Reviewerly/exHarmony huggingface dataset:
Experimental evaluation of quantum Bayesian networks on IBM QX hardware

Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware.
A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.
A Blockchain Traceable Scheme with Oversight Function

Many blockchain researches focus on the privacy protection. However, criminals can leverage strong privacy protection of the blockchain to do illegal crimes (such as ransomware) without being punished. These crimes have caused huge losses to society and users. Implementing identity tracing is an important step in dealing with issues arising from privacy protection. In this paper, we propose a blockchain traceable scheme with oversight function (BTSOF). The design of BTSOF builds on SkyEye (Tianjun Ma et al., Cryptology ePrint Archive 2020). In BTSOF, the regulator must obtain the consent of the committee to enable tracing. Moreover, we construct a non-interactive verifiable multi-secret sharing scheme (VMSS scheme) and leverage the VMSS scheme to design a distributed multi-key generation (DMKG) protocol for the Cramer-Shoup public key encryption scheme. The DMKG protocol is used in the design of BTSOF. We provide the security definition and security proof of the VMSS scheme and DMKG protocol.
Development of a Context-Aware Assistive System for Manual Repair Processes - A Combination of Probabilistic and Deterministic Approaches

Looking at the trend of mass customization of products, companies are confronted with an increasing number of different products and product variants. Especially for humans working in assembly and rework domain it is increasingly difficult to maintain an overview of all assembly paths and work steps. To tackle this problem, context aware assistance systems are introduced to the field. Although there is a lot of research in the area of context aware assistive systems, most work focuses on fixed work plans or purely probability-based activity recognition. As a result, these systems either restrict the worker’s personal way of working or the modelling of the work plans is complex and time-consuming. The goal of our approach is a context sensitive support for all performable steps at a given time. That system requires an intuitive modelling of work processes including an activity recognition. Therefore, we present a process model consisting of the combination of a petri net and aspects of Hidden Markov Models (HMM). Based on the modelled work process, the system determines performable work steps at the given time, while the activity recognition selects the most likely work step on the subset of feasible work steps. In this paper we describe the structure of the process model and how petri nets and approaches from the area of HMM are combined in this model. The outlook shows an implementation approach for the model.
The surface expression of hydrocarbon seeps characterized by satellite image spectral analysis and rock magnetic data (Falcon basin, western Venezuela)

Satellite images have been widely used to characterize mineral alteration zones in surface rocks affected by hydrocarbons' upward seepage. Likewise, magnetic surveys over oil ﬁelds have been employed for similar purposes. This work integrates satellite image spectral analyses with rock magnetic and geochemical data (i.e., mass-specific magnetic susceptibility χ, saturation isothermal remanent magnetization SIRM, analysis of SIRM acquisition curves, absorption spectro-radiometry, and X-ray diffraction analyses). The target area was an oil-prospective region in northwestern Venezuela (Falcon Basin) swarmed by hydrocarbon seeps. The study's main goal was to depict the spatial extension and vertical reach of the hydrocarbon-mediated alteration produced by these seeps and gain some knowledge about the processes involved in the subsequent mineral changes. Multispectral and hyperspectral satellite images showed three hydrocarbon-induced diagenetic anomalies (HIDAs) associated with undifferentiated clays and kaolinite, and low dolomite content. A simple weathering model was suggested combining the likely effects of the ascending seepage of oil and gas with the seasonal alternation of leaching and evaporation of meteoric waters. This model explains the magnetic enhancement with depth due to Fe oxides and sulfides' formation by reducing and oxidizing events. A non-supervised Two-Step Cluster Analysis (TSCA) was applied to integrate geochemical and rock magnetic properties with satellite images. The input variables were log SIRM and proxies of undifferentiated clays and dolomite concentrations obtained from the band-ratio ASTER images. The TSCA yields three clusters associated with different alteration levels in the top and low (0.3–1 m) soils and sediments. By mapping the class membership of each sampling site, for both depth levels, it was possible to obtain a broad view of the synergistic change of these combined properties, not only over the whole extent of the study area but also from top to bottom of the weathered sequence. This case study illustrates the potential of such an integrated method as an oil exploration tool, and a means to assess the level and scope of the environmental impact produced by hydrocarbon seepage on terrestrial ecosystems.
The role of titles in enhancing data visualization

Many in the data visualization and evaluation communities recommend conveying the message or takeaway of the visualization in the visualization's title. This study tested that recommendation by examining how informative or generic titles impact a visualization’s visual efficiency, aesthetics, credibility, and the perceived effectiveness of the hypothetical program examined. Furthermore, this study tested how simple or complex graphs, and positive, negative, or mixed results (i.e., valence of the results) affected outcomes. Participants were randomly assigned to one of 12 conditions, representing a 2 (graph: simple or complex) x 2 (title: generic or informative) x 3 (valence: positive, negative, mixed) between-subjects study. The results indicated that informative titles required less mental effort and were viewed as more aesthetically pleasing, but otherwise did not lead to greater accuracy, credibility, or perceived effectiveness. Furthermore, titles did not interact with graph type or the valence of the findings. While the results suggest it is worthwhile to consider adding an informative title to data visualizations as they can reduce mental effort for the viewer, the intended goal of the visualization should be taken into consideration. Considering the goal of the visualization can be a deciding factor of the type of graph and title that will best serve its intended purposes. Overall, this suggests that data visualization recommendations that impact evaluation reporting practices should be scrutinized more closely through research.
Quantum correlated coherence and Hellinger distance in the critical systems

In this paper, we explore the quantum phase transitions in the XY chain and the XXZ model. The quantum correlated coherence has been introduced by Tan and Jeong [Phys. Rev. Lett. 121, 220401 (2018)]. We utilize the quantum correlated coherence to investigate the quantum critical systems. We also demonstrate the Hellinger distance which shows the quantum critical behaviors. The numerical results are presented and analyzed in details.
Novel Convergence Results of Adaptive Stochastic Gradient Descents

Adaptive stochastic gradient descent, which uses unbiased samples of the gradient with stepsizes chosen from the historical information, has been widely used to train neural networks for computer vision and pattern recognition tasks. This paper revisits the theoretical aspects of two classes of adaptive stochastic gradient descent methods, which contain several existing state-of-the-art schemes. We focus on the presentation of novel findings: In the general smooth case, the nonergodic convergence results are given, that is, the expectation of the gradients' norm rather than the minimum of past iterates is proved to converge; We also studied their performances under Polyak-Łojasiewicz property on the objective function. In this case, the nonergodic convergence rates are given for the expectation of the function values. Our findings show that more substantial restrictions on the steps are needed to guarantee the nonergodic function values' convergence (rates).
Analysis and Improvement of an E-voting System Based on Blockchain

Voting is one of the important aspects of a democratic society in which people can express their opinions in a formal and legitimate way. In recent years, there has been an increasing interest in electronic voting (e-voting) systems. E-voting is a modern method of vote casting which is based on cryptographic tools. In many proposed scheme, it is a need to have a trusted entity to manage to take the votes and properly counting them. Recently and in the light of pivotal features of blockchain technology including immutability, transparency, and decentralization, blockchain based schemes have received considerable attention. In 2017, J. P. Cruz and Y. Kaji presented a blockchain based e-voting scheme with the help of some cryptographic concepts like bit-commitment and blind signatures. The vital property of their scheme was that it did not use any kind of anonymous channel which is a common and of course hard to implement tool in many e-voting protocols. In this paper, we address some security issues in the mentioned scheme and propose an improved version that has the edge on the prior one in terms of preserving privacy of voters and efficiency when it comes to large scale elections. We show how to use private blockchain instead of a public blockchain to provide more efficiency and privacy. Also, by utilizing proper entities, some security breaches like double voting are nrevented.
Digital Forensics for Drones: A Study of Tools and Techniques

Digital forensics investigation on drones has gained significant popularity during the recent past mainly due to intensifying drone-related cybercrime activities. Collecting valid digital evidence from confiscated drones and to prove a case in the court of law has been a difficult and lengthy process due to various factors including choice of tools. Consequently, to simplify and speed up the investigation process, the most apt forensic investigation tools must be selected to carry out the investigation, accompanied with data mining techniques for data analysis and visualization, ascertains accuracy in reporting. We present our findings on tool selection and intelligent clustering of DJI Mavic Air drone data. The proposed methodology can help forensic investigators identify the most pertinent forensic investigation tools to foster a sound artificial intelligence-based forensic investigation process. Experiments were conducted on sample drone data, with findings reported on the superiority of Airdata.com and Autopsy forensic tools over others.
RETRACTED: Research on English translation long text filtering based on LSTM semantic relevance

A semantic examination is a critical job undertaking in text mining the tremendous measure of information random to the Internet and different assets. Here, the semantic-based abstract should be successful using data classification and incorporate relevant data extraction. Precise grouping preparation is done as of late, utilizing profound learning procedures. Nonetheless, there is no off-the-rack model to accomplish sensible connection exactness. It proposes effective semantic analysis-based data retrieval on the Internet to obtain relevant data models from local resource libraries and web applications to overcome these shortcomings. Based on artificial neural networks and semantic analysis, another component choice calculation is proposed to choose the comparability of ordering related information in a nearby store or web application. Additionally, the new semantic-based information aggregation technology is outlined in the textbook of online resources. Finally, a deep neural network based on semantic similarity and semantic relations is also introduced. A new classifier is based on classification data. The model is based on various experiments that extract relevant data from internet resources to prove that data processing is effective and tested on recognized datasets.
Port or Shim? Stress Testing Application Performance on Intel SGX

Intel's newer processors come equipped with Software Guard Extensions (SGX) technology, allowing developers to write sections of code that run in a protected area of memory known as an enclave. In this work, we compare performance of two scenarios for running existing code on SGX. In one, a developer manually ports the code to SGX. In the other, a shim-layer and library OS are used to run the code unmodified on SGX. Our initial results demonstrate that when running an existing benchmarking tool under SGX, in addition to being much faster for development, code running in the library OS also tends to run at the same speed or faster than code that is manually ported. After obtaining this result, we then go on to design a series of microbenchmarks to characterize exactly what types of workloads would benefit from manual porting. We find that if the application to be ported has a small sensitive working set (less than the 6MB available cache size of the CPU), infrequently needs to enter the enclave (less than 110,000 times per second), and spends most of its time working on data outside of the enclave, then it may indeed perform better if it is manually ported as opposed to run in a shim.
FlowCFL: generalized type-based reachability analysis: graph reduction and equivalence of CFL-based and type-based reachability

Reachability analysis is a fundamental program analysis with a wide variety of applications. We present FlowCFL, a type-based reachability analysis that accounts for mutable heap data. The underlying semantics of FlowCFL is Context-Free-Language (CFL)-reachability. We make three contributions. First, we define a dynamic semantics that captures the notion of flow commonly used in reachability analysis. Second, we establish correctness of CFL-reachability over graphs with inverse edges (inverse edges are necessary for the handling of mutable heap data). Our approach combines CFL-reachability with reference immutability to avoid the addition of certain inverse edges, which results in graph reduction and precision improvement. The key contribution of our work is the formal account of correctness, which extends to the case when inverse edges are removed. Third, we present a type-based reachability analysis and establish equivalence between a certain CFL-reachability analysis and the type-based analysis, thus proving correctness of the type-based analysis.
Quantitative microscale Fe redox imaging by multiple energy X-ray fluorescence mapping at the Fe<i>K</i>pre-edge peak

Abstract Fe oxidation/reduction reactions play a fundamental role in a wide variety of geological processes. In natural materials, Fe redox state commonly varies across small spatial scales at reaction interfaces, yet the approaches available for quantitatively mapping the Fe redox state at the microscale are limited. We have designed an optimized synchrotron-based X-ray spectroscopic approach that allows microscale quantitative mapping of Fe valence state by extending the Fe XANES pre-edge technique. An area of interest is mapped at nine energies between 7109–7118 eV and at 7200 eV, allowing reconstruction, baseline subtraction, and integration of the pre-edge feature to determine Fe(III)/ΣFe with 2 μm spatial resolution. By combining the Fe redox mapping approach with hyperspectral Raman mineralogy mapping, the Fe oxidation state distributions of the major mineral phases are revealed. In this work, the method is applied to a partially serpentinized peridotite with various Fe-bearing secondary mineral phases to trace the Fe transformations and redox changes that occurred during its alteration. Analysis with the Fe redox mapping technique revealed that the peridotite contained relict olivine with abundant Fe(II), while serpentine, pyroaurite, and another hydroxide phase are secondary mineral reservoirs of Fe(III). Although serpentine is not Fe-rich, it contained approximately 74% ± 14% Fe(III)/ΣFe. These analytical results are integral to interpreting the sequence of alteration reactions; serpentinization of primary olivine formed Fe(II)-rich brucite and oxidized serpentine, which could have contributed to H2 production during serpentinization. Subsequent weathering by oxidizing, CO2-bearing fluids led to the partial carbonation and oxidation of brucite, forming pyroaurite and a hydroxide phase containing dominantly Fe(III). This Fe redox imaging approach is applicable to standard petrographic thin sections or grain mounts and can be applied to various geologic and biogeochemical systems.
Identifiable Tampering Multi-Carrier Image Information Hiding Algorithm Based on Compressed Sensing

Aiming at the security problem of secret information preprocessing and the difficulty of improving the capacity and robustness of the single-carrier image information hiding algorithm, an identifiable tampering multi-carrier image information hiding algorithm based on compressed sensing is proposed. Firstly, the angle structure descriptor feature vector was used to preprocess and classify the image carrier set. Secondly, the GHM multiwavelet transform was applied to different types of image carriers to obtain the secret information hiding area which can balance the invisibility and robustness. Thirdly, the secret image was processed by compressed sensing, the resulting observation matrix was decomposed by singular value, and the chaotic scrambling was encoded by logistic mapping. Finally, the secret information was embedded in the image singular value to complete the information hiding of different types of multi-quantity image carriers. Combined with the angle structure descriptor of the image, the algorithm proposed an effective way to organize multiple carriers, which improved the embedding quality and efficiency of secret information. The verification data and segmented secret information classification and embedding strategy made the proposed algorithm have a keen ability to detect tampering and effectively improve the efficiency and integrity of secret information extraction. Experimental results show that compared with image sharing information hiding algorithm and the single-carrier information hiding algorithm based on compressed sensing, the invisibility and robustness of our algorithm are significantly improved. At the same time, the proposed algorithm has strong anti-analysis ability, can effectively resist most image processing attacks, and is suitable for large capacity secret communication and high-security applications.
Dynamic Projection Mapping with Networked Multi-projectors Based on Pixel-parallel Intensity Control

We present a new method of mapping projections onto dynamic scenes by using multiple high-speed projectors. The proposed method controls the intensity in a pixel-parallel manner for each projector. As each projected image is updated in real time with low latency, adaptive shadow removal can be achieved for a projected image even in a complicated dynamic scene. Additionally, our pixel-parallel calculation method allows a distributed system configuration so that the number of projectors can be increased by networked connections for high scalability. We demonstrated seamless mapping onto dynamic scenes at 360 fps by using ten cameras and four projectors.
Web3D Distance live workshop for children in Mozilla Hubs

This paper proposes the design and procedures of a live workshop for children in Mozilla Hubs. We developed a step-by-step method called "Sugoroku," spatial design, and audio effective calculations. The method proved to be efficient during the kids' workshop. The "Sugoroku" method can be implemented in other systems.
High-Level Design and Rapid Implementation of a Clinical and Non-clinical Blockchain-Based Data Sharing Platform for COVID-19 Containment

As the world has moved toward rigorous containment measures due to the spread of a novel coronavirus, it is crucial to push the boundaries of clinical data obtaining methods using real-time capturing facilities. During this time of crisis, data-centric technologies that could provide authenticity and immediate access to patient data are essential. A blockchain-based digital health protocol for access to real-time data with user-centric data protection measures can achieve these aims. Immediate and secure access to biomedical data can provide credible insights and also help in discovering intelligence to expediate the development of effective therapeutics. It also aids in altering policies for restrictions by extracting key insights required for modeling studies. This paper delivers a high-level design of a blockchain-based clinical research data collection and health service intervention platform, where the users can exercise control of data. This application also provides a platform to deliver technology-based interventions which would assist in streamlining aid for vulnerable users to prevent the NHS from being overwhelmed. Further steps are also recommended to achieve a data compliant solution for rapid deployment, based on available resources, allowing a collaborative effort, which is extremely necessary at times of such crisis.