Reviewerly/exHarmony huggingface dataset:
5G mobile networks: reviewing security control correctness for mischievous activity

Abstract A mobile telecommunications network has arguably become a vital part of today’s critical communications infrastructure underpinning society’s interconnectedness. A mobile telecommunications network can be considered a critical communications infrastructure that has been built upon a complex set of network technologies. However, the migration in recent years from pre-5G to 5G network technologies has presented the mobile telecommunications network operators with not only several security-related challenges but also potential unfortunate risk exposure. A new approach called Control-Risk-Correctness (CRC) addresses the need for evaluating a complex mix of network technology and the associated trade-offs between security and risk. CRC simplifies the analysis by examining the mobile telecommunications network from the perspective of security control effectiveness and risk treatments. This article outlines the application of CRC when assessing a mobile telecommunication network and highlights direct risk mitigation treatments in an aim to increase security control effectiveness and decrease risk exposure. CRC usefulness will assist in the evaluation of existing networks and safeguarding new networks over the coming years.
Effective Multi-Object Tracking via Global Object Models and Object Constraint Learning

Effective multi-object tracking is still challenging due to the trade-off between tracking accuracy and speed. Because the recent multi-object tracking (MOT) methods leverage object appearance and motion models so as to associate detections between consecutive frames, the key for effective multi-object tracking is to reduce the computational complexity of learning both models. To this end, this work proposes global appearance and motion models to discriminate multiple objects instead of learning local object-specific models. In concrete detail, it learns a global appearance model using contrastive learning between object appearances. In addition, we learn a global relation motion model using relative motion learning between objects. Moreover, this paper proposes object constraint learning for improving tracking efficiency. This study considers the discriminability of the models as a constraint, and learns both models when inconsistency with the constraint occurs. Therefore, object constraint learning differs from the conventional online learning for multi-object tracking which updates learnable parameters per frame. This work incorporates global models and object constraint learning into the confidence-based association method, and compare our tracker with the state-of-the-art methods on public available MOT Challenge datasets. As a result, we achieve 64.5% MOTA (multi-object tracking accuracy) and 6.54 Hz tracking speed on the MOT16 test dataset. The comparison results show that our methods can contribute to improve tracking accuracy and tracking speed together.
LBSS: A Lightweight Blockchain-Based Security Scheme for IoT-Enabled Healthcare Environment

Recently, global healthcare has made great progress with the use of Internet of Things technology. However, for there to be excellent patient care, there must be a high degree of safety for the IoT health system. There has been a massive increase in hacking systems and the theft of sensitive and highly confidential information from large health centers and hospitals. That is why establishing a highly secure and reliable healthcare system has become a top priority. In this paper, a security scheme for the IoT-enabled healthcare environment, LBSS, is proposed. This security scheme comprises three security mechanisms. The first mechanism is based on the blockchain technology and is used for transaction integrity. The second mechanism is used to store the healthcare system data in a secure manner through the distribution of its data records among multiple servers. The third mechanism is used to access the healthcare data after applying a proposed authorization test. To minimize the security overhead, the healthcare data is prioritized in regard to its importance. Therefore, each security mechanism has specific steps for each level of data importance. Finally, the NS3 package is used to construct a simulation environment for IoT-enabled healthcare systems to measure the proposed security scheme performance. The simulation results proved that the proposed healthcare security scheme outperformed the traditional models in regard to the performance metrics.
Universal method to estimate quantum coherence

Coherence is a defining property of quantum theory that accounts for quantum advantage in many quantum information tasks. Although many coherence quantifiers have been introduced in various contexts, the lack of efficient methods to estimate them restricts their applications. In this paper, we tackle this problem by proposing one universal method to provide measurable bounds for most current coherence quantifiers. Our method is motivated by the observation that the distance between the state of interest and its diagonal parts in reference basis, which lies at the heart of the coherence quantifications, can be readily estimated by disturbance effect and uncertainty of the reference measurement. Thus, our method of bounding coherence provides a feasible and broadly applicable avenue for detecting coherence, facilitating its further practical applications.
Low-Dose COVID-19 CT Image Denoising Using Batch Normalization and Convolution Neural Network

Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature.
MLSMBQS: Design of a Machine Learning Based Split &amp; Merge Blockchain Model for QoSAware Secure IoT Deployments

Internet of Things (IoT) Networks are multitier deployments which assist on-field data to be sensed, processed, communicated, and used for taking control decisions.These deployments utilize hardware-based components for data sensing & actuation, while cloud components are used for data-processing & recommending control decisions.This process involves multiple low-security, low-computational capacity & high-performance entities like IoT Devices, short range communication interfaces, edge devices, routers, & cloud virtual machines.Out of these entities, the IoT Device, router, & short-range communication interfaces are highly vulnerable to a wide-variety of attacks including Distributed Denial of Service (DDoS), worm hole, sybil, Man in the Middle (MiTM), Masquerading, spoofing attacks, etc.To counter these attacks, a wide variety of encryption, key-exchange, and data modification models are proposed by researchers.Each of these models have their own levels of complexities, which reduces QoS of underlying IoT deployments.To overcome this limitation, blockchain-based security models were proposed by researchers, and these models allow for high-speed operations for small-scale networks.But as network size is increased, delay needed for blockchain mining increases exponentially, which limits its applicability.To overcome this issue, a machine learning based blockchain model for QoS-aware secure IoT deployments is proposed in this text.The proposed MLSMBQS model initially deploys a Proof-of-Work (PoW) based blockchain model, and then uses bioinspired computing to split the chain into multiple sub-chains.These sub-chains are termed as shards, and assists in reduction of mining delay via periodic chain splitting process.The significance of this research is use of Elephant Herd Optimization (EHO) which assists in managing number of blockchain-shards via splitting or merging them for different deployment conditions.This decision of splitting or merging depends on blockchain's security & quality of service (QoS) performance.Due to integration of EHO for creation & management of sidechains, the findings of this research showcase that the proposed model is capable of improving throughput by 8.5%, reduce communication delay by 15.3%, reduce energy consumption by 4.9%, and enhance security performance by 14.8% when compared with existing blockchain & non-blockchain based security models.This is possible because EHO initiates dummy communication requests, which are arbitrarily segregated into malicious & non-malicious, and usedfor continuous QoS & security performance improvement of the proposed model.Due to this continuous performance improvement, the proposed MLSMBQS model is capable of deployment for a wide variety of high-efficiency IoT network scenarios.
The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing

Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly predict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal—those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory (Hale, 2001; Levy, 2008), which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms.
Rigorous convergence condition for quantum annealing

We derive a generic bound on the rate of decrease of transverse field for quantum annealing to converge to the ground state of a generic Ising model when quantum annealing is formulated as an infinite-time process. Our theorem is based on a rigorous upper bound on the excitation probability in the infinite-time limit and is a mathematically rigorous counterpart of a previously known result derived only from the leading-order term of the asymptotic expansion of adiabatic condition. Since our theorem gives a sufficient condition of convergence for a generic transverse-field Ising model, any specific problem may allow a better, faster, control of the coefficient.
Hiding and Extracting Important Information in Encrypted Images by Using the Transformation of All Possible Permutations and VQ Codebook

Due to its applications in cloud computing, research on reversible data hiding in encrypted images (RDHEI) is becoming more and more important. This paper proposes a reversible data hiding scheme for encrypted images that utilizes an all-permutation technique to embed data into encrypted images. The proposed scheme follows a block-wise data hiding process. Message extraction and image restoration are performed by the receiver using the trained vector quantization (VQ) codebook. This scheme can provide a high embedding rate and reduce the hardware burden on the receiver.
Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data

When enabled by machine learning (ML), Learning Health Systems (LHS) hold promise for improving the effectiveness of healthcare delivery to patients. One major barrier to LHS research and development is the lack of access to EHR patient data. To overcome this challenge, this study demonstrated the feasibility of developing a simulated ML-enabled LHS using synthetic patient data. The ML-enabled LHS was initialized using a dataset of 30,000 synthetic Synthea patients and a risk prediction XGBoost base model for lung cancer. 4 additional datasets of 30,000 patients were generated and added to the previous updated dataset sequentially to simulate addition of new patients, resulting in datasets of 60,000, 90,000, 120,000 and 150,000 patients. New XGBoost models were built in each instance, and performance improved with data size increase, attaining 0.936 recall and 0.962 AUC (area under curve) in the 150,000 patients dataset. The effectiveness of the new ML-enabled LHS process was verified by implementing XGBoost models for stroke risk prediction on the same Synthea patient populations. By making the ML code and synthetic patient data publicly available for testing and training, this first synthetic LHS process paves the way for more researchers to start developing LHS with real patient data.
Is virtual reality the solution? A comparison between 3D and 2D creative sketching tools in the early design process

Creativity is key in the early phases of innovation processes. With the rapid evolution of technologies, designers now have access to various tools to support this activity. Virtual reality (VR) takes over multiple domains, especially during conception. However, is VR really facilitating creativity in the initial ideation phases? We compare two sketching modalities through dedicated creativity support tools (CSTs): one in VR and one on a 2D interactive whiteboard. We propose a two-part creativity task (divergent and convergent thinking) for two groups of 30 participants each. We record user experience, creative experience, and creative performance. Our results show that VR is more stimulating, attractive, and engaging. We also observe a better level of creativity for the participants using the VR CST. Our results indicate that VR is an effective and relevant tool to boost creativity and that this effect might carry over to following creative tasks.
MiPo: How to Detect Trajectory Outliers with Tabular Outlier Detectors

Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular features from trajectory data prior to the application of conventional outlier detectors to detect trajectory outliers. By representing variable-length trajectory data as fixed-length tabular data, MiPo granted tabular outlier detectors the ability to detect trajectory outliers, which was previously impossible. Experiments with real-world datasets showed that MiPo outperformed all baseline methods with 0.99 AUC on average; however, it only required approximately 10% of the computing time of the existing industrial best. MiPo exhibited linear time and space complexity. The features extracted by MiPo may aid other trajectory data mining tasks. We believe that MiPo has the potential to revolutionize the field of trajectory outlier detection.
Detection of Anomaly using Machine Learning: A Comprehensive Survey

Anomaly detection is an important element in the domain of security. As a result, we undertook a literature review on ML algorithms that identify abnormalities. In this paper, we are presenting a review of the 101 research articles describing ML techniques for anomaly detection published between 2015 - 2022.The goal of this paper is to review research papers that have used machine learning to develop anomaly detection algorithmThe forms of anomaly detection examined in this study include system log anomaly detection, network anomaly detection, cloud-based anomaly detection, and anomaly detection in the medical profession. After assessing the selected research articles, we present more than 10 applications of anomaly detection. Also, we have shared a range of datasets used in anomaly detection research, in addition to revealing 30+ new ML models employed in anomaly detection. We have discovered 55 new datasets for anomaly detection. We've noticed that the majority of researchers utilize real-life datasets and an unsupervised learning technique to detect anomalies. Many ML methods may be applied in this subject, so we present a summary of all work done in the previous six years. Keywords Intrusion detection, Artificial intelligence, Anomaly detection, security, Machine learning.
Simple Lightweight Cryptographic Algorithm to Secure Imbedded IoT Devices

The internet of things (IoT) revolution has been sparked by the exponential increase in connected devices caused by recent advances in wireless technology. These embedded devices gather, analyze, and send vast data via the network. Data transmission security is a primary problem in IoT networks. Several lightweight encryption/decryption algorithms have been developed for such resource-constraint devices. One of most effective and fast lightweight encryption algorithms for IoT applications is the Tiny Encryption Algorithm (TEA). TEA has few lines source of codes to implement and based on Feistel structure to provide cryptographic primitive confusion and diffusion features in order to hide statistical aspects of plaintext. However, it is vulnerable to assaults using equivalent and related key attacks. This study suggested modifying TEA by employing a new proposed generating keys function using two Linear Feedback Shift Registers (LFSRs) as a combination to address the security flaw caused by utilizing different keys for each round function. The key sensitivity, Avalanche effect, and a completeness test were used to evaluate its security performance. The key sensitivity of the proposed modified TEA outperforms original TEA by 50.18 % to 44.88 %. The modified TEA avalanche effect outperforms TEA by 52.57 % to 47.69 %, and its completeness test outperforms TEA by 51.75 % to 48.36 %. Experimental results indicates that, the encryption performance of proposed modified TEA is better than original TEA.
On physics-informed neural networks for quantum computers

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks. One of the advantages of using PINN is to leverage the usage of Machine Learning computational frameworks relying on the combined usage of CPUs and co-processors, such as accelerators, to achieve maximum performance. This work investigates the design, implementation, and performance of PINNs, using the Quantum Processing Unit (QPU) co-processor. We design a simple Quantum PINN to solve the one-dimensional Poisson problem using a Continuous Variable (CV) quantum computing framework. We discuss the impact of different optimizers, PINN residual formulation, and quantum neural network depth on the quantum PINN accuracy. We show that the optimizer exploration of the training landscape in the case of quantum PINN is not as effective as in classical PINN, and basic Stochastic Gradient Descent (SGD) optimizers outperform adaptive and high-order optimizers. Finally, we highlight the difference in methods and algorithms between quantum and classical PINNs and outline future research challenges for quantum PINN development.
Capturing Conversational Gestures for Embodied Conversational Agents Using an Optimized Kaneda–Lucas–Tomasi Tracker and Denavit–Hartenberg-Based Kinematic Model

In order to recreate viable and human-like conversational responses, the artificial entity, i.e., an embodied conversational agent, must express correlated speech (verbal) and gestures (non-verbal) responses in spoken social interaction. Most of the existing frameworks focus on intent planning and behavior planning. The realization, however, is left to a limited set of static 3D representations of conversational expressions. In addition to functional and semantic synchrony between verbal and non-verbal signals, the final believability of the displayed expression is sculpted by the physical realization of non-verbal expressions. A major challenge of most conversational systems capable of reproducing gestures is the diversity in expressiveness. In this paper, we propose a method for capturing gestures automatically from videos and transforming them into 3D representations stored as part of the conversational agent's repository of motor skills. The main advantage of the proposed method is ensuring the naturalness of the embodied conversational agent's gestures, which results in a higher quality of human-computer interaction. The method is based on a Kanade-Lucas-Tomasi tracker, a Savitzky-Golay filter, a Denavit-Hartenberg-based kinematic model and the EVA framework. Furthermore, we designed an objective method based on cosine similarity instead of a subjective evaluation of synthesized movement. The proposed method resulted in a 96% similarity.
An Efficient Information Retrieval System Using Evolutionary Algorithms

When it comes to web search, information retrieval (IR) represents a critical technique as web pages have been increasingly growing. However, web users face major problems; unrelated user query retrieved documents (i.e., low precision), a lack of relevant document retrieval (i.e., low recall), acceptable retrieval time, and minimum storage space. This paper proposed a novel advanced document-indexing method (ADIM) with an integrated evolutionary algorithm. The proposed IRS includes three main stages; the first stage (i.e., the advanced documents indexing method) is preprocessing, which consists of two steps: dataset documents reading and advanced documents indexing method (ADIM), resulting in a set of two tables. The second stage is the query searching algorithm to produce a set of words or keywords and the related documents retrieving. The third stage (i.e., the searching algorithm) consists of two steps. The modified genetic algorithm (MGA) proposed new fitness functions using a cross-point operator with dynamic length chromosomes with the adaptive function of the culture algorithm (CA). The proposed system ranks the most relevant documents to the user query by adding a simple parameter (∝) to the fitness function to guarantee the convergence solution, retrieving the most relevant user’s document by integrating MGA with the CA algorithm to achieve the best accuracy. This system was simulated using a free dataset called WebKb containing Worldwide Webpages of computer science departments at multiple universities. The dataset is composed of 8280 HTML-programed semi-structured documents. Experimental results and evaluation measurements showed 100% average precision with 98.5236% average recall for 50 test queries, while the average response time was 00.46.74.78 milliseconds with 18.8 MB memory space for document indexing. The proposed work outperforms all the literature, comparatively, representing a remarkable leap in the studied field.