Reviewerly/exHarmony huggingface dataset:
The Combined Engineering Assessment Methods for Business Courses under the Background of Artificial Intelligence

In the era of artificial intelligence, it is difficult for traditional business courses to adapt to the development of new technologies. The combination of work and study in business courses has become one of the main directions of business course reform in the new era. Taking the course “Commercial Banking Business and Management” as an example, the paper divides the course assessment into four modules by using artificial intelligence platform and big data processing technology, and uses Python language to carry out procedural evaluation and feedback on the four modules in an all-round management evaluation system. Relying on artificial intelligence technology, the program combines online and offline teaching, assessment and evaluation to effectively improve the teaching effect of business courses.
Performance and Security Analysis of Distributed Ledger Under the Internet of Things Environments With Network Instability

Applying blockchain technology to the Internet of Things (IoT) environment can significantly enhance the privacy protection of information and enable trusted sharing of data. However, the classic Proof-based blockchain systems have problems, such as unsatisfactory system throughput, low-consensus efficiency, and excessive computing resource consumption, making it difficult to directly apply them to the IoT environment. The recently proposed distributed ledger technology (DLT) represented by Tangle is designed based on the logical structure of the directed acyclic graph (DAG), which significantly improves the transaction confirmation delay and system throughput, making it more suitable for IoT scenarios. This article focuses on the performance and security of Tangle DLT in IoT applications with network instability. Such an unstable network environment can be caused by the predefined node working mode or signal interference, which leads to the status switching between online (work-mode) and offline (sleep-mode) of nodes or unstable data transmission. First, a Markov-chain-based model is established to analyze the time delay of block propagation in the underlying network under different status switching rates, different generation rates of blocks, different nodes’ average uplink bandwidths, and different number of neighbor nodes. Next, the impact of block propagation delay on the consensus efficiency and system throughput is analyzed. Furthermore, we also analyze the influence of block propagation delay on the attacker’s decision to conduct a double-spending attack under different network load, and deduce the suitable moment for the attacker to broadcast the parasite chain to maximize the probability of successful attack. Finally, through detailed numerical simulations, the experimental results comprehensively reveal the performance and security of the Tangle system in the unstable network environment. This article provides a valuable reference for the deployment and performance improvement of Tangle DLT in practical scenarios.
A SQL Injection Attack Recognition Model Based on 1D Convolutional Neural Network

The rapid development of Internet technology and the widespread popularization of web applications have brought a lot of convenience to people’s lives. At the same time, due to the interaction of more user information between browsers and servers, attacks on web applications have intensified, and network security issues occur frequently. SQL injection attacks have always been a common tactic of cyber attackers due to their simplicity and high threat level. Nowadays, SQL injection attacks are emerging in an endless stream, and detection models based on traditional machine learning algorithms have been unable to identify effectively and accurately complex SQL injection attacks. In this paper, we propose a SQL injection recognition model based on one-dimensional convolutional neural network (1D CNN) and combining word embedding and ASCII transcoding techniques. The model can recognize all sorts of SQL injection attacks more efficiently and accurately, dramatically lower human intervention, and offer some defense against 0day attacks that never occur.
Towards Parametric Speech Synthesis Using Gaussian-Markov Model of Spectral Envelope and Wavelet-Based Decomposition of F0

Neural network-based Text-to-Speech has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron2, FastSpeech, FastPitch) usually generate Mel-spectrogram from text and then synthesize speech using vocoder (e.g., WaveNet, WaveGlow, HiFiGAN). Compared with traditional parametric approaches (e.g., STRAIGHT and WORLD), neural vocoder based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust and lack of controllability. In this work, we propose a novel updated vocoder, which is a simple signal model to train and easy to generate waveforms. We use the Gaussian-Markov model toward robust learning of spectral envelope and wavelet-based statistical signal processing to characterize and decompose F0 features. It can retain the fine spectral envelope and achieve high controllability of natural speech. The experimental results demonstrate that our proposed vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder, slightly better than WaveNet, and somewhat worse than the WaveRNN.
PTS: A Prompt-based Teacher-Student Network for Weakly Supervised Aspect Detection

Most existing weakly supervised aspect detection algorithms utilize pre-trained language models as their backbone networks by constructing discriminative tasks with seed words. Once the number of seed words decreases, the performance of current models declines significantly. Recently, prompt tuning has been proposed to bridge the gap of objective forms in pre-training and fine-tuning, which is hopeful of alleviating the above challenge. However, directly applying the existing prompt-based methods to this task not only fails to effectively use large amounts of unlabeled data, but also may cause serious over-fitting problems. In this paper, we propose a lightweight teacher-student network (PTS) based on prompts to solve the above two problems. Concretely, the student network is a hybrid prompt-based classification model to detect aspects, which innovatively compounds hand-crafted prompts and auto-generated prompts. The teacher network comprehensively considers the representation of the sentence and the masked aspect token in the template to guide classification. To utilize unlabeled data and seed words intelligently, we train the teacher and student network alternately. Furthermore, in order to solve the problem that the uneven quality of training data obviously affects the iterative efficiency of PTS, we design a general dynamic data selection strategy to feed the most pertinent data into the current model. Experimental results show that even given the minimum seed words, PTS significantly outperforms previous state-of-the-art methods on three widely used benchmarks.
Learning Deep Co-Occurrence Features

We exploit the computational capability of deep convolutional neural network (CNN) architecture and the natural interpretability of the co-occurrence matrix (CM) to learn deep co-occurrence features (DCOFs). The DCOFs represent the statistics of the co-occurrences of pixels thus overcoming the black box nature of traditional deep representation learning while at the same time solving the inherent computational difficulty of CM. We propose a parametric co-occurrence matrix (PCM) model to approximate the CM with multivariate Gaussian functions, and have developed three approaches to decomposing the PCM model into linear and nonlinear operations such that the model can be easily implemented using standard CNN operations and to learn the DCOFs of arbitrary shapes. The CNN implementation of the PCM model, termed PCMCNN, can be used as a standard plugin module of a deep learning system and adaptively learns the DCOFs for downstream applications. We demonstrate the broad applicability of the DCOFs and their effectiveness in fine-grained image classification tasks such as texture classification and GAN (generative adversarial network) image detection. The introduction of the PCMCNN module makes it much more compact and efficient than conventional implementations of deep learning models, achieving comparable classification performances to state of the art methods on a variety of benchmarking datasets with models that are more than 30 folds smaller and 11 times less complex. The small model size for learning the DCOFs makes the new method particularly effective for few shot classification of large number of texture categories where the small number of training samples can easily cause traditional deep learning models to overfit. This work shows the potential benefits of combining the principles of traditional handcrafted features and deep representation learning to take advantage of both for advancing state of the art.
Unsupervised Denoising for Super-Resolution (UDSR) of Real-World Images

Single Image Super-Resolution (SISR) using Convolutional Neural Networks (CNNs) for many applications in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">supervised</i> manner has resulted in state-of-the-art results. Such supervised models achieve remarkable accuracy; albeit their poor generalization ability for real-world Low-Resolution (LR) images. Supervised training in many SR works involves synthetically generated LR images from its corresponding High-Resolution (HR) images. As the distribution of such LR observation is relatively different from that of real LR image, the supervised training in SISR task results degradation when applied on real-world data. SISR has been scaled to real-world data recently by posing the unsupervised problem into a supervised one through learning the distribution of noisy LR observation first, following which supervised training is performed to obtain the SR image. It therefore involves two steps where the accuracy of SR image relies on how closely the LR’s distribution is learnt in the first step. In this work, we overcome such limitation by introducing unsupervised denoising network to transform real noisy LR image to clean image and then pre-trained SR network is utilised to increase the spatial resolution of cleaned LR image to generate SR image. Thus, instead of evaluating the denoised image in LR space to train the denoising network, we inspect the denoised image in SR space which allows to overcome the SR network’s generalization problem. The proposed Unsupervised Denoising framework for Super-Resolution ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UDSR</i> ) is validated on real-world datasets (NTIRE-2020 Real-World SR Challenge validation and testing dataset (Track-1)) by comparing it with many recent unsupervised SISR methods. The performance of denoising and SR networks is superior in terms of various perceptual indices such as Perceptual Index (PI) and Ma Score in addition to numerous non-references metrics.
Fakequipo: Deep Fake Detection

Deep learning have a variety of applications in different fields such as computer vision, automated self-driving cars, natural language processing tasks and many more. One of such deep learning adversarial architecture changed the fundamentals of the data manipulation. The inception of Generative Adversarial Network (GAN) in the computer vision domain drastically changed the way how we saw and manipulated the data. But this manipulation of data using GAN has found its application in various type of malicious activities like creating fake images, swapped videos, forged documents etc. But now, these generative models have become so efficient at manipulating the data, especially image data, such that it is creating real life problems for the people. The manipulation of images and videos done by the GAN architectures is done in such a way that humans cannot differentiate between real and fake images/videos. Numerous researches have been conducted in the field of deep fake detection. In this paper, we present a structured survey paper explaining the advantages, gaps of the existing work in the domain of deep fake detection.
A Learning Preference Analysis Method Based on a Novel Developed Teaching Skill Training App for Mobile Learning

The emergence of mobile learning makes the learning no longer limited by time and space, and also gives a new way for teaching skill training of normal university students. In this paper, we proposed a learning preference analysis method to explore learning behavior characteristics from the training data based on a novel developed teaching skill training App. Four indexes including frequency, time, media and location preferences are selected to analyze the learning interest of users during mobile learning based skill training. By using visual analysis through Python and K-means clustering algorithm in data mining, the changes of users' learning preferences over time, the preference degree for different skills, and when and where users are more willing to learn knowledge are obtained. By using the learning preference analysis method, learners realize personalized learning, and teachers and developers can adaptively adjust training content and training route in time to improve the skill training efficiency.
Selection of the most accurate training algorithm in the backpropagation network based on the accuracy of data pattern matching

Backpropagation is a supervised learning algorithm that is widely used to solve problems. There are 12 types of training algorithms contained in the Backpropagation network. These algorithms need to be tested to get the most accurate training algorithm in the accuracy of data pattern matching. In this study, the twelve algorithms were tested using two neuron models, namely 15-20-1 and 15-25-1. In this test, the main network parameters used are target error=0.001 with 12 variations of learning rate, namely 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1. Based on the results of the ANOVA test with alpha (α)=5%, the results show that the Levenberg-Marquardt (LM) algorithm is the most accurate algorithm for both neuron models with an average difference between the network output data and the target (delta) of 0.00577 and 0.00515, respectively. This delta value is achieved at lr=0.2 for the neuron 15-20-1 model and lr=0.8 for the 15-25-1 neuron model. Thus, the LM algorithm can be considered in choosing a training algorithm for application development in the field of artificial neural networks.
Language Model-Based Spell-Checker for Sri Lankan Names and Addresses

Names are used abundantly in various applications, but traditional spell-checkers are not adapted to correcting errors in names. In this research, we suggest a spell-checker for Sri Lankan names and addresses. The main challenge in building a spell checker for names is the inability to create a comprehensive dictionary. Our spell-checker overcomes this challenge by utilizing a language model for evaluating the validity of names and a non-Dictionary suggestion generator. The resulting spell-checker boasts performance of up to 96% suggestion adequacy. This spellchecker can be used in applications directly, and the components built can be repurposed for other named entity-related research.
Introduction to Bayesian Decision Theory (BDT)

This chapter provides a simple introduction to Bayesian decision theory (BDT) and its core idea: maximising the expectation for utility. We keep the mathematics to a minimum and develop the ideas using a general graphical model for decision-making, referring to the literature for details.
The utilization of augmented reality on online learning: The impact for students’ physics problem-solving ability

Augmented Reality is a technology that combines the virtual world (virtual) and the real world (real) in real-time. This technology is suitable as the learning media due to provides a real learning experience for students. Therefore, this research has aimed to determine the effectiveness of augmented reality in online learning during the covid-19 pandemic to students’ physics problem-solving ability. The true-experimental method was used for this research which the Randomized Control Group Pretest-Posttest Design as the design research. The participant of this research were 69 students that divided into two groups (experimental class and control class). The initial similarity results indicated that both the experimental class and the control class were normal and homogeneous samples. The independent t-test result showed that the integrating of augmented reality into online learning in pandemic covid-19 gave a significant impact on the students’ problem-solving ability. Moreover, the addition of Augmented Reality to the learning process provided a real and direct experience for students in a physics phenomenon so the students could easily imagine the phenomena to solve the problems.
Digital Geographies and Digital Surveillance Technologies: Power and Space in the Italian Society Under Control for Public Health

Over the past decades, digital technologies have been exploited to supply traditional measures employed to trace people’s behavior. This chapter aims to discuss the current field of actual surveillance and the age of surveillance capitalism with its contradictions. The main focus is on the Italian technology solutions deployed to tackle the spread of COVID-19. This chapter analyzes the interaction between the procedures shaped by the Italian surveillance technologies implemented to discipline (e.g., social distancing) and control (e.g., prescribing spatial behavior) forms of governmentality. Another aspect of this research explores the very complex stratification of public and private spaces producing new spatiality during the pandemic. A preliminary analysis of surveillance technologies deployed to struggle COVID-19 is reviewed and their biopolitical power in the current exceptional state discussed in the specific case of Italy. Two perspectives, a biopolitical one and data justice, provide an essential lens of reading to understand the effects of surveillance technologies in the Italian society during the pandemic, leading to a further progress in the phenomenon of surveillance capitalism to which we have all been subjected for a long time.
A feature catcher with excellent deblurring effects

Camera shake and object movement are the two prime causes of blurred images. Efficient feature extraction is crucial for deblurring. Although the existing methods have achieved remarkable achievements in the deblurring task, there is still room for improvement in effects. In this paper, we propose an efficient architecture called the feature catcher network(FCN). In this multi-stage FCN architecture, the following design allows us to achieve improvements in performance. Firstly, we propose to apply different calculated trust ratios to the output results of different stages before calculating losses and then carry out the cumulative evaluation to update parameters for backpropagation. Secondly, we have improved Transformer to create a query-key mechanism that is effect-friendly to the deblurring task. Thirdly, we propose a multi-stage attention block to make up for the loss of information in high-level feature extraction. And the enhanced feature extraction block is employed to capture detailed information to ensure a greater degree of image recovery. Fourthly, besides considering detailed features and high-level features at the same stage, we also construct residual supplements for blurry images in the raw information mechanism. The experimental results on several datasets demonstrate that our model(FCN) outperforms state-of-the-art methods in terms of deblurring effect. The code and models will be available at https://github.com/XinyueZhangqdu/FCN.
Errare humanum est?

Olivaw is an AI Othello playing agent which autonomously learns how to improve its gameplay by playing against itself. Some top-notch players (including former World Champions) reported that they had the impression that Olivaw's gameplay was human-like. To better investigate the processes related to these impressions, we conducted a pilot study using the Othello Game Evaluation App, a computer application we developed to evaluate pre-recorded Othello games in a controlled setting while assuring an adequate user experience. An exploratory analysis of the results shows that the participants mostly evaluated Olivaw as a human. When asked for a motivation for their choice, some of them reported that they evaluate poor game moves (and, consequently, losing the game) as an indication of the human-likeness of the player.
Reconfigurable Multifunction Computing Unit Using an Universal Piecewise Linear Method

Computing units for nonlinear complex functions are indispensable in deep neural network training processors. However, the existing computing units for nonlinear complex functions have low utilization efficiency and poor agreement and precision. In this article, we propose a multifunction computing unit for training deep neural networks by reusing computing resources based on a piecewise linear (PWL) method to improve computing density. Based on the state-of-the-art segmentor of PWL method, multiple nonlinear functions are divided into the fewest segments with the same bit width of computation. In hardware implementation, the reconfigurable technique is implemented on multiple functions while reusing computing resources including the multiplier and adder. The application-specific integrated circuit (ASIC) implementation results reveal that the architecture with reuse reduces the area by 44.50% and the power by 43.71% at the same frequency, when compared with the architecture without reuse.
ThreMA: Ontology-Based Automated Threat Modeling for ICT Infrastructures

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Threat Modelling</i> allows defenders to identify threats to which the target system is exposed. Such a process requires a detailed infrastructure analysis to map threats to assets and to identify possible flaws. Unfortunately, the process is still mostly done manually and without the support of formally sound approaches. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Threat Modelling</i> often involves teams with different levels of security knowledge, leading to different possible interpretation in the system under analysis representation. Threat modelling automation comes with two main challenges: (i) the need for a standard representation of models and data used in various stages of the process, establishing a formal vocabulary for all involved parties, and (ii) the requirement for a well-defined inference rule set enabling reasoning process automation for threat identification. The paper presents the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ThreMA</i> approach to automating threat modelling for ICT infrastructures, aiming at addressing the key automation issues through the use of ontologies. Specifically, a formal vocabulary for modelling an ICT infrastructure, a threat catalog and a set of inference rules needed to support the reasoning process for threat identification are provided. The proposed approach has been validated against actual significant case studies provided by different Stakeholders of the Italian Public Sector.