Reviewerly/exHarmony huggingface dataset:
The application of artificial intelligence and virtual reality in the auxiliary teaching of American science fiction literature

In order to improve the teaching effect of American science fiction literature, based on artificial intelligence virtual reality technology, this paper constructs an auxiliary teaching system of intelligent American science fiction literature. Moreover, this paper analyzes the time complexity and space complexity of constructing point cloud spatial topological relations and finding the nearest k neighboring points. Simultaneously, this paper uses CUDA to find k nearest neighbors on the GPU, analyzes the point cloud denoising technology, uses the KD-tree to construct the point cloud topology in the DBSCAN-based denoising method, searches for the k nearest neighbors to complete the mark of the core point and the boundary point. In addition, this paper combines artificial intelligence virtual technology and intelligent algorithms to construct the framework of the auxiliary teaching system of American science fiction literature, and analyze its functional modules. Finally, this paper designs experiments to verify the performance of the model. The research results show that the system constructed in this paper can meet the needs of auxiliary teaching of American science fiction literature.
How Should Developers Respond to App Reviews? Features Predicting the Success of Developer Responses

Context: The Google Play Store allows app developers to respond to user reviews. Existing research shows that response strategies vary considerably. In addition, while responding to reviews can lead to several types of favorable outcomes, not every response leads to success, which we define as increased user ratings.
Dual Traceable Distributed Attribute-Based Searchable Encryption and Ownership Transfer

In this article, we propose <b>d</b> ual <b>t</b> raceable <b>d</b> istributed <b>a</b> ttribute <b>b</b> ased <b>e</b> ncryption with <b>s</b> ubset <b>k</b> eyword <b>s</b> earch system (DT-DABE-SKS, abbreviated as <inline-formula><tex-math notation="LaTeX">$\mathcal {DT}$</tex-math></inline-formula> ) to simultaneously realize data source trace (secure provenance) and user trace (traitor trace) and flexible subset keyword search from polynomial interpolation. Leveraging non-interactive zero-knowledge proof technology, <inline-formula><tex-math notation="LaTeX">$\mathcal {DT}$</tex-math></inline-formula> preserves privacy for both data providers and users in normal circumstances, but a trusted authority can disclose their real identities if necessary, such as the providers deceitfully uploading false data or users maliciously leaking secret attribute key. Next, we introduce the new conception of updatable and transferable message-lock encryption (UT-MLE) for block-level dynamic encrypted file update, where the owner does not have to download the whole ciphertext, decrypt, re-encrypt and upload for minor document modifications. In addition, the owner is permitted to transfer file ownership to other system customers with efficient computation in an authenticated manner. A nontrivial integration of <inline-formula><tex-math notation="LaTeX">$\mathcal {DT}$</tex-math></inline-formula> and UT-MLE lead to the distributed ABSE with ownership transfer system ( <inline-formula><tex-math notation="LaTeX">$\mathcal {DTOT}$</tex-math></inline-formula> ) to enjoy the above merits. We formally define <inline-formula><tex-math notation="LaTeX">$\mathcal {DT}$</tex-math></inline-formula> , UT-MLE, and their security model. Then, the instantiations of <inline-formula><tex-math notation="LaTeX">$\mathcal {DT}$</tex-math></inline-formula> and UT-MLE, and the formal security proof are presented. Comprehensive comparison and experimental analysis based on real dataset affirm their feasibility.
Designing excitonic circuits for the Deutsch–Jozsa algorithm: mitigating fidelity loss by merging gate operations

Precisely arranged sets of dye molecules can utilized as elementary quantum computing elements. Here, we consider two different strategies for designing these excitonic circuits for a 2-qubit multi-step quantum algorithm.
New combination of simple additive and entropy weighting criteria for the selection of best substitution box

Substitution boxes (S-boxes) are among the most widely recognized and fundamental component of most modern block ciphers. This is on the grounds that they can give a cipher fortifying properties to oppose known and possible cryptanalytic assaults. We have suggested a novel tool to select nonlinear confusion component. This nonlinear confusion component added confusion capability which describes to make the connection among the key and the cipher as complex and engaging as possible. The confusion can be obtained by using substitution box (S-box) and complex scrambling algorithm that relies on key and the input (plaintext). Various statistical and cryptographic characteristics were introduced to measure the strength of substitution boxes (S-boxes). With the help of the present objective weight methods and ranking technique, we can select an ideal S-box among other constructed confusion component to make our encryption algorithm secure and robust against various cryptographic attacks.
Blockchain adoption in health market: a systems thinking and modelling approach

Purpose The purpose of this paper is to formulate strategic interventions that can contribute to adoption of blockchain in Asian health market. Design/methodology/approach A methodological framework based on the three qualitative phases of the systems thinking and modelling methodology, namely, problem structuring, causal loop modelling and developing strategic interventions were used. Data was collected using thirty interviews and secondary research. Findings The reference model developed in the study captured the system behavior, showing health market blockchain adoption increasing in other countries but struggling in Asia. The qualitative systems model developed explained this behavior by capturing the underlying system structure using six interconnected feedback loops. Three strategic interventions were formulated to change the system structure to improve its adoption. Research limitations/implications This study acknowledges its limitation that blockchain integration in health supply chain management (SCM) and adaptability to technological trends are just few elements that contributes to challenges in blockchain adoption in Asia Pacific health market . It also acknowledges the limited scale of this study using 30 interviews with a narrow set of stakeholders, limiting its generalisability. Practical implications Although the blockchain technology has medical data security benefits, its integration in health SCM could potentially improve cost of health service delivery to patient. Not only it prevents drug counterfeiting, it enhances patient’s safety by reducing the time it takes to alert the supply chain of a product recall from few days to few seconds. Social implications “Blockchain-as-a-Service” has wide applications in SCM, identity management, payments, smart contracts, governance risk and compliance management, with its market size expected to grow in future. This affordable solution opens up the roads for future empirical research for information systems academicians and information technology practitioners. Originality/value This research provides a holistic analysis of the challenges facing blockchain adoption in the health market for Asia. It offers a methodological approach based on systems thinking and modelling to improve its adoption.
Individual-Initiated Auditable Access Control for Privacy-Preserved IoT Data Sharing with Blockchain

With the rapid development of sensors and IoT technology, personal health data can be collected and stored by various wearable devices and utilized for healthcare. To share and use sensitive health data securely and efficiently, a variety of solutions based on blockchain have been proposed and developed. However, there are still many issues to be solved, such as how to let individuals control and manage their own data, and how to make all data accesses strictly auditable. In this paper, we present a new model of Individual-Initiated Auditable Access Control (IIAAC) enabled with blockchain, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and IPFS (InterPlanetary File System). After introducing scenarios for sharing and use of health data, we define the design requirements for a blockchain-based system and describe the basic system architecture. We discuss the detailed procedures in IIAAC, including CP-ABE key generation, data publication and data retrieval. We further compare this study with related work in terms of functions and features.
Precedential constraint

Horty, Rigoni and Prakken have developed formal characterisations of precedential constraint based on dimensions and factors as introduced in HYPO and CATO. We discuss the relation between dimensions and factors and also describe the current models of precedential constraint based on factors, along with some criticisms of them. We argue that problems arise from ignoring the structure of legal cases that is provided by the notion of issues, and that seeing precedential constraint in terms of issues rather than whole cases provides a more effective approach and better reflects legal practice. The advantages of the issue based approach are illustrated with a concrete example. We then discuss how dimensions should be accommodated, suggesting that this is best done by seeing reasoning with legal cases as a two stage process: first factors are ascribed to cases and then factor based reasoning can be used to arrive at a decision. Thus precedential constraint can be described in terms of factors, dimensions being handled at the first stage. Both stages are constrained, in different ways, by precedents: we identify three types of precedent: framework precedents which structure cases into issues, preference precedents which resolve conflicts between opposing sets of factors within these issues, and ascription precedents which constrain the mapping from facts to factors.
A monogamy-of-entanglement game for subspace coset states

We establish a strong monogamy-of-entanglement property for subspace coset states, which are uniform superpositions of vectors in a linear subspace of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msubsup></mml:math> to which has been applied a quantum one-time pad. This property was conjectured recently by [Coladangelo, Liu, Liu, and Zhandry, Crypto'21] and shown to have applications to unclonable decryption and copy-protection of pseudorandom functions. We present two proofs, one which directly follows the method of the original paper and the other which uses an observation from [Vidick and Zhang, Eurocrypt'20] to reduce the analysis to a simpler monogamy game based on BB'84 states. Both proofs ultimately rely on the same proof technique, introduced in [Tomamichel, Fehr, Kaniewski and Wehner, New Journal of Physics '13].
A reinforcement Learning approach to resource allocation in genomic selection

Genomic selection (GS) is a technique that plant breeders use to select individuals to mate and produce new generations of species. Allocation of resources is a key factor in GS. At each selection cycle, breeders are facing the choice of budget allocation to make crosses and produce the next generation of breeding parents. Inspired by recent advances in reinforcement learning for AI problems, we develop a reinforcement learning-based algorithm to automatically learn to allocate limited resources across different generations of breeding. We mathematically formulate the problem in the framework of Markov Decision Process (MDP) by defining state and action spaces. To avoid the explosion of the state space, an integer linear program is proposed that quantifies the trade-off between resources and time. Finally, we propose a value function approximation method to estimate the action-value function and then develop a greedy policy improvement technique to find the optimal resources. We demonstrate the effectiveness of the proposed method in enhancing genetic gain using a case study with realistic data.
Gage MPC: Bypassing Residual Function Leakage for Non-Interactive MPC

Abstract Existing models for non-interactive MPC cannot provide full privacy for inputs, because they inherently leak the residual function (i.e., the output of the function on the honest parties’ input together with all possible values of the adversarial inputs). For example, in any non-interactive sealed-bid auction, the last bidder can figure out what was the highest previous bid. We present a new MPC model which avoids this privacy leak. To achieve this, we utilize a blockchain in a novel way, incorporating smart contracts and arbitrary parties that can be incentivized to perform computation (“bounty hunters,” akin to miners). Security is maintained under a monetary assumption about the parties: an honest party can temporarily supply a recoverable collateral of value higher than the computational cost an adversary can expend. We thus construct non-interactive MPC protocols with strong security guarantees (full security, no residual leakage) in the short term. Over time, as the adversary can invest more and more computational resources, the security guarantee decays. Thus, our model, which we call Gage MPC, is suitable for secure computation with limited-time secrecy, such as auctions. A key ingredient in our protocols is a primitive we call “Gage Time Capsules” (GaTC): a time capsule that allows a party to commit to a value that others are able to reveal but only at a designated computational cost. A GaTC allows a party to commit to a value together with a monetary collateral. If the original party properly opens the GaTC, it can recover the collateral. Otherwise, the collateral is used to incentivize bounty hunters to open the GaTC. This primitive is used to ensure completion of Gage MPC protocols on the desired inputs. As a requisite tool (of independent interest), we present a generalization of garbled circuit that are more robust: they can tolerate exposure of extra input labels. This is in contrast to Yao’s garbled circuits, whose secrecy breaks down if even a single extra label is exposed. Finally, we present a proof-of-concept implementation of a special case of our construction, yielding an auction functionality over an Ethereum-like blockchain.
DEVELOPING OF COMPUTATIONAL THINKING USING MICROCONTROLLERS AND SIMULATIONS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 7945-7951ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1619Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Non Invasive Anemia Detection in Pregnant Women Based on Digital Image Processing and K-Nearest Neighbor

Anemia is a disease caused by low levels of hemoglobin in the blood. In general, the detection process of anemia is carried out invasively through an examination of hemoglobin levels in the blood. Another method can be done non-invasively by clinical examination of the conjunctiva of the eyes, tongue, palms, and nails. Early detection of anemia is a solution to prevent severe consequences of lack of hemoglobin levels in the blood, especially in pregnant women who are at higher risk of the mother and fetus's safety. This study proposes a non-invasive computer-aided diagnose system for detecting anemia based on digital image processing. The method is by analyzing the conjunctival image of the eye. This study uses the first-order statistic feature extraction method and K-Nearest Neighbor (K-NN) for classifying the conjunctival image into two conditions, anemia and non-anemia conditions. The feature extraction method is performed on RGB, Hue, Saturation, and Value (HSV), and grayscale color space. The system achieved 71.25% of accuracy by using the most optimal parameters on the Green layer of RGB with K=5 and Euclidean distance equation.
From IP to transport and beyond

We perform the first analysis of methodologies for launching DNS cache poisoning: manipulation at the IP layer, hijack of the inter-domain routing and probing open ports via side channels. We evaluate these methodologies against DNS resolvers in the Internet and compare them with respect to effectiveness, applicability and stealth. Our study shows that DNS cache poisoning is a practical and pervasive threat.
End-to-End Learning of Communication System without Known Channel

Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of communication system is able to outperform the classical communication system. Unfortunately, this communication system cannot be trained by deep learning without known channel. To deal with this problem, a generative adversarial network (GAN) based training scheme has been recently proposed to imitate the real channel. However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system. To mitigate these two problems, this paper proposes a residual aided GAN (RA-GAN) based training scheme. Particularly, inspired by the idea of residual learning, we propose a residual generator to mitigate the gradient vanishing problem by realizing a more robust gradient back propagation. Moreover, to cope with the overfitting problem, we reconstruct the loss function for training by adding a regularizer, which limits the representation ability of RA-GAN. Simulation results show that the trained residual generator has better generation performance than conventional generator, and the proposed RA-GAN based training scheme can achieve the near-optimal block error rate (BLER) performance.
Criminal Identification System using Facial Recognition

We all know that our Face is a unique and crucial part of the human body structure that identifies a person. Therefore, we can use it to trace the identity of a criminal person. With the advancement in technology, we are placed CCTV at many public places to capture the criminal’s crime. Using the previously captured faces and criminal’s images that are available in the police station, the criminal face recognition system of can be implemented. In this paper, we propose an automatic criminal identification system for Police Department to enhance and upgrade the criminal distinguishing into a more effective and efficient approach. Using technology, this idea will add plus point in the current system while bringing criminals spotting to a whole new level by automating tasks. Technology working behind it will be face recognition, from the footage captured by the CCTV cameras; our system will detect the face and recognize the criminal who is coming to that public place. The captured images of the person coming to that public place get compared with the criminal data we have in our database. If any person’s face from public place matches, the system will display their image on the system screen and will give the message with their name that the criminal is found and present in this public place. This system matching more than 80% of the captured images with database images.
Evolvable Hardware-Based Data Security System Using Image Steganography Through Dynamic Partial Reconfiguration

A highly secure communication method is essential for end users for the exchange of information which is not interpreted by an intruder. Cryptography plays a crucial role in the current and upcoming digital worlds, for secure data transmission in wired and wireless networks. Asymmetric and symmetric cryptographic algorithms encrypt data against vulnerable attacks and transfer to authenticated users. Steganography is a method for providing secure information with the help of a carrier file (text, video, audio, image, etc.). This paper proposes Deoxyribonucleic Acid (DNA)-based asymmetric algorithm which is used to encrypt the patient’s secret information and its performance is compared with ElGamal, RSA and Diffie–Hellman (DH) cryptographic algorithms. The proposed asymmetric algorithm is applied to image steganography which is used for encrypting and concealing the patient’s secret information in a cover image. The proposed method consumes less hardware resources with improved latency. Dynamic Partial Reconfiguration (DPR) allows to transform a selective area rather than complete shutdown of the entire system during bitstream configuration. Cryptosystem with DPR is designed, synthesized in Xilinx Vivado and simulated in Vivado simulator. The design is targeted at Basys3, Nexys4 DDR and Zync-7000 all-programmable SOC (AP SoC) architectures and programmed with secure partial bit files to avoid vulnerable attacks in the channel.
A Light-Weight Contextual Spelling Correction Model for Customizing Transducer-Based Speech Recognition Systems

It's challenging to customize transducer-based automatic speech recognition (ASR) system with context information which is dynamic and unavailable during model training. In this work, we introduce a light-weight contextual spelling correction model to correct context-related recognition errors in transducer-based ASR systems. We incorporate the context information into the spelling correction model with a shared context encoder and use a filtering algorithm to handle large-size context lists. Experiments show that the model improves baseline ASR model performance with about 50% relative word error rate reduction, which also significantly outperforms the baseline method such as contextual LM biasing. The model also shows excellent performance for out-of-vocabulary terms not seen during training.
Active speckle deflectometry based on 3D digital image correlation

A method based on 3D digital image correlation (DIC) to measure the shape of specular surface is proposed. The proposed active speckle deflectometry (ASD) utilizes a stereo-camera system to monitor the liquid crystal display (LCD), which is deliberately moved during the measurement. Another testing camera (TC) is used to capture the single-shot speckle pattern displayed on the LCD screen after reflection by the test surface. With this proposal, the movement of the LCD screen can be arbitrary as long as the TC can capture the reflection of speckle pattern. The distance as well as the direction of the movement is not required to be known. The coordinates of the point source are determined by applying the 3D DIC technique with the monitoring stereo-cameras (MSC) before and after the movement of the LCD screen, then the slope and surface shape are obtained. The measurement accuracy of this method is evaluated by measuring a flat glass with a diameter of about 80 mm, compared with the measurement results of interferometer, the shape measurement difference is 0.278um in root mean square (RMS). The shape of two wafers is also measured, and the measurement results are compared to that of the traditional phase measuring deflectometry (PMD). ASD has the advantages of fast measurement, low cost, arbitrary LCD movement, tolerance for the out-of-plane shape of the LCD screen. It provides a new method for specular surface measurement.