Reviewerly/exHarmony huggingface dataset:
NFTs for Open-Source and Commercial Software Licensing and Royalties

Software licenses are legal agreements of sale and usage among software developers and clients. Such legal agreements are crucial to effectively manage ownership and protect the rights of involved parties. Today’s software licensing mechanisms are mostly centralized and do not address the ever-increasing issues and complexities of modern software that may include multiple licenses, open-source distribution, rewarding other contributors of external software libraries, and utilizing royalty payments for monetization. As a result, developers have lost confidence in the existing software licensing models, and many software projects are failing due to lack of funding and royalty payments. This paper addresses such issues and complexities by proposing a novel decentralized software licensing system based on Non-Fungible Tokens (NFTs) and blockchain. The proposed licensing system is applicable to both commercial and open-source software. We use NFTs as digital tokens that encapsulate software code and their artifacts by minting them as unique valuable assets that allow developers to store and manage them on a blockchain ledger. With NFTs, developers can register and license their code, monetize it on NFT marketplaces, and earn royalties from other software projects that use their code. We present system architecture, relevant sequence diagrams, and develop aggregation algorithms for Ethereum smart contracts with ERC-1155 NFTs. Furthermore, we perform functional validation of our system and analyze the cost of its adoption. We also analyze the security of the solution and show how its applicability can be generalized and extended. We have made our smart contract code and related testing scripts publicly available on GitHub.
Robust Automatic Modulation Classification in Low Signal to Noise Ratio

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.
Two-layer path planning for multi-area coverage by a cooperative ground vehicle and drone system

A novel two-layer path planning method for a cooperative ground vehicle (GV) and drone system is investigated, where the GV and the drone are used to conduct multiple area covering tasks collaboratively. The GV acts as the mobile platform of the drone and takes the drone to visit a set of discrete areas. The drone takes off from the GV at potential nodes around target areas and scans each area for collecting information. The drone can be recharged in the GV during the time when it travels between different areas. The objective is to optimize the drone's scanning path for area coverage and the GV's travel path for visiting all areas. A 0–1 integer programming model is developed to formulate the problem. A two-stage heuristic based on cost saving strategy is designed to quickly construct a feasible solution, then the Adaptive Large Neighborhood Search (ALNS) algorithm is employed to improve the quality of the solution. A simulation experiment based on the parks in Changsha, China, is presented to illustrate the application of the method. Random instances are designed to further test the performance of the proposed algorithm.
Review of Chatbot Security Systems

Globally, technological advancements have led to innovative developments like the creation of chatbot technology. Chatbots have been created to act as virtual advisors across industries like banking, financial sector, education, hospitality, hospitals and the like. Chatbot systems suffered from threats and vulnerabilities. Spoofing someone, data manipulation, and data theft are all threats that a chatbot could bring. on the other hand, when a system is not effectively maintained, has bad coding, lacks protection, or is subject to human mistake, it becomes vulnerable. This way, chatbot security continues to be a major problem that is yet to be fully addressed. So, there is a big need to conduct thus review to study the critical review of chatbot security systems by analyzing past research studies on the subject to understand the limitations of existing chatbot technologies; and how they can be improved further. Such review would assist in determining the cumulative shortcomings of the current chatbot security systems, and help identify what the future systems need to work on to build better chatbot security systems.
Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides

Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-β deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-β-stained slides with minimal changes in preanalytic variables. In this study, we evaluated select preanalytical variables including magnification, compression rate, and storage format using three digital slides scanners (Zeiss Axioscan Z1, Leica Aperio AT2, and Leica Aperio GT 450), on over 60 whole slide images, in a cohort of 14 cases having a spectrum of amyloid-β deposits. We conducted statistical comparisons of preanalytic variables with repeated measures analysis of variance evaluating the outputs of two DL frameworks for segmentation and object classification tasks. For both WM/GM segmentation and amyloid-β plaque classification tasks, there were statistical differences with respect to scanner types (p < 0.05) and magnifications (p < 0.05). Although small numbers of cases were analyzed, this pilot study highlights the significance of preanalytic variables that may alter the performance of ML algorithms.
Locally Typical Sampling

Abstract Today’s probabilistic language generators fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics (e.g., perplexity). This discrepancy has puzzled the language generation community for the last few years. In this work, we posit that the abstraction of natural language generation as a discrete stochastic process—which allows for an information-theoretic analysis—can provide new insights into the behavior of probabilistic language generators, for example, why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, aiming to do so in a simultaneously efficient and error-minimizing manner; in fact, psycholinguistics research suggests humans choose each word in a string with this subconscious goal in mind. We formally define the set of strings that meet this criterion: Those for which each word has an information content close to the expected information content, namely, the conditional entropy of our model. We then propose a simple and efficient procedure for enforcing this criterion when generating from probabilistic models, which we call locally typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of quality while consistently reducing degenerate repetitions.
The nature of losses from cyber-related events: risk categories and business sectors

Abstract In this study, we examine the nature of losses from cyber-related events across different risk categories and business sectors. Using a leading industry dataset of cyber events, we evaluate the relationship between the frequency and severity of individual cyber-related events and the number of affected records. We find that the frequency of reported cyber-related events has substantially increased between 2008 and 2016. Furthermore, the frequency and severity of losses depend on the business sector and type of cyber threat: the most significant cyber loss event categories, by number of events, were related to data breaches and the unauthorized disclosure of data, while cyber extortion, phishing, spoofing, and other social engineering practices showed substantial growth rates. Interestingly, we do not find a distinct pattern between the frequency of events, the loss severity, and the number of affected records as often alluded to in the literature. We also analyse the severity distribution of cyber-related events across all risk categories and business sectors. This analysis reveals that cyber risks are heavy-tailed, i.e. cyber risk events have a higher probability to produce extreme losses than events whose severity follows an exponential distribution. Furthermore, we find that the frequency and severity of cyber-related losses exhibit a very dynamic and time-varying nature.
FedSEA

Federated learning (FL) has attracted increasing attention as a promising technique to drive a vast number of edge devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of a FL system in practice due to the heterogeneous computation resources on different devices. To improve the efficiency of FL systems in the real world, asynchronous FL (AFL) and semi-asynchronous FL (SAFL) methods are proposed such that the server does not need to wait for stragglers. However, existing AFL and SAFL systems suffer from poor accuracy and low efficiency in realistic settings where the data is non-IID distributed across devices and the on-device resources are extremely heterogeneous. In this work, we propose FedSEA - a semi-asynchronous FL framework for extremely heterogeneous devices. We theoretically disclose that the unbalanced aggregation frequency is a root cause of accuracy drop in SAFL. Based on this analysis, we design a training configuration scheduler to balance the aggregation frequency of devices such that the accuracy can be improved. To improve the efficiency of the system in realistic settings where the devices have dynamic on-device resource availability, we design a scheduler that can efficiently predict the arriving time of local updates from devices and adjust the synchronization time point according to the devices' predicted arriving time. We also consider the extremely heterogeneous settings where there exist extremely lagging devices that take hundreds of times as long as the training time of the other devices. In the real world, there might be even some extreme stragglers which are not capable of training the global model. To enable these devices to join in training without impairing the systematic efficiency, Fed-SEA enables these extreme stragglers to conduct local training on much smaller models. Our experiments show that compared with status quo approaches, FedSEA improves the inference accuracy by 44.34% and reduces the systematic time cost and local training time cost by 87.02× and 792.9×. FedSEA also reduces the energy consumption of the devices with extremely limited resources by 752.9×.
Exploring Rolling Shutter Effect for Motion Tracking with Objective Identification

Sensing-based user interfaces hold enormous potential for smart homes, medical equipment, educational systems, AR/VR/MR, etc. However existing hand and body gesture recognition systems are mostly based on frame-level computer vision approaches, which have limitations such as the inability to operate in the environment with low brightness, short detection distance, without the objective identification ability, and coarse-grained tracking when the objectives are in high-speed motion. Therefore, in this paper, we propose to attach active LED elements on objectives and utilize rolling shutter effect to enhance the gesture recognition and achieve the fine-grained motion tracking with objective identification.
Overlap-ADAPT-VQE: practical quantum chemistry on quantum computers via overlap-guided compact Ansätze

ADAPT-VQE is a robust algorithm for hybrid quantum-classical simulations of quantum chemical systems on near-term quantum computers. While its iterative process systematically reaches the ground state energy, ADAPT-VQE is sensitive to local energy minima, leading to over-parameterized ans\"atze. We introduce the Overlap-ADAPT-VQE to grow wave-functions by maximizing their overlap with any intermediate target wave-function that already captures some electronic correlation. By avoiding building the ansatz in the energy landscape strewn with local minima, the Overlap-ADAPT-VQE produces ultra-compact ans\"atze suitable for high-accuracy initializations of a new ADAPT procedure. Spectacular advantages over ADAPT-VQE are observed for strongly correlated systems including massive savings in circuit depth. Since this compression strategy can also be initialized with accurate Selected-Configuration Interaction (SCI) classical target wave-functions, it paves the way for chemically accurate simulations of larger systems, and strengthens the promise of decisively surpassing classical quantum chemistry through the power of quantum computing.
Neurochaos feature transformation for Machine Learning

With today’s increasing data complexity, efficient feature extraction has become an integral part of learning. In this spirit, we use features from a recently proposed brain-inspired learning algorithm for classification namely Neurochaos Learning (NL). This paper compares NL: chaos-based hybrid ML architectures to stand-alone ML/NL architectures. The maximum performance boost obtained is 25.97% (Statlog (Heart) dataset using NL features+Decision Tree) in the high training sample regime and 144.38% (Haberman’s Survival dataset using NL features+Random Forest) in the low training sample regime. NL offers enormous flexibility in integrating Neurochaos features with any ML classifier.
Low-Light Image Enhancement via Stage-Transformer-Guided Network

Images collected in low-light environments usually suffer from multiple, non-uniform distributed distortions, including local dark, dim light, backlit and so on. In this paper, we propose a Stage-Transformer-Guided Network (STGNet) that effectively handles region-specific distributions and enhance diverse low-light images. Specifically, our STGNet adopts a multi-stage way to progressively learn hierarchical features that benefit the robustness of our model. At each stage, we design an efficient transformer with horizontal and vertical attentions that jointly capture degradation distributions with different magnitudes and orientations. We also introduce learnable degradation queries to adaptively select task-specific features of degradations for enhancement. In addition, we design a histogram loss for enhancement and combine it with other loss functions, in order to exploit both global contrast and local details during network training. Benefiting from the above contributions, our STGNet achieves the state-of-the-art performances on both synthetic and real-world datasets.
A Robust Graph-Based Framework for 3-D Shape Reconstruction of Flexible Medical Instruments Using Multi-Core FBGs

This paper proposes a novel graph-based framework for 3-D shape sensing of flexible medical instruments using multi-core fiber Bragg grating (FBG) sensors. Due to noisy signals, deformability of instruments, and environmental disturbances, conventional shape sensing methods using direct FBG measurements are far from accurate and stable, especially for long devices. The localization errors will substantially accumulate with the increase of sensing lengths. To tackle this challenge, we propose a generic 3-D shape graph to optimize the entire shape of flexible instruments globally and account for the accumulative errors in both spatial and temporal domains. By leveraging the geometry configurations of FBG cores as the measurement model, a robust dynamic filtering approach is introduced for iterative curvature and twist estimation, which guarantees edge constraints of the graph-based shape optimization. Dedicated experiments are processed to validate our sensing approach in both structured and unstructured environments, where a robotic-assisted colonoscope system embedded with a multi-core FBG fiber is manipulated for the evaluations of bending as well as paths following in 3-D space. The results demonstrate the superiority of our framework as a promising solution for 3-D shape reconstruction of flexible instruments and continuum robots in terms of accuracy, robustness, and fast response compared to state-of-the-art works.
An Approach for Blockchain and Symmetric Keys Broadcast Encryption Based Access Control in IoT

This paper considers the problem of data access control when the subscribers are IoT devices with initialization that cannot be updated during the entire life cycle. A generic framework and a particular instance for conditional data access control within IoT are proposed. The generic framework is based on the employment of a dedicated secret key-based broadcast encryption scheme where encrypted credentials for conditional data access is available in the blockchain and encrypted data subject to conditional access are available in an off-chain source of streaming data. Reduction of the keys management overhead in comparison with a straightforward decryption keys delivery is experimentally illustrated. An instance of the proposed framework built over the Ethereum blockchain platform is developed and experimentally evaluated.
Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit

The in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making.Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature.Overall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model.The ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.
Smart System for Multi-Cloud Pathways

Enterprises are rapidly working on strategies to migrate their applications to cloud. Multi-cloud allows mixing and matching multiple cloud vendors when migrating thousands of applications based on their set of requirements and various different types of constraints. To utilize the advantages of different clouds, achieve maximum flexibility and avoid concentration risk, enterprises spread their applications across cloud providers. But this activity is not trivial as it would require honoring the constraints which the customer has, and at the same time generating the most optimal configuration of cloud resources.There are a few challenges associated with this. Firstly, the applications to be migrated need to be documented well, in order to migrate them successfully. Some applications may be very old (legacy) and need an architect overhaul which means the cloud feasibility needs to be checked. Also, enterprises would like to have an optimal list of cloud vendors that satisfy their need. To overcome these challenges, we propose a smart system for determining multi-cloud pathway for applications. The system identifies cloud feasible applications, understands their requirements and recommends optimal set of cloud vendors honoring their constraints. This is enabled through Reinforcement Learning with Human-in-the-Loop. We show our results with a use case from real world scenario.
ACGIS: Adversarial Cover Generator for Image Steganography with Noise Residuals Features-Preserving

Recent works with the technique of adversarial example have been bringing the possibility of effectively resisting the machine learning-based steganalyzers. Nevertheless, these methods likely introduce unexpected artifacts and destroy the statistics when adding adversarial perturbations. In this paper, under the assumption of similarity between the noise residuals of normal image sub-regions, we propose a Siamese generator to learn and preserve sub-regions noise residuals features for minimizing the impact of adversarial perturbations on similarity. The cover and stego sub-regions pairs are used as the input, which incorporates steganography domain knowledge to further encourage the generator to yield the more favorable adversarial covers. Moreover, during interactive training with steganalyzer, using a random embedding strategy to replace the specific steganographic algorithm saves training time and improves the generalization. We can employ the trained generator to produce numerous adversarial covers, cooperating with the existing steganographic methods to embed secret messages achieving much safer steganography. Security analysis and experiments show that the generated adversarial covers are superior in terms of quality and security.