Reviewerly/exHarmony huggingface dataset:
Quantum Algorithm to Identify Division Property of a Multiset

Division property-based integral attack is the generalization of integral property developed by blending saturation attack and higher-order differential attack. This attack is considered as a chosen-plaintext attack because the cryptanalyst generates a multiset of plaintext which possesses a certain division property. However, in real-world applications, it is required to find the division property of a given multiset which turns the attack into a known-plaintext attack. The problem, finding the division property of a given multiset $$\mathbb {X}$$ of size $$|\mathbb {X}|$$ with each element of n-bit, when solved on a classical computer has the time complexity of $$O(n2^n|\mathbb {X}|)$$ (fixed in both average and worst cases). In this paper, a better and comparable algorithm using quantum computing is presented along with its quantum oracle designs that can find the correct division property of a multiset in the average case time complexity of $$O \left( \log (n)2^n\sqrt{|\mathbb {X}|} \right) $$ and worst case time complexity of $$O\left( \log (n) 2^n|\mathbb {X}| \right) $$ using $$\left( n + \lceil \log |\mathbb {X}|\rceil + p \right) $$ -qubits, where p are the precision qubits required by the quantum counting subroutine.
Wet-Etched Microlens Array for 200 nm Spatial Isolation of Epitaxial Single QDs and 80 nm Broadband Enhancement of Their Quantum Light Extraction

Uniform arrays of three shapes (gauss, hat, and peak) of GaAs microlenses (MLs) by wet-etching are demonstrated, ∼200 nm spatial isolation of epitaxial single QDs embedded (λ: 890–990 nm) and broadband (Δλ∼80 nm) enhancement of their quantum light extraction are obtained, which is also suitable for telecom-band epitaxial QDs. Combined with the bottom distributed Bragg reflector, the hat-shaped ML forms a cavity and achieves the best enhancement: extraction efficiency of 26%, Purcell factor of 2 and single-photon count rate of 7×106 counts per second at the first lens; while the gauss-shaped ML shows a broader band (e.g., longer λ) enhancement. In the MLs, single QDs with featured exciton emissions are observed, whose time correlations prove single-photon emission with multi-photon probability g(2)(0)=0.02; some QDs show both biexciton XX and exciton X emissions and exhibit a perfect cascade feature. This work could pave a step towards a scalable array of QD single-photon sources and the application of QD photon-pair emission for entanglement experiments.
A Survey on Data privacy and preservation using Blockchain in Healthcare organization

Nowadays, health care data has always needed privacy and security. These healthcare data depend upon the cloud computing technology to manage and easy access for patients, health care providers and other authorized parties. But the suspicious attackers always attracted towards this healthcare data, and it automatically leads to consequences in the healthcare organization. This paper has been intended to study and surveyed how to preserve and secure health care data against suspicious attacks. The high demand for efficient storage leads to Blockchain as storage. The dominant Blockchain storage stored a large amount of health data with a shared key to encrypt the data to access it. The privacy-preserving platform technique has been implemented to curb the unauthorized users and accomplish privacy and security. Elliptic curve cryptography used for encryption purpose to encrypt the sensitive data while sharing the health data. Health care data has been considered as more sensitive so that security and user control has been accomplished by access control mode. In this survey, the cloud computing which made a mark in the field of health care organization, Blockchain a tremendous effective storage routine and the privacy-preserving scheme for authorization leads to legal user entry have been reviewed. Therefore the person who reads this survey can able to know about the secured way to access and store the sensitive information in cloud computing.
WITHDRAWN: Determining the impact of cyber security awareness on employee behaviour: A case of Saudi Arabia

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/about/our-business/policies/article-withdrawal ). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Learning Decision Trees Recurrently Through Communication

Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process. The key aspect of our model is its ability to build a decision tree whose structure is encoded into the memory representation of a Recurrent Neural Network jointly learned by two models communicating through message passing. In addition, our model assigns a semantic meaning to each decision in the form of binary attributes, providing concise, semantic and relevant rationalizations to the user. On three benchmark image classification datasets, including the large-scale ImageNet, our model generates human interpretable binary decision sequences explaining the predictions of the network while maintaining state-of-the-art accuracy.
ActiveKeeper: An Accurate and Efficient Algorithm for Finding Top-k Elephant Flows

Finding top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> elephant flows in high-speed network is a crucial task for network traffic measurement. Since modern routers do not hold enough on-chip memory to precisely track all flow sizes, identifying elephant flows becomes more challenging than ever. Existing solutions mostly use compact data structures to fit in limited on-chip memory. However, they have to use large-size counters, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , 20-bit counter, to prevent overflows when recording flow sizes. This wastes significant on-chip memory since flow size distribution is highly skewed. Most counters only require 8 bits to track mouse flows rather than 20 bits. In this letter, we propose ActiveKeeper, an efficient top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> finding solution that uses two-mode active counter to record flow sizes, reducing memory usage while ensuring high accuracy. We also introduce dynamically exponential decay to increase the precision in expelling the small flows while holding the large ones. Experimental results based on real-world Internet traces show that our method outperforms the state-of-the-art by achieving high accuracy and higher precision with small on-chip memory usage.
Multiple global optima location using differential evolution, clustering, and local search

Multimodal optimization can be divided into two main categories. The first focuses on finding only one global optimum, e.g., one peak, and the second focuses on finding multiple global optima, e.g., multiple peaks, and it is the focus of this work. The first category can be approached by single-solution and population-based metaheuristics, while in the second category, the use of population-based metaheuristics is best suited. Finding multiple global optima is more common in real-world optimization problems where there is no previous information about the number of globally optimal solutions in the search space landscape. Thus, this work proposes a self-adaptive Differential Evolution with DBSCAN algorithm and a two-step exploitation routine, named NCjDE-2LS a r , applied to multiple global optima multimodal optimization. The jDE algorithm with Michalewicz mutation strategy is employed as a global optimizer. Candidate solutions are grouped in an external archive using the DBSCAN algorithm. External archive solutions represent possible peaks that will feed the Nelder–Mead and Hooke–Jeeves exploitation algorithms. The peak ratio is used as a performance metric. Six state-of-the-art algorithms are used to compare the results obtained by the proposed approach. Results show that the proposed algorithm outperforms the state-of-the-art algorithms concerning the mean absolute value of the peak ratio evaluation metric. The algorithm also achieved the perfect peak ratio (100%) in 10 out of 20 functions. • A hybrid algorithm to approach multiple global optima problems. • A highly competitive algorithm for multiple global optima location. • The use of DBSAN algorithm to cluster promising solutions. • The use of a two-level local search routine. • Impact analysis of the local search application.
Application of Linear Regression Mathematical Model in the Evaluation of Teachers’ Informatization Quality

With the widespread application of information technology and the continuous deepening of my country’s education informatization construction, the educational field has increasingly higher requirements for teachers’ informatization quality. The advancement of the process of education informatization depends to a large extent on the level of teachers’ own level of informatization and has gradually become an important indicator to measure the informatization of education. The main body of education and the decisive factor are teachers. The scientific and reasonable evaluation of informatization education is an important content of today’s education informatization. This paper uses regression analysis to evaluate the teacher education information system and uses mathematical statistics to establish a linear regression model for the city and analyze and calculate it. Through the quantitative analysis of the evaluation index system of the education informatization level, the multiple linear regression analysis method was used to establish the linear regression mathematical model of the school education informatization level on each secondary index, and the influence of various indicators on the school education informatization level was analyzed to establish a scientific evaluation system to evaluate the literacy of teachers’ informatization education and to give suggestions for improving the level of school education informatization. The design of the evaluation system is more scientific and reasonable, the regression error is small, the results obtained are more accurate, and the impact of nonlinear errors is basically eliminated.
Off-Chain Execution of IoT Smart Contracts

Modern blockchains allow the definition of smart contracts (SCs). An SC is a computer protocol designed to digitally ease, verify, or enforce the terms of agreement between users. SCs execution can require high fees when lots of computation is required or a high volume of data is stored. This is usually the case of Internet-of-Things (IoT) systems where a large amount of devices can produce a high volume of data. Off-chain contract execution is a viable solution to decrease the blockchain fees. Users can agree on an on-chain SC which is stored in the main chain. Computation can then be moved securely outside the chain to reduce fees. In this paper we propose DIVERSITY a novel approach that allows off-chain execution of SCs. DIVERSITY provides a novel model for defining on-chain contracts that can be securely executed by using a novel off-chain protocol. We have validate our approach on a novel IoT case study where fees have been greatly reduced.
BB-PKI: Blockchain-Based Public Key Infrastructure Certificate Management

Recently, real-world attacks against the web Public Key Infrastructure (PKI) have arisen more frequently. The current PKI that use Registration Authorities/Certificate Authorities (RAs/CAs) model suffer from notorious security vulnerabilities. Most of these vulnerabilities are due to compromises of RAs, which lead to impersonation attacks resulting in CAs misbehaving to issue bogus certificates. To counter this problem, many approaches, such as Certificate Transparency (CT), ARPKI, and PoliCert, have been proposed. Nonetheless, no solution has yet gained widespread acceptance as a result of complexity and deployability issues. Moreover, existing approaches still require to satisfy complicated interactions and synchronisation among the entities that are involved during certificate issuance, updates, and revocations. In this paper, we propose a new Blockchain-Based PKI (BB-PKI) to address these vulnerabilities of CA misbehaviour caused by impersonation attacks against RAs. Certificate Issuance Request (CIR) should be vouched by manifold RAs. Multiple CAs shall sign and issue the certificate using an out-of-band secure communication channel. Any RA that contributes to the verification process of a user's request can publish the certificate in the blockchain by creating a smart contract certificate transaction. BB-PKI offers strong security guarantees, compromising $n - 1$ of the RAs or CAs is not enough to launch impersonation attacks, meaning that attackers cannot compromise more than the threshold of the latter signatures to launch an attack.
SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)

Security of inference phase deployment of Convolutional neural network (CNN) into resource constrained embedded systems (e.g. low end FPGAs) is a growing research area. Using secure practices, third party FPGA designers can be provided with no knowledge of initial and final classification layers. In this work, we demonstrate that hardware intrinsic attack (HIA) in such a "secure" design is still possible. Proposed HIA is inserted inside mathematical operations of individual layers of CNN, which propagates erroneous operations in all the subsequent CNN layers that leads to misclassification. The attack is non-periodic and completely random, hence it becomes difficult to detect. Five different attack scenarios with respect to each CNN layer are designed and evaluated based on the overhead resources and the rate of triggering in comparison to the original implementation. Our results for two CNN architectures show that in all the attack scenarios, additional latency is negligible (<; 0.61%), increment in DSP, LUT, FF is also less than 2.36%. Three attack scenarios does not require any additional BRAM resources, while in two scenarios BRAM increases, which compensates with the corresponding decrease in FF and LUTs. To the authors' best knowledge this work is the first to address the hardware intrinsic CNN attack with attacker does not have knowledge of the full CNN.
The social media response to the rollout of legalized cannabis retail in Ontario, Canada

With Canada becoming the first G20 country to legalize the recreational use of cannabis, there has been increasing interest in the emergence of this new retail market. The research utilizes social media analytics to analyze the public's response to the rollout of the government-controlled cannabis retail stores: Ontario Cannabis Store (OCS). The research analyzes 17,162 tweets mentioning the OCS (@ONCannabisStore) on Twitter in the one-year period following the legalization of recreational cannabis. Using thematic analysis, 19 codes are identified and further categorized under six themes—i.e., consignment, product, retail model, policy, producers, and consumers. The research provides valuable insight into the public's perceptions of the newly legalized cannabis retail market on social media. As a practical implication of the research, key concerns and issues with the initial retail rollout are identified, which provides insight into the evolution of an illegal to legal retail market. The methods can be used by future researchers, policy makers, and emerging cannabis retailers to gather and understand cannabis consumers' opinions on social media. Furthermore, the findings can be leveraged to inform future government policies and decisions around the emergence of this new retail sector.
InMap

There exist techniques that attempt to automate the mapping step in Reflexion Modelling, a popular software architecture consistency checking method. However, most of these techniques require an initial set of pre-mapped entities in order to execute automated mapping. This study proposes an interactive technique that is able to automate mapping without the need for pre-mapped entities. Using some minimal architecture documentation, the technique utilises information retrieval concepts applied to the software's codebase to recommend mappings between the software's source code entities and its architectural modules. The technique achieves higher f1-scores than existing techniques that require initial pre-mapping before automation.
Underwater image sharpening based on structure restoration and texture enhancement

Light can be absorbed and scattered when traveling through water, which results in underwater optical images suffering from blurring and color distortion. To improve the visual quality of underwater optical images, we propose a novel, to the best of our knowledge, image sharpening method. We utilize the relative total variation model to decompose images into structure and texture layers in a novel manner. On those two layers, the red-blue dark channel prior (RBDCP) and detail lifting algorithms are proposed, respectively. The RBDCP model calculates background light based on brightness, gradient discrimination, and hue judgment, which then generates transmission maps using red-blue channel attenuation characteristics. The linear combination of the Gaussian kernel and binary mask is employed in the proposed detail lifting algorithm. Furthermore, we combine the layers of restoration structure and enhancement texture for image sharpening, inspired by the concept of fusion. Our methodology has rich texture information and is effective in color correction and atomization removal through RBDCP. Extensive experimental results indicate that the proposed method effectively balances image hue, saturation, and clarity.
Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these networks for significant differences, finding that networks from Alzheimer’s patients have a lower heterogeneity and centralization, but a higher edge density. We then use these measures, a network embedding method and some measures from the word frequency distribution to classify the transcripts into control or Alzheimer’s, and to estimate the cognitive test score of a participant based on the transcript. We find it is possible to distinguish between the AD and control networks on structure alone, achieving 66.7% accuracy on the test set, and to predict cognitive scores with a root mean squared error of 5.675. Using the network measures is more successful than using the network embedding method. However, if the networks are shuffled we find relatively few of the measures are different, indicating that word frequency drives many of the network properties. This observation is borne out by the classification experiments, where word frequency measures perform similarly to the network measures.
Source identification of the geochemical anomaly from the fine-grained soil survey in the Nuheting sandstone-type uranium deposit, Erlian Basin, north China

Sedimentary basins in the arid areas of northern China have great potential to aid the discovery of new sandstone-type uranium deposits. A fine-grained (<75 μm) soil survey covering an area of approximately 6400 km2 has been undertaken to explore the concealed sandstone-type uranium deposit in Erlian Basin, north China. Displaying an obvious geochemical anomaly, the Nuheting (NHT) sandstone-type uranium deposit has been chosen for detailed studies of the source of geochemical anomaly, using lead (Pb) isotopic tracing and nanoparticle observation. Fine-grained soils are divided into three groups according to their spatial distance to the NHT uranium deposit which are soils above deposit, proximal soils and distal soils. Using analysis of variance (ANOVA), Pb isotope ratios of these soils show significant difference between proximal and distal samples. Deep mineralization and regional faults may give rise to the difference of radiogenic Pb isotopes. In addition, the widely observed Mo-bearing nanoparticles provide evidence that fine-grained soils contain signature of deep uranium ore. The characteristics of Pb isotope and nanoparticles indicate that accumulation of radiogenic Pb isotopes in the fine-grained soils is resulted from the concealed NHT uranium deposit. The transport and evaporation of groundwater has led to the vertical migration of geochemical signature from the deep to the surface. The fine-grained soil survey shows promise as an effective exploration technique for the sandstone-type uranium deposit in arid areas.
Proof-Carrying Hardware-Based Information Flow Tracking in Analog/Mixed-Signal Designs

Information flow tracking (IFT) is a widely used methodology for ensuring data confidentiality and/or integrity in electronic systems and many such methods have been developed at various software or hardware description levels. Among them, Proof-Carrying Hardware Intellectual Property (PCHIP) introduced an IFT methodology for digital hardware designs described in hardware description languages (HDLs). However, it is not only the digital domain that suffers from the risk of inadvertent information leakage. Indeed, analog signals originating from sources of sensitive information such as biometric sensors, as well as analog circuit outputs could also carry confidential information. Moreover, analog circuits are equally susceptible as their digital counterparts to malicious modifications, known as hardware Trojans, which could introduce covert channels for leaking such confidential information. Furthermore, in analog/mixed-signal circuits, such information leakage channels may cross the analog/digital or digital/analog interface, making their detection even harder and, thereby, intensifying this security concern. As a solution, we introduce a PCHIP-based methodology which enables systematic formal evaluation of information flow policies in analog/mixed-signal designs. This solution can reason on analog designs described at the transistor-level or at the block-level, where an abstract model of the analog circuit is considered. Additionally, it can handle analog circuit models developed in Verilog-A or Verilog-AMS, thereby enabling the use of circuit models developed in these HDLs for IFT purposes. By integrating IFT across the digital and analog domains, the proposed solution is able to detect sensitive data leakage from the digital domain to the analog domain and vice-versa, without requiring any modification of the current analog/mixed-signal circuit design flow.
Instagram Sentiment Analysis: Opinion Mining

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