Reviewerly/exHarmony huggingface dataset:
Conditional Born machine for Monte Carlo event generation

Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as a random source. So called Born machines are purely quantum models and promise to generate probability distributions in a quantum way, inaccessible to classical computers. This paper presents an application of Born machines to Monte Carlo simulations and extends their reach to multivariate and conditional distributions. Models are run on (noisy) simulators and IBM Quantum superconducting quantum hardware. More specifically, Born machines are used to generate muonic force carrier (MFC) events resulting from scattering processes between muons and the detector material in high-energy physics colliders experiments. MFCs are bosons appearing in beyond-the-standard-model theoretical frameworks, which are candidates for dark matter. Empirical evidence suggests that Born machines can reproduce the marginal distributions and correlations of data sets from Monte Carlo simulations.
Efficient Quadrature Squeezing from Biexcitonic Parametric Gain in Atomically Thin Semiconductors

Modification of electromagnetic quantum fluctuations in the form of quadrature squeezing is a central quantum resource, which can be generated from nonlinear optical processes. Such a process is facilitated by coherent two-photon excitation of the strongly bound biexciton in atomically thin semiconductors. We show theoretically that interfacing an atomically thin semiconductor with an optical cavity makes it possible to harness this two-photon resonance and use the biexcitonic parametric gain to generate squeezed light with input power an order of magnitude below current state-of-the-art devices with conventional third-order nonlinear materials that rely on far off-resonant nonlinearities. Furthermore, the squeezing bandwidth is found to be in the range of several meV. These results identify atomically thin semiconductors as a promising candidate for on-chip squeezed-light sources.
Machine learning invariants of arithmetic curves

We show that standard machine learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around 105, we demonstrate the utility of machine learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the analogous invariants of a genus 2 curve. Our results show that a trained machine can efficiently classify curves according to these invariants with high accuracies (>0.97). For problems such as distinguishing between torsion orders, and the recognition of integral points, the accuracies can reach 0.998.
Classification of Dynamic Hand Gestures using Multi Sensors Combinations

Medical applications in general and hand gestures classification-based rehabilitation applications specifically are highly dependent on the choice of essential parameters such as the set of gestures and the number and type of used sensors among others. Aiming for medically satisfying results, the choice of the artificial intelligence algorithm to implement, and the choice of the data set to investigate, are critical to making. Different types of signals can be collected while preparing the data set, such as force myography (FMG), surface electromyography (sEMG), and body’s specific force, angular rate, and orientation, using a combination of accelerometers, gyroscopes, and magnetometers as Inertial Measurements Units (IMU). To ensure dynamic hand gesture identification, this paper proposes to investigate the combination of these three types of channels in one embedded system placed on the subject’s hand. The whole system contains two sEMG sensors, four strain sensors in one hand glove, eight pressure sensors fixed in one armband, and one 9 axis IMU sensor placed on the hand. Ten different dynamic hand gestures, collected from one healthy participant, have been performed twenty times using the left hand. For the classification process, three machine learning approaches namely k-Nearest Neighbors (kNN), Decision Tree (DT), and Random Forest (RF) are implemented. Different combinations of sensors from the system are investigated to test their relevance for the collected gestures classification. The algorithms are evaluated based on the classification accuracy, training time, and testing time and used as the sensor relevance indicator. Results show that using the whole system results in the best accuracy up to 96.12% and 99.55% with DT and RF, respectively.
Object Recognition Network using Continuous Roadside Cameras

For the purpose of establishing a digital twin society, this paper proposes an object recognition network architecture using high-definition (HD) camera images. HD camera is installed to the edge computing resource in Road-Side Unit (RSU). The edge computing resource detects vehicles by object detection and Re-ID function based on deep learning. Its key feature is to reduce the amount of network data traffic by extracting vehicle images and directly transferring them to neighboring RSUs, i.e. vehicle identification and tracking can be achieved in the localized backhaul network. This paper experimentally verifies its fundamental feasibility using outdoor testbed field. The proposed framework can significantly reduce the data traffic by more than 90% while maintaining vehicle Re-ID accuracy.
Influence of Internet of things on human psychology (internet of thoughts) for education, healthcare, and businesses

INTRODUCTION: The study investigated how technological innovations and Embedded Systems, processes, and Communication tools influence human psychology. The IoTs have succeeded to connect humans with things, and it is now preoccupied with understanding how humans react to these things by influencing our actions, reactions, and behavior.&#x0D; OBJECTIVES: To identify items that IoTs use to influence human psychology in education, healthcare, and business and to find out how IoTs utilize AI, ML, Cloud computing, NLP, ICT tools, and big data technology to overpower human psychology.&#x0D; METHODS: The methods “Behavior-oriented drive and influential function method” and the qualitative-quantitative method were used. The qualitative-quantitative method makes use of 3 research questions while behavior-oriented drive and influential function focus on statistical data.&#x0D; RESULTS: The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function and above 90% positive respondents for the qualitative-quantitative method.&#x0D; CONCLUSION: The study concluded that the high dependency of humans on IoTs services is the reason for the IoTs influence on our thoughts, actions, and behavior.
Geological mapping and mining prospecting in the Aouli inlier (Eastern Meseta, Morocco) based on remote sensing and geographic information systems (GIS)

Landsat 8 Oli, ASTER, and Sentinel 2A satellite images processing was used to map geological formations, lineaments and hydrothermal alteration minerals in the Aouli inlier, as a case study to illustrate the application of digital images processing and Geographic Information System (GIS) in geological mapping and mining prospecting. Principal Component Analysis (PCA) applied to the Landsat images allowed good lithological discrimination and contributed to the updating of available geological maps. The Automatic lineament extraction from Sentinel images revealed the main tectonic structures affecting Aouli inlier. The ratio bands (b5+b7)/b6 and the false color composite (b4/b6, b2/b1, b3/b2) allowed the hydrothermal alteration minerals mapping from Aster images. Combined with available geological data and field observations, the satellite derived data were integrated and analyzed in a GIS software to establish mining prospecting guides. The results showed that the anomaly zones are intimately linked to NNE–SSW and NW–SE oriented faults and to highly fractured areas developing argillic and Fe rich alterations. Verified via field survey, this approach was successfully applied to the Aouli inlier to rapidly target potential areas to be explored in the tactical phase. This provides a model for future prospecting efforts for similar mineral deposits in other areas.
Organ Bank Based on Blockchain

The organ donation and transplant numbers differ in large numbers. The main reason for the difference is the number of donations that are performed compared to the number of organs being transplanted. A total of 7453 transplants were completed in the year 2020 whereas the number of patients who required transplants was over 2 lakhs. This significant difference is mainly due to the unawareness in the people of the importance of organ donation. On top of this, the black marketing of the organs is another major aspect which has led to an unorganized distribution of the organs. The existing system relies upon the central database system which includes the problems of reliability, immutability of the records and security of the data. The central database system lacks the validation methods for checking if the data has been modified or changed since they are the single source of information. This paper aims to solve the problem by using blockchain technology to store the records of the organs which can be tracked if any changes are occurred and stop the potential black marketing of the organs by verifying all the records that have been entered. The performance of the system will be measured based on the average response time of the system and the gas money used by the Ethereum Smart Contracts. We have found very low response times and lower cost of maintenance for the smart contacts, which shows that this approach can be used in the real-time organ transplant network.
How Not to Handle Keys: Timing Attacks on FIDO Authenticator Privacy

This paper presents a timing attack on the FIDO2 (Fast IDentity Online) authentication protocol that allows attackers to link user accounts stored in vulnerable authenticators, a serious privacy concern. FIDO2 is a new standard specified by the FIDO industry alliance for secure token online authentication. It complements the W3C WebAuthn specification by providing means to use a USB token or other authenticator (which holds the secret authenticating material and implements FIDO protocols) as a second factor during the authentication process. From a cryptographic perspective, the protocol is a simple challenge-response where the elliptic curve digital signature algorithm is used to sign challenges. To protect the privacy of the user the token uses unique key pairs per service. To accommodate for small memory, tokens use various techniques that make use of a special parameter called a key handle sent by the service to the token with which the token can securely produce an authentication key (through generation or decryption). We identify and analyse a vulnerability in the way the processing of key handles is implemented that allows attackers to remotely link user accounts on multiple services. We show that for vulnerable authenticators there is a difference between the time it takes to process a key handle for a different service but correct authenticator, and for a different authenticator but correct service. This difference can be used to perform a timing attack allowing an adversary to link user’s accounts across services. We present several real world examples of adversaries that are in a position to execute our attack and can benefit from linking accounts. We found that two of the eight hardware authenticators we tested were vulnerable despite FIDO level 1 certification, indicating a not insignificant problem. This vulnerability cannot be easily mitigated on authenticators because, for security reasons, they usually do not allow firmware updates. In addition, we show that due to the way existing browsers implement the WebAuthn standard, the attack can be executed remotely. However, we discuss countermeasures that can be implemented by browser providers to mitigate the remote form of the attack.
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer

In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for raw image restoration. ELMformer contains two core designs especially for raw images whose primitive attribute is single-channel. The first design is a Bi-directional Fusion Projection (BFP) module, where we consider both the color characteristics of raw images and spatial structure of single-channel. The second one is that we propose a Locally Multiplicative Self-Attention (L-MSA) scheme to effectively deliver information from the local space to relevant parts. ELMformer can efficiently reduce the computational consumption and perform well on raw image restoration tasks. Enhanced by these two core designs, ELMformer achieves the highest performance and keeps the lowest FLOPs on raw denoising and raw deblurring benchmarks compared with state-of-the-arts. Extensive experiments demonstrate the superiority and generalization ability of ELMformer. On SIDD benchmark, our method has even better denoising performance than ISP-based methods which need huge amount of additional sRGB training images. The codes are release at https://github.com/leonmakise/ELMformer.
A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems

In this paper, a new swarm intelligence algorithm based on orca behaviors is proposed for problem solving. The algorithm called artificial orca algorithm (AOA) consists of simulating the orca lifestyle and in particular the social organization, the echolocation mechanism, and some hunting techniques. The originality of the proposal is that for the first time a meta-heuristic simulates simultaneously several behaviors of just one animal species. AOA was adapted to discrete problems and applied on the maze game with four level of complexity. A bunch of substantial experiments were undertaken to set the algorithm parameters for this issue. The algorithm performance was assessed by considering the success rate, the run time, and the solution path size. Finally, for comparison purposes, the authors conducted a set of experiments on state-of-the-art evolutionary algorithms, namely ACO, BA, BSO, EHO, PSO, and WOA. The overall obtained results clearly show the superiority of AOA over the other tested algorithms.
Probabilistic volcanic hazard assessment at an active but under-monitored volcano: Ceboruco, Mexico

Abstract A probabilistic volcanic hazard assessment (PVHA) for Ceboruco volcano (Mexico) is reported using PyBetVH, an e-tool based on the Bayesian Event Tree (BET) methodology. Like many volcanoes, Ceboruco is under-monitored. Despite several eruptions in the late Holocene and efforts by several university and government groups to create and sustain a monitoring network, this active volcano is monitored intermittently rather than continuously by dedicated groups. With no consistent monitoring data available, we look at the geology and the eruptive history to inform prior models used in the PVHA. We estimate the probability of a magmatic eruption within the next time window (1 year) of ~ 0.002. We show how the BET creates higher probabilities in the absence of monitoring data, which if available would better inform the prior distribution. That is, there is a cost in terms of higher probabilities and higher uncertainties for having not yet developed a sustained volcano monitoring network. Next, three scenarios are developed for magmatic eruptions: i) small magnitude (effusive/explosive ), ii) medium magnitude (Vulcanian/sub-Plinian) and iii) large magnitude (Plinian). These scenarios are inferred from the Holocene history of the volcano, with their related hazardous phenomena: ballistics, tephra fallout, pyroclastic density currents, lahars and lava flows. We present absolute probability maps (unconditional in terms of eruption size and vent location) for a magmatic eruption at Ceboruco volcano. With PyBetVH we estimate and visualize the uncertainties associated with each probability map. Our intent is that probability maps and uncertainties will be useful to local authorities who need to understand the hazard when considering the development of long-term urban and land-use planning and short-term crisis management strategies, and to the scientific community in their efforts to sustain monitoring of this active volcano.
Object classification on video data of meteors and meteor-like phenomena: algorithm and data

ABSTRACT Every moment, countless meteoroids enter our atmosphere unseen. The detection and measurement of meteors offer the unique opportunity to gain insights into the composition of our solar systems’ celestial bodies. Researchers therefore carry out a wide-area-sky-monitoring to secure 360-degree video material, saving every single entry of a meteor. Existing machine intelligence cannot accurately recognize events of meteors intersecting the earth’s atmosphere due to a lack of high-quality training data publicly available. This work presents four reusable open source solutions for researchers trained on data we collected due to the lack of available labelled high-quality training data. We refer to the proposed data set as the NightSkyUCP data set, consisting of a balanced set of 10 000 meteor- and 10 000 non-meteor-events. Our solutions apply various machine-learning techniques, namely classification, feature learning, anomaly detection, and extrapolation. For the classification task, a mean accuracy of 99.1 per cent is achieved. The code and data are made public at figshare with DOI 10.6084/m9.figshare.16451625.
The effective tasks management of workflows inspired by NIM-game strategy in smart grid environment

In smart grid environment incorporated with IoT-based cloud infrastructure, management of efficiency is a paradigm. This procedure considers an adversary search technique (ADVST). It approaches the MINIMAX algorithm on virtual machines for management of workflows in IoT-based cloud infrastructure, which is named as a VMMINMAX algorithm. The algorithm finds the over-loaded-tasks group named as VMMAXIMISER. The under-loaded-tasks group named as VMMINIMISER and the balanced-tasks group named as VMBALANCED. The VMMINMAX algorithm is used for effective tasks management strategy, and it can be achieved, by moving the workflows from heavily-loaded-VMs to low-loaded-VMs. It balances over VMs as VMBALANCED and reduces the non-critical-workflow-tasks and effective allocation of critical-workflow-tasks, with respect to make-span and average make-span of VMs. The algorithm has been tested with different parameters such as throughput, overhead, resource utilisation, response time, scalability and performance and proved.
Widening or narrowing inequalities? The equity implications of digital tools to support COVID‐19 contact tracing: A qualitative study

As digital tools are increasingly used to support COVID-19 contact tracing, the equity implications must be considered. As part of a study to understand the public's views of digital contact tracing tools developed for the national 'Test and Protect' programme in Scotland, we aimed to explore the views of groups often excluded from such discussions. This paper reports on their views about the potential for contact tracing to exacerbate inequalities.A qualitative study was carried out; interviews were conducted with key informants from organizations supporting people in marginalized situations, followed by interviews and focus groups with people recruited from these groups. Participants included, or represented, minority ethnic groups, asylum seekers and refugees and those experiencing multiple disadvantage including severe and enduring poverty.A total of 42 people participated: 13 key informants and 29 members of the public. While public participants were supportive of contact tracing, key informants raised concerns. Both sets of participants spoke about how contact tracing, and its associated digital tools, might increase inequalities. Barriers included finances (inability to afford smartphones or the data to ensure access to the internet); language (digital tools were available only in English and required a degree of literacy, even for English speakers); and trust (many marginalized groups distrusted statutory organizations and there were concerns that data may be passed to other organizations). One strength was that NHS Scotland, the data guardian, is seen as a generally trustworthy organization. Poverty was recognized as a barrier to people's ability to self-isolate. Some participants were concerned about giving contact details of individuals who might struggle to self-isolate for financial reasons.The impact of contact tracing and associated digital tools on marginalized populations needs careful monitoring. This should include the contact tracing process and the ability of people to self-isolate. Regular clear messaging from trusted groups and community members could help maintain trust and participation in the programme.Our patient and public involvement coapplicant, L. L., was involved in all aspects of the study including coauthorship. Interim results were presented to our local Public and Patient Involvement and Engagement Group, who commented on interpretation and made suggestions about further recruitment.
New Blockchain Based Special Keys Security Model With Path Compression Algorithm for Big Data

In recent years, following the introduction of the IoT (Internet of Things) into our lives and thanks to the rapidly increasing number of digital applications, data is collected from a wide variety of sources at an astonishing rate, and the amount of data is increasing exponentially. Today, social networks, cloud computing and data analytics make it possible to collect huge amounts of data. The concept of big data has subsequently emerged and is an important topic in many fields. However, it is not only very difficult to store big data and analyze it, but it is also a serious threat to the security of an individual’s sensitive information. This study describes the issues surrounding big data security and privacy, and provides a solution involving a new blockchain-based security model. This proposed model is called the Blockchain-based Special Key Security Model (BSKM). BSKM proposes, implements and integrates three elements (confidentiality, integrity and availability) of information security together for big data. With this proposed model, a more practical and flexible structure is established for all operations (read, write, update and delete) performed on a database with real data. In this study performed with a special key, all separate blockchain transactions were used for read, write, update and delete operations, and there was a structure that could ensure both confidentiality and integrity at the same time. By looking at a special key for all the blockchain transaction operations performed on the big data it has been shown what type of authorization and access control can be established between which processes and which users. Thus, in contrast to previous studies seen in published literature, data confidentiality, data integrity and data consistency were guaranteed for all transactions. The results of the proposed BSKM model have also been compared by conducting an experimental study of its application. Moreover, this study has shown the effectiveness and benefits of the path compression algorithm. This result has been shown with experimental studies modeling big data and also shows promise for further studies.