) critical cyber security challenges are identified which are; &#x201C;Security issues/Access of Cyberattacks&#x201D;, &#x201C;Lack of Right Knowledge&#x201D;, &#x201C;Framework&#x201D;, &#x201C;Lack of Technical Support&#x201D;, &#x201C;Disaster Issues&#x201D;, &#x201C;Cost Security issues&#x201D;, &#x201C;Lack of Confidentiality and Trust&#x201D;, &#x201C;Lack of Management&#x201D;, &#x201C;Unauthorized Access issues&#x201D;, &#x201C;Lack of Resources&#x201D;, &#x201C;Lack of Metrics&#x201D;, &#x201C;Administrative Mistakes during Development&#x201D; and &#x201C;Lack of Quality, Liability, and Reliability&#x201D;. The findings of our analysis study signify the similarities and dissimilarities in the recognized cybersecurity challenges in different decades, companies/firms, continents, databases, and methodologies.
A Codesigned Integrated Photonic Electronic Neuron

In the modern era of artificial intelligence, increasingly sophisticated artificial neural networks (ANNs) are implemented, which pose challenges in terms of execution speed and power consumption.To tackle this problem, recent research on reduced-precision ANNs opened the possibility to exploit analog hardware for neuromorphic acceleration.In this scenario, photonic-electronic engines are emerging as a short-medium term solution to exploit the high speed and inherent parallelism of optics for linear computations needed in ANN, while resorting to electronic circuitry for signal conditioning and memory storage.In this paper we introduce a precision-scalable integrated Photonic-Electronic Multiply-Accumulate Neuron (PEMAN).The proposed device relies on (i) an analog photonic engine to perform reduced-precision multiplications at high speed and low power, and (ii) an electronic front-end for accumulation and application of the nonlinear activation function by means of a nonlinear encoding in the analog-to-digital converter (ADC).The device has been numerically validated through cosimulations to perform multiply-accumulate operations (MAC).Simulations are based on the iSiPP50G SOI process for the photonic engine and a commercial 28 nm CMOS process for the electronic front-end.The PEMAN exhibits a multiplication accuracy of 6.1 ENOB up to 10 GMAC/s, while it can perform computations up to 56 GMAC/s with a reduced accuracy down to 2.1 ENOB.The device can trade off speed and power consumption with resolution, significantly outperforming its analog electronics counterparts both in terms of speed and energy consumption.With respect to other photonic ANNs, the PEMAN has comparable speed and energy consumption with a higher resolution, while outperforming them by a hundredfold in the fan-in, which opens the possibility to accelerate more complex networks.
A New Dynamic Substitution Box for Data Security Using an Innovative Chaotic Map

As the motivations and capabilities of threat actors continue to evolve, providing data security has become more important than ever. For this purpose, different ciphers using various techniques are being developed. Currently, chaotic maps are designed and applied in the development of these ciphers. Modern ciphers utilize a substitution box (S-Box) as a core module to provide data security. In this article, an innovative chaotic map is suggested for the design of new and dynamic S-Box. Criteria like Bijectiveness, Nonlinearity (NL), Strict Avalanche Criterion (SAC), Bit Independence Criterion (BIC), Linear Approximation Probability (LP), and Differential Approximation Probability (DP) are used to critically analyze and evaluate the proposed S-Box performance against various attacks. The cryptanalytic strength of the proposed S-Box is equated with freshly designed S-Boxes for its customization in real-life security applications. The comparative analysis gratifies the true potential of the proposed S-Box for its solicitation in data security domain.
Event-Based Anomaly Detection Using a One-Class SVM for a Hybrid Electric Vehicle

In the controller development process, it is required to verify whether control functions operate normally, without fault or anomaly. Many control functions with complex system structures require various actual tests and test data analysis for verification. This paper presents an anomaly detection algorithm to verify control functions and to more efficiently analyze test data of a hybrid control unit (HCU) of a hybrid electric vehicle (HEV). The anomaly detection algorithm automatically detects anomaly of control functions from the test data, instead of manual labor by engineers. The examined target HCU control functions in this paper are the engine clutch engagement/disengagement control function and the engine start cooperative control function, which are crucial functions of transmission mounted electric drive (TMED) HEVs. A data-driven approach using a one-class support vector machine (SVM) is used to make it easy to apply various control functions. Actual vehicle test data is examined by the algorithm to verify the control functions at an actual vehicle level. The developed anomaly detection algorithm demonstrates feasibility and effectiveness of the proposed algorithm in detecting not only prior-known anomalies but also prior-unknown anomalies. Because other vehicle control functions have similar characteristics with the target functions of this paper, this algorithm is expected to be successfully applied to other HCU control functions.
A Class of Nonlinear Kalman Filters Under a Generalized Measurement Model With False Data Injection Attacks

In this letter, a generalized measurement model (GMM) is established under the attack injecting additive and multiplicative false data, simultaneously. The GMM can be applied to any existing nonlinear Kalman filters (NKFs), such as extended Kalman filter (EKF), cubature Kalman filter (CKF), and geometric unscented Kalman filter (GUF). Based on the constructed GMM, a class of nonlinear Kalman filters with GMM (NKFs-GMM) is proposed to deal with additive false data, multiplicative false data, and simultaneous attacks on additive and multiplicative false data with no increase of computational complexity. Numerical simulations show that the filtering accuracy of NKFs-GMM is superior to that of corresponding NKFs.
A Workflow Scheduling Approach With Modified Fuzzy Adaptive Genetic Algorithm in IaaS Clouds

The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms.
TRUE: Re-evaluating Factual Consistency Evaluation

Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data.While attracting increasing attention, such evaluation metrics are usually developed and evaluated in silo for a single task or dataset, slowing their adoption.Moreover, previous meta-evaluation protocols focused on system-level correlations with human annotations, which leave the examplelevel accuracy of such metrics unclear.In this work, we introduce TRUE: a comprehensive study of factual consistency metrics on a standardized collection of existing texts from diverse tasks, manually annotated for factual consistency.Our standardization enables an example-level meta-evaluation protocol that is more actionable and interpretable than previously reported correlations, yielding clearer quality measures.Across diverse stateof-the-art metrics and 11 datasets we find that large-scale NLI and question generation-andanswering-based approaches achieve strong and complementary results.We recommend those methods as a starting point for model and metric developers, and hope TRUE will foster progress towards even better methods.
Semi-Supervised Specific Emitter Identification Based on Bispectrum Feature Extraction CGAN in Multiple Communication Scenarios

Specific emitter identification (SEI) refers to the technology that uses the inherent defects of the physical layer of a hardware device to identify and uniquely associate a single emitter. Given its unique capability to automatically extract high-dimensional features, deep learning has recently shown great potential in SEI. However, the training process of supervised neural networks largely depends on the generalization of sample distribution and integrity of data labels. In specific noncooperative areas, such as electronic reconnaissance and spectrum monitoring, the unknown radio frequency devices and the complexity of the electromagnetic environment make it difficult to utilize sufficient labeled samples and capture the potential distribution of the data. Therefore, we introduce semi-supervised learning into SEI and propose a self-classification generative adversarial network (GAN) using bispectrum-based feature extraction. The bispectrum estimation of the signal is used as the feature representation of emitters, and the label information is embedded into the input latent layer to guide the training of the GAN. The shared weight update of the classification network is realized through semi-supervised training, and the optimization function of the generator is redefined to solve the mode collapse caused by the game principle. We innovatively extend SEI to communication scenarios with multiple relays and evaluate the effectiveness of our algorithm in different communication scenarios with contaminated radio-frequency fingerprints. The algorithm is also verified using Universal Software Radio Peripheral based on software-defined radio platforms. The numerical experimental results on six modulation signals demonstrate the excellent semi-supervised classification performance of the proposed SEI scheme in multiple scenarios.
TFW: Annotated Thermal Faces in the Wild Dataset

Face detection and subsequent localization of facial landmarks are the primary steps in many face applications. Numerous algorithms and benchmark datasets have been introduced to develop robust models for the visible domain. However, varying conditions of illumination still pose challenging problems. In this regard, thermal cameras are employed to address this problem, because they operate on longer wavelengths. However, thermal face and facial landmark detection in the wild is an open research problem because most of the existing thermal datasets were collected in controlled environments. In addition, many of them were not annotated with face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,982 images of 147 subjects collected under controlled and uncontrolled conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. In addition to our test set, we evaluated the models on the external RWTH-Aachen thermal face dataset to show the efficacy of our dataset. We have made the dataset, source code, and pre-trained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis.
BDC-GAN: Bidirectional Conversion Between Computer-Generated and Natural Facial Images for Anti-Forensics

Aiming at degrading the capability of the existing forensic methods in discriminating computer generated and natural facial images, a bidirectional conversion between computer-generated and natural facial images based on generative adversarial network (BDC-GAN) is proposed for anti-forensics in this paper. The generator of BDC-GAN is composed of noise encoding and content encoding. In the noise encoding, three high-pass filters are first utilized to extract the sensor pattern noise of the image, and then the stacked convolution layer is combined to continue encoding. In the content encoding, VGG-19 is truncated and fine-tuned to encode the content of the image. Some stacked convolution layers and adaptive instance normalization layer are used in the decoder. The discriminator uses multi-scale image discriminator. Furthermore, content loss and noise loss are well designed, and hyperparameters are reasonably set to accomplish the bidirectional conversion between two domain images meanwhile retaining the original facial contour. Experimental results and analysis demonstrate that the proposed anti-forensic method can achieve better visual quality and stronger deception ability compared with the existing unidirectional CG facial image anti-forensic methods and bidirectional domain adaptive methods, and its effectiveness is verified by the tests on the existing 9 forensic methods. It reveals that the existing forensic techniques can be bypassed by using adversarial learning, and it will eventually push the performance improvement of the discrimination of computer generated and natural facial images.
Weakly-Supervised Enhanced Semantic-Aware Hashing for Cross-Modal Retrieval

Owing to its query and storage efficiency, hash learning has sparked much interest for Cross-Modal Retrieval (CMR) task. Previous literatures have proved the superiority of supervised Cross-Modal Hashing (CMH) methods over unsupervised ones. Nevertheless, most existing supervised CMH methods still suffer from some limitations: 1) it is assumed that the observed labels of training data are complete and accurate, which may be impractical due to the missing and wrong class assignments in real applications, and 2) the semantic information is not fully excavated, especially for the semantic correlations among labels. To address these issues, this paper proposes a Weakly-supervised enhAnced Semantic-aware Hashing (WASH) method which simultaneously estimates the label noises and performs enhanced semantic-aware hash learning. WASH employs the low-rank and sparse decomposition to alleviate the label noises, and a high-level semantic factor as well as a semantic correlation matrix is obtained by low-rank factorization on the noise-reduced labels. The low-rank semantic factors and multi-modal features are jointly factorized into a common subspace to reduce the heterogeneity gaps, so as to enhance the semantic awareness of shared representation. In this way, the hash codes can be obtained by binarizing the shared representation with pairwise semantic similarity preserved. Experiments on several benchmark datasets verify the effectiveness of the proposed method in comparison with the state-of-the-art CMH approaches.
A Delegatable Attribute Based Encryption Scheme for a Collaborative E-Health Cloud

With the popularization and growing utilization of electronic health records (EHRs) coupled with the advancements in cloud computing, healthcare providers are interested in storing EHRs in third-party, semi-trusted cloud platforms. Given the collaborative nature of modern e-health environments, integrating access delegation is of paramount importance to strengthen the flexibility of the sharing of health information. However, access delegation has to be enforced in a controlled manner so that it will not jeopardize the security of the system. For such applications, attribute based encryption (ABE) mechanisms are quite useful given the fact that ABE facilitates an efficient way of enforcing secure, fine-grained access control over encrypted data. However, incorporating delegatability with ABE mechanisms is tricky, and the existing schemes lack the control over the process of delegation of encrypted data. As a solution, we propose a novel ABE based access control scheme which can enforce multi-level, controlled access delegation and demonstrated how it could be deployed in an e-health environment to securely share outsourced EHRs of patients. Furthermore, we have shown that the proposed scheme is secure against chosen plaintext attacks as well as attacks mounted via attribute collusion.
A Small UAV Optimized for Efficient Long-Range and VTOL Missions: An Experimental Tandem-Wing Quadplane Drone

Most types of Unmanned Aerial Vehicle (UAV, drone) missions requiring Vertical-Take-Off-and-Landing (VTOL) capability could benefit if a drone’s effective range could be extended. Example missions include Search-And-Rescue (SAR) operations, a remote inspection of distant objects, or parcel delivery. There are numerous research works on multi-rotor drones (e.g., quadcopters), fixed-wing drones, VTOL quadplanes, or tilt-motor/tilt-wing VTOLs. We propose a unique compact VTOL UAV optimized for long hover and long-range missions with great lifting capacity and manoeuvrability: a tandem-wing quadplane with fixed motors only. To the best of our knowledge, such a drone has not yet been researched. The drone was designed, built, and tested in flight. Construction details, its advantages, and issues are discussed in this research.
Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning

Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization.The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright-field microscopy images that contain white blood cells (WBCs).We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate in vitro cancer cells from WBCs. The second approach is based on faster region-based convolutional neural network (Faster R-CNN).Both approaches detected cancer cells with higher than 95% sensitivity and 99% specificity with the Faster R-CNN being more efficient and suitable for deployment presenting an improvement of 4% in sensitivity. The distinctive feature that CNN uses for discrimination is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations.CNN-based DL approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.