Reviewerly/exHarmony huggingface dataset:
Unlocking the Potential of (AI-Powered) Blockchain Technology in Environment Sustainability and Social Good

The world economic forum (WEF) has identified the blockchain as a repair mechanism to the world's most challenging environmental issues. It is a promising technology currently gaining a lot of attention in diverse fields. With the help of this technology, which consists of a decentralized ledger of all dealings across a peer-to-peer network, participants can ratify transactions without the need for a central authority. Blockchain has a greater relevance for global environmental sustainability. It can act as a disruptive technology in managing the world's most pressurizing environmental issues. Blockchain has a real capability to enable a shift to cleaner and more resource-conserving decentralized solutions to unravel natural capital and empower communities. The joint use of AI and blockchain might change and upgrade the tech and environment paradigm significantly enough for environmental protection. This chapter is an attempt to study the applicability of the blockchain in protecting and sustaining a global environment on various levels such as life on land, life below the earth, and climate changes. The deployment of blockchain technology is needed in areas like climate change, biodiversity conservation, and healthy water bodies. Finally, this chapter contemplates combining blockchain with artificial intelligence (AI) and deliberates the revolutionary changes that would result by rapidly advancing and evolving the AI arena.
Development of Scalable Coding of Encrypted Images Using Enhanced Block Truncation Code

Only few researchers are reported on scalable coding of encrypted images, and it is an important area of research. In this paper, a novel method of scalable coding of encrypted images using Enhanced Block Truncation Code (EBTC) has been proposed. The raw image is compressed using EBTC and then encrypted using the pseudo-random number (PSRN) at the transmitter and the Key is disseminated to the receiver. The transmitted image is decrypted at the receiver by using the PSRN key. Finally, the output image is constructed using EBTC, scaled by scaling factor 2 and Bilinear Interpolation Technique. The proposed system gives better PSNR, Compression ratio and storage requirement than existing techniques such as Hadamard, DMMBTC and BTC.
AI Approach for Autonomous Vehicles to Defend from Adversarial Attacks

Neural Networks are really impeccable sometimes, but when it comes to adversarial attacks, their performance falls off swiftly. Moreover, considerations to build more robust models that would be resilient to adversarial attacks are ignored frequently. Here, we aim to design a model that is robust and can handle multiple types of adversarial attacks on traffic signs. Despite those attacks, the model should not be fooled and identify the sign correctly. Simple input transformations can help defend against adversarial attacks. We used the German Traffic Sign Recognition Benchmark (GTSRB) dataset to generate adversarial samples by Fast Gradient Sign Method [1] and Projected Gradient Descent Method [1]. In this paper, we focus on training a model with a defense mechanism to remove the adversarial noise, to make the model Anti-Spoof for Traffic Sign Recognition.
On stochastic stabilization of sampled systems

This paper addresses stochastic stabilization in case where implementation of control policies is digital, i. e., when the dynamical system is treated continuous, whereas the control actions are held constant in predefined time steps. In such a setup, special attention should be paid to the sample-to-sample behavior of the involved Lyapunov function. This paper extends on the stochastic stability results specifically to address for the sample-and-hold mode. We show that if a Markov policy stabilizes the system in a suitable sense, then it also practically stabilizes it in the sample-and-hold sense. This establishes a bridge from an idealized continuous application of the policy to its digital implementation. The central result applies to dynamical systems described by stochastic differential equations driven by the standard Brownian motion. Generalizations are discussed, including the case of non-smooth Lyapunov functions for systems driven by bounded noise. A brief overview of bounded noise models is given.
Visualized Management System of Prefabricated Construction and Installation Engineering Based on BIM and Machine Learning

The visual management system of prefabricated construction and installation engineering cannot meet the boundaries of overlapping construction conditions and prolongs the project completion time. Therefore, a visual management system of prefabricated construction and installation engineering is designed based on BIM and machine learning. In the hardware part, the signal conditioning circuit and the main control circuit are designed to realize the amplification and filtering of the collected signal, which is convenient for the realization of the programmable gain. In the software part, a unified organization coding system is used to establish a building component information database, and a schedule-resource collaborative management model is established based on BIM while satisfying the constraints of overlapping relationships and resource constraints. Using the machine learning method, the quality management process is connected in series according to the relationship between quality flow and information flow, the nodes of the construction quality chain are selected, and resources are allocated. The system performance test is carried out with the prefabricated building project as the experimental object. The result shows that the completion time of the designed system in this paper is 40 weeks, which is 12 and 16 weeks shorter than the system based on RFID integration technology and the genetic algorithm, respectively. Therefore, the design system in this paper is highly sensitive to the difference in the construction period in the project schedule control and can shorten the project completion period.
A Novel Automatic Probe-to-Pad Alignment Error Correction Approach

In wafer test, the probe mark center shall be close to the pad center as much as possible. But the traditional probe-to-pad alignment (PTPA) process cannot always meet the requirements. So a novel automatic error correction approach was developed in this paper to improve the PTPA process. The approach is based on a classic two dimension rigid motion model and enhanced by integrating an iteratively reweighted least squares algorithm. The singular value decomposition method is also used to seek the solutions. Using the probe mark and pad geometric information, it first best fits the probe mark centers to the targets, then it expands the minimum probe mark to pad edge clearance to the largest. The experiments proved its good feasibility, accuracy and efficiency.
Remote English Teaching during Covid-19 Pandemic: Challenges and Lessons in Higher Education Teaching in Colombia

The objective of this article is to do bibliographic research lined up with the worldwide COVID-19 pandemic in which the main focus will be inclusion, Pygmalion effect as well as teaching and learning remotely. The basis is to gather different perspectives to build knowledge through exhaustive reading of books, doctoral dissertations, masters programs dissertations, current news, reliable sources of information, scientific papers, and own experience amongst others. The analysis of this bibliographic research will be useful for future research on topics related. Emotional and affective relations are important and have impact when facing English learning difficulties and when working to have inclusion. In the context of Covid-19, the learning and teaching conditions got complex when migrating to virtual classes. Remote teaching is not a solution to face learning and teaching difficulties. In this way, affection is the best tool for inclusive learning in virtual environments. It is said that one of the biggest barriers is teacher’s formation whose career is based on traditional pedagogy not easily adaptable to digital environments. This situation creates a deep feeling of frustration in teachers, which can be reflected in the low academic performance of English students. Several reflections will be made to encourage the actors involved in the teaching-learning process and in the educational system to ask themselves: Which are the challenges of remote teaching and learning during the Covid-19 pandemic being inclusive through the Pygmalion effect?
A Privacy-Preserving Data Feed Scheme for Smart Contracts

Smart contracts are becoming more and more popular in financial scenarios like medical insurance. Rather than traditional schemes, using smart contracts as a medium is a better choice for both participants, as it is fairer, more reliable, more efficient, and enables real-time payment. However, medical insurance contracts need to input the patient's condition information as the judgment logic to trigger subsequent execution. Since the blockchain is a closed network, it lacks a secure network environment for data interaction with the outside world. The Data feed aims to provide the service of the on-chain and off-chain data interaction. Existing researches on the data feed has solved the security problems on it effectively, such as Town Crier, TLS-N and they have also taken into account the privacy-preserving problems. However, these schemes cannot actually protect privacy because when the ciphertext data is executed by the contract, privacy information can still be inferred by analyzing the transaction results, since states of the contract are publicly visible. In this paper, based on zero-knowledge proof and Hawk technology, a on-and-off-chain complete smart contract data feed privacy-preserving scheme is proposed. In order to present our scheme more intuitively, we combined the medical insurance compensation case to implement it, which is called MIPDF. In our MIPDF, the patient and the insurance company are parties involved in the contract, and the hospital is the data provider of data feed. The patient's medical data is sent to the smart contract under the umbrella of the zero-knowledge proof signature scheme. The smart contract verifies the proof and calculates the insurance premium based on the judgment logic. Meanwhile, we use Hawk technology to ensure the privacy of on-chain contract execution, so that no information will be disclosed due to the result of contract execution. We give a general description of our scheme within the Universal Composability (UC) framework. We experiment and evaluate MIPDF on Ethereum for in-depth analysis. The results show that our scheme can securely and efficiently support the functions of medical insurance and achieve complete privacy-preserving.
Visualizing Electric and Magnetic Field Coupling in Au-Nanorod Trimer Structures via Stimulated Electron Energy Gain and Cathodoluminescence Spectroscopy: Implications for Meta-Atom Imaging

Trimer meta-atoms composed of three gold rods in an equilateral triangular geometry were fabricated, and their near-field plasmonic responses were characterized via electron energy loss (EEL), cathodoluminescence (CL), and stimulated electron energy loss/gain (sEEL/sEEG) spectroscopy. The trimer structure hybridizes into a low-energy mode with all three rods coupling in-phase, which produces a circulating current and thus a magnetic field. The next highest-energy mode consists of two rods coupling out-of-phase and produces a net electric dipole. We investigate the near fields of hybridized magnetic and electric modes via EEL and CL and correlate their spectral characteristics and intensity maps. Then, by changing the length of the trimer rods, we tune the magnetic and electric modes to our laser energy and characterize the excited state via sEEL/sEEG spectroscopy. Exploration of the tilt dependence, relative to the optical source, of the two modes reveals that the electric mode sEEG intensity increases more than the expected sin2(θ) dependence of the optical electric field coupling (see the Supporting Information for a detailed description). After correcting for the tail of the close-proximity electric mode, we demonstrate sEEG via coupling of the magnetic component of the optical field to the magnetic meta-atoms, which has the expected cos2(θ) tilt dependence. This realization opens the possibility to explore the nanoscale excited-state near-field imaging of other magnetic meta-atom structures.
Implementation of 5-Qubit approach-based Shor's Algorithm in IBM Qiskit

RSA cryptography is a principle part in today's cyber-security frameworks, which intensely depends on the diffi-culty of factorizing large integers. Finding a factor of large prime number is not a small feature compared to the time complexity for computation on a classical computer is far beyond comprehension. Back in 1994, Peter Shor proposed a quantum algorithm to factorize integers much more efficiently than a classical computer. This can effectively break asymmetric cryptosystems such as RSA or Diffie-Hellman, depicted as the most famous quantum algorithm. It leverages the efficacy proclaimed by quantum bits and clearly shows how quantum computers can outperform classical ones in specific tasks. In this paper, we show how Shor's algorithm factorizes an integer into its two prime factors. Shor uses both classical and quantum computation to perform this task in polynomial time, using Euclidean algorithm and find the greatest common divisor (gcd) of two numbers in the classical part. Thereafter, the power of Quantum Fourier Transform (QFT) and Quantum Phase Estimation (QPE) is utilised for period-finding using quantum bits. We have implemented this algorithm on IBM's Quantum Lab and successfully factorized the number 15 into its prime factors as 5 and 3, by using 5 qubits. This paper provides an overview of the implementation of Shor's algorithm involving QPE, QFT, Modular Exponentiation and the corresponding individual implementations, the only prerequisite being, to know how a qubit works.
From Requirements to Source Code: Evolution of Behavioral Programs

Automatically generating executable code has a long history of arguably modest success, mostly limited to the generation of small programs of up to 200 lines of code, and genetic improvement of existing code. We present the use of genetic programming (GP) in conjunction with context-oriented behavioral programming (COBP), the latter being a programming paradigm with unique characteristics that facilitate automatic coding. COBP models a program as a set of behavioral threads (b-threads), each aligned to a single behavior or requirement of the system. To evolve behavioral programs we design viable and effective genetic operators, a genetic representation, and evaluation methods. The simplicity of the COBP paradigm, its straightforward syntax, the ability to use verification and formal-method techniques to validate program correctness, and a program comprising small independent chunks all allow us to effectively generate behavioral programs using GP. To demonstrate our approach we evolve complete programs from scratch of a highly competent O player for the game of tic-tac-toe. The evolved programs are well structured, consisting of multiple, explainable modules that specify the different behavioral aspects of the program and are similar to our handcrafted program. To validate the correctness of our individuals, we utilize the mathematical characteristics of COBP to analyze program behavior under all possible execution paths. Our analysis of an evolved program proved that it plays as expected more than 99% of the times.
A Convolution Neural Network Implemented by Three 3 × 3 Photonic Integrated Reconfigurable Linear Processors

The convolution neural network (CNN) is a classical neural network with advantages in image processing. The use of multiport optical interferometric linear structures in neural networks has recently attracted a great deal of attention. Here, we use three 3 × 3 reconfigurable optical processors, based on Mach-Zehnder interferometers (MZIs), to implement a two-layer CNN. To circumvent the random phase errors originating from the fabrication process, MZIs are calibrated before the classification experiment. The MNIST datasets and Fashion-MNIST datasets are used to verify the classification accuracy. The optical processor achieves 86.9% accuracy on the MNIST datasets and 79.3% accuracy on the Fashion-MNIST datasets. Experiments show that we can improve the classification accuracy by reducing phase errors of MZIs and photodetector (PD) noises. In the future, our work provides a way to embed the optical processor in CNN to compute matrix multiplication.
Encrypted Traffic characterization using None Zero payload and Payload Ratio Characteristics

Traffic characterization has been the backbone of network maintenance procedures, such as limiting bandwidth for specific services. However, with today&#x0027;s network, traditional techniques fall short. Traffic characterization using machining learning has been intensively researched to alleviate the shortcomings of traditional methods. This paper proposed a Bi-direction Flow Non-zero Payload Flow (BIF-NZPF) data extraction scheme and Bi-direction Flow Payload Ratio feature (BIF-PR) for supervised traditional machine learning. Our approach is measured on a publicly available ISCX VPN-NonVPN dataset to classify 12 types of traffic using precision, recall, and accuracy. BIF-NZPF reduced the obscurity of application characteristics by filtering out TCP configuration packets. BIF-PR further detailed traffic characteristics using payload size distribution characteristics through local and global traffic flow while being lightly coupled with the duration of traffic flow. Lastly, the ISCX-VPN-NonVPN imbalance class issue is alleviated using a boosting ensemble algorithm, which improves performance.
Antilocality effect without head-final dependencies.

Antilocality effects provide strong evidence for expectation-based sentence parsing models. Previous discussion of the antilocality effect, however, largely focused on the argument-verb dependencies in verb-final constructions, for which a memory retrieval-based account has been argued to be equally adequate. To test whether the principles of expectation/memory-based accounts hold for a wider range of dependencies, we report on two self-paced reading experiments that compared two different determiners in German: the morphologically complex determiner derjenige 'the-jenig,' which obligatorily requires a relative clause, and the bare determiner der 'the,' which does not trigger such expectations. The first experiment did not show the expected antilocality effect, but the reliability of our results was restricted by the experiment's low statistical power. In a large-scale second experiment we addressed confounds in the design of Experiment 1 and found evidence for an antilocality effect with the complex determiner. As the antilocality effect found in our study does not involve argument-verb dependencies, the memory-based account cannot be extended to the current case. Thus, our findings provide novel empirical support for the expectation-based antilocality effect. At the same time, the experiment attests to a processing cost in later sentence regions, hinting that memory- and expectation-based effects can co-occur within the same structure. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Introduction to Artificial Intelligence

Information technology is the third industrial revolution in human history. The popularity of computers, the Internet, and smart home technology has greatly facilitated people's daily lives. Through programming, humans can hand over the interaction logic designed in advance to the machine to execute repeatedly and quickly, thereby freeing humans from simple and tedious repetitive labor. However, for tasks that require a high level of intelligence, such as face recognition, chat robots, and autonomous driving, it is difficult to design clear logic rules. Therefore, traditional programming methods are powerless to those kinds of tasks, whereas artificial intelligence (AI), as the key technology to solve this kind of problem, is very promising.
The potential of applying blockchain technologies in various sectors of the digital economy

The objective of the article is to determine the potential of blockchain technologies in various sectors of the digital economy, in the case of the countries of the European Union. Methodology. The research assesses the use of digital technologies to ensure the country's digital development, based on data from the European Commission and the Digital Economy and Society Index (DESI). On the basis of DESI, a cluster diagram was created to determine the potential for the use of blockchain technology in the countries of the European Union. Results. The potential for the use of blockchain technology in each country depends and is significantly connected with the introduction of different technologies and the availability of the Internet, the provision of digital services by the government, the development of human capital. The main potential advantages of the technology include the following: acceleration of globalization processes, ensuring the country's transition from real production to technological development, change of established business models, transformation and optimization of value chains of different sectors of the economy by reducing costs; transformation of the nature of mediation due to technological changes; the use of cryptocurrency to achieve the consensus of economic agents on the allocation of scarce resources on a global scale.
An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images

With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients’ sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.