Reviewerly/exHarmony huggingface dataset:
An Energy-Efficient SIFT Based Feature Extraction Accelerator for High Frame-Rate Video Applications

Visual feature extraction is a key technology of computer vision for intelligent video processing. Efficient feature extraction is a fundamental problem in computer vision applications. Scale-Invariant Feature Transform (SIFT) is one of the most popular feature extraction algorithms because SIFT features are invariant to image scale and rotation and robust to changes in illumination and noise. However, SIFT is a computationally-intensive and power-hungry algorithm, which needs to be accelerated by efficient hardware design to achieve both high-speed feature extraction and high energy efficiency for many high frame-rate video applications at Artificial-intelligent Internet of Things edges. In this work, an energy-efficient SIFT based feature extraction accelerator is proposed. In the Gaussian pyramid and Differences of Gaussian (DoG) pyramid construction process, three design methods are proposed to reduce power consumption and improve information fidelity: a fast and slow dual clock domain design method with a reconfigurable design strategy is proposed to reduce the computation resources; a partial sum reuse design method is proposed to further reduce the computation resources and the amount of computation; a dynamic padding design method is proposed to solve the problem of information loss at image edges and corners after convolution operation. In the keypoint descriptor generation process, an optimized algorithm using circular region and polar coordinates is proposed to parallelize the main orientation assignment and descriptor generation to achieve high-speed processing, while maintaining a comparable matching accuracy with the state-of-the-art designs. The experiment results show that the proposed SIFT hardware accelerator is able to extract features by up to 162 frames per second ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times 480$ </tex-math></inline-formula> pixels) under 100 MHz, with the power consumption of 364.26 mW and energy efficiency of 2.25 mJ/frame based on 180 nm technology, which is suitable for many high frame-rate AIoT applications including autonomous driving cars and unmanned aerial vehicles.
A Stackelberg Game and Federated Learning Assisted Spectrum Sharing Framework for IoV

With the rapid development of Internet of Vehicles (IoV), an increasing number of vehicular users (VUEs) will connect to the Internet via 5G and beyond 5G(B5G) networks, which makes the spectrum resource becoming extremely scarce. However, the traditional spectrum allocation method cannot well adapt to the explosive growth of IoV traffic, and an efficient spectrum management for the IoV in B5G networks is an urgent challenge. In this paper, we propose a Stackelberg game and federated learning assisted spectrum sharing framework for the IoV. First, we develop a power control strategy for the region nodes considering its revenue and energy consumption, while VUEs can dynamically change their spectrum resource request strategy to maximize their revenue. We find the Stackelberg equilibrium using the alternating direction method of multiplier (ADMM) algorithm. To maximize the global revenue of region nodes, we leverage federated learning to realize the interaction between the region nodes and the central node. In specific, each region node utilizes deep learning to fit the relationship between the allocated power and its revenue. Then, they upload the network parameters to the central node. After collecting the network parameters from all region nodes, the central node can make the global decision about the spectrum allocation to the region nodes. Numerous simulation results verify the effectiveness of the proposed framework compared with the benchmark methods.
CNN Based Target Classification in Vehicular Networks with Millimeter-Wave Radar

Millimeter-wave radar is widely used in intelligent vehicle infrastructure cooperative systems (IVICS) because of its long detection distance, high integration and less affected by weather conditions. The target recognition and classification technology based on millimeter wave radar is a research hotspot. However, traditional recognition methods require a complex feature extraction process. In this paper, a millimeter-wave radar classification method based on deep learning is proposed, which uses the ability of convolutional neural networks (CNN) method to automatically extract feature data, so as to replace most of the complex processes of traditional radar signal processing chain. It solves the four classification tasks of bus, cars, electromobile and pedestrians. Taking the selected feature data as the RV spectrum, a seven layer convolution neural network is built to classify the target. Compared with traditional machine learning methods, CNN greatly reduces the workload of feature engineering, and its classification effect is better than traditional machine learning methods. Then, from the perspective of redundancy of the convolution kernels, we simplify the CNN architecture. As a result, final classification accuracy of the CNN is 93.16%. After simplifying the structure of the CNN, the computational complexity and storage consumption of the network are reduced by 74.67% and 73.71%, respectively, while ensuring that the classification accuracy does not decline.
Evaluation of Probability Distribution Distance Metrics in Traffic Flow Outlier Detection

Recent approaches have proven the effectiveness of local outlier factor-based outlier detection when applied over traffic flow probability distributions. However, these approaches used distance metrics based on the Bhattacharyya coefficient when calculating probability distribution similarity. Consequently, the limited expressiveness of the Bhattacharyya coefficient restricted the accuracy of the methods. The crucial deficiency of the Bhattacharyya distance metric is its inability to compare distributions with non-overlapping sample spaces over the domain of natural numbers. Traffic flow intensity varies greatly, which results in numerous non-overlapping sample spaces, rendering metrics based on the Bhattacharyya coefficient inappropriate. In this work, we address this issue by exploring alternative distance metrics and showing their applicability in a massive real-life traffic flow data set from 26 vital intersections in The Hague. The results on these data collected from 272 sensors for more than two years show various advantages of the Earth Mover's distance both in effectiveness and efficiency.
An LSTM-based Intent Detector for Conversational Recommender Systems

With the rapid development of artificial intelligence (AI), many companies are moving towards automating their services using automated conversational agents. Dialogue-based conversational recommender agents, in particular, have gained much attention recently. The successful development of such systems in the case of natural language input is conditioned by the ability to understand the users&#x2019; utterances. Predicting the users&#x2019; intents allows the system to adjust its dialogue strategy and gradually upgrade its preference profile. Nevertheless, little work has investigated this problem so far. This paper proposes an LSTM-based Neural Network model and compares its performance to seven baseline Machine Learning (ML) classifiers. Experiments on a new publicly available dataset revealed The superiority of the LSTM model with 95&#x0025; Accuracy and 94&#x0025; F1-score on the full dataset despite the relatively small dataset size (9300 messages and 17 intents) and label imbalance.
Proof-of-Communication-Capability Based Authentication in Blockchain-enabled Wireless Autonomous Vehicular Networks

Blockchain technology is finding applications in wireless networks, in particular vehicular networks, for the purposes of establishing trust. A certain set of network resources in terms of communication and computation requirements is also necessary for successful blockchain deployment. Moreover, heterogeneity in wireless networks and changing radio conditions at the physical layer, make it even more challenging to guarantee steady block generation latency. In this work, we study the possible denial of blockchain service attacks where malicious nodes threaten to slow down the block generation latency. We propose a novel Proof-of-Communication-Capability (PoCC) authentication framework that acts as a defense against communication capability spoofing over wireless networks. Our PoCC authentication framework utilizes physical properties such as distance between nodes, channel state information (CSI), and communication puzzle latency to establish the communication capabilities of nodes in the wireless network. Results from a simulated AV network under three different variations of the proposed PoCC framework are encouraging and demonstrate that such attacks can be effectively mitigated.
Online Simulation Quality Assessment of Illustration Patterns Based on Digital Art Design in Neural Network Perspective

Digital art illustration as new media is a major advancement in the history of illustration. It has broadened the application of graphic arts and the way of communication. At present, digital illustrators are constantly exploring new artistic expressions for digital illustration in order to pursue new digital effects. A wide range of integrated materials and experimental design concepts are gradually emerging, making digital illustration evolve in the competition. The artistic style of digital illustration has also shown the development trend of “diversification.” With the trend of commercialization of digital illustration, the quality of illustration patterns is gradually neglected. The traditional illustration pattern assessment relies on manual subjective judgment, with backward evaluation means and poor accuracy. This study proposes an illustration pattern evaluation method based on a deep neural network. In particular, this study proposes a reference-free image evaluation model with multiple feature fusion; specifically, we use CNN and information entropy-based methods for feature extraction and regularization methods for information fusion to solve the problem of missing reference images in applications. The chunking process is performed on the basis of considering the influence of information entropy on image quality, and the information entropy of multiple chunked features is calculated as importance weights, representing the degree of their influence on distorted image quality. The experimental results on the digital illustration pattern database show that the method in this study has strong robustness and can output a reasonable and reliable quality assessment score for distorted patterns.
A Data Hiding Technique Based on QR Code Decomposition in Transform Domain

Designed and created by Toyota subsidiary Denso Wave, quick response (QR) code has now been commonly used in many suitable applications to deal with real-time data because it can be read quickly by smartphones or tablets. The covering of information is indeed the technique of keeping secret knowledge inside the source media. Two qualities would have a strong approach to data hiding: high consistency of the stego image as well as high potentiality for concealing. In this paper, a redundant discrete wavelet transform (RDWT) domain-based information hiding method using QR code decomposition is proposed. An appropriate cover is torn down by RDWT mechanism, and the private image is concealed within the sub-bands of the factorized QR code. For the purpose of demonstrating the effectiveness of the proposed approach with respect to imperceptibility, capability and robustness to different signal processing assaults, multiple tests and case analysis are conducted.
Text complexity and linguistic features: Their correlation in English and Russian

Text complexity assessment is a challenging task requiring various linguistic aspects to be taken into consideration. The complexity level of the text should correspond to the reader’s competence. A too complicated text could be incomprehensible, whereas a too simple one could be boring. For many years, simple features were used to assess readability, e.g. average length of words and sentences or vocabulary variety. Thanks to the development of natural language processing methods, the set of text parameters used for evaluating readability has expanded significantly. In recent years, many articles have been published the authors of which investigated the contribution of various lexical, morphological, and syntactic features to the readability level. Nevertheless, as the methods and corpora are quite diverse, it may be hard to draw general conclusions as to the effectiveness of linguistic information for evaluating text complexity due to the diversity of methods and corpora. Moreover, a cross-lingual impact of different features on various datasets has not been investigated. The purpose of this study is to conduct a large-scale comparison of features of different nature. We experimentally assessed seven commonly used feature types (readability, traditional features, morphological features, punctuation, syntax frequency, and topic modeling) on six corpora for text complexity assessment in English and Russian employing four common machine learning models: logistic regression, random forest, convolutional neural network and feedforward neural network. One of the corpora, the corpus of fiction literature read by Russian school students, was constructed for the experiment using a large-scale survey to ensure the objectivity of the labeling. We showed which feature types can significantly improve the performance and analyzed their impact according to the dataset characteristics, language, and data source.
Discourse complexity in the light of eye-tracking: a pilot Russian language study

The paper explores the influence of discourse structure on text complexity. We assume that certain types of discourse units are easier to read than others, due to their explicit discourse structure, which makes their informational input more accessible. As a data source, we use the dataset from the MECO corpus, which contains eye movement data for 12 Russian texts read by 35 native speakers. We demonstrate that the approach relying on elementary discourse units (EDUs) can be felicitously used in the analysis of eye movement data, since fixation patterns on EDUs are similar to those on whole sentences. Our analysis has identified EDU outliers, which show shorter time of first fixation than estimated. We arranged these outliers into several groups associated with different discourse structures. First, these are statements with nominal predicates that set exposition of the text or macroproposition and, following those, EDUs that elaborate on the previous statement and signal the beginning of the narrative. Second, they are EDUs that serve as the middle component of a listing or a group of coordinated clauses or phrases. The final group represents EDUs that are part of an opposition, contrast or comparison. Discourse analysis based on EDUs has never been applied to eye movement data, so our project opens many avenues for further research of complexity of discourse structure.
QR Code Detection

Due to the incorrect image collecting approach, QR code identification frequently confronts obstacles such as uneven backdrop fluctuations, inadequate illuminations, and distortions. As a result, identifying QR codes is difficult, and artificial intelligence-based methods were developed to address this challenge. This article uses an improved adaptive median filter technique and a QR code distortion correction method based on backpropagation (BP) neural networks to increase the identification rate of QR image codes. The distorted QR image can be fitted into the geometric deformation pattern using this combination of artificial intelligence methods, and QR code identification is possible. This paper addresses two-dimensional code distortion, which has been a critical research topic in present software systems. The research findings, which focused on the picture preprocessing step, demonstrated a significant improvement of 14 percent in the reading rate of QR image codes following processing using the system algorithm described in this article. The artificial intelligence technique used improves the recognition rate of the two-dimensional code image to some extent.
Modification of Machine Learning Algorithms for Embedding in Image Compression Methods

The article deals with the modification of machine learning methods for embedding in an digital image compression framework. The modification leads to the need to change both the structure of machine learning methods and the algorithms for tuning the methods. When tuning machine learning methods, we use a quality score that is relevant to the problem of digital image compression. We develop an algorithm for tuning binary classifiers, and then we generalize the algorithm to the situation of tuning arbitrary machine learning algorithms when compressing digital images. We describe the data structures, numeric arrays, and calculation formulas used in the tuning algorithm of the modified machine learning method. We embed the modified machine learning based method into a digital image compression framework. We perform computational experiments to study the effectiveness of modified machine learning methods for natural image compression. Computational experiments confirm the high efficiency of modified machine learning methods for digital image compression.
A Reliable JPEG Quantization Table Estimator

JPEG compression is a commonly used method of lossy compression for digital images.The degree of compression can be adjusted by the choice of a quality factor QF .Each software associates this value to a quantization table, which is a 8 × 8 matrix used to quantize the DCT coefficients of an image.We propose a method for recovering the JPEG quantization table relying only on the image information, without any metadata from the file header; thus the proposed method can be applied to an uncompressed image format to detect a previous JPEG compression.A statistical validation is used to decide whether significant quantization traces are found or not, and to provide a quantitative measure of the confidence on the detection.
Analysis of Dynamic Influence Mechanism of Network Public Opinion Based on Simulation Feature Extraction

In order to improve the effect of network public opinion management, this paper constructs an intelligent computer network simulation model to analyze the dynamic influence mechanism of network public opinion. This paper converts semistructured text information into a form that is easy for computer analysis and recognition, which facilitates opinion extraction and sentiment analysis. Moreover, by analyzing the text content to be mined, this paper uses a specific algorithm to select different words that best represent the text to be tested as the characteristic words of network public opinion. These network public opinion feature words can classify texts, thereby reducing the complexity of text classification. The experimental study shows that the dynamic influence mechanism analysis model of network public opinion based on intelligent computer network simulation constructed in this paper has a good effect.
Machine Learning Approach on Time Series for PV-Solar Energy

Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.
Improved Integer-wise Homomorphic Comparison and Division based on Polynomial Evaluation

Fully homomorphic encryption (FHE) is a promising tool for privacy-preserving applications, and it enables us to perform homomorphic addition and multiplication on FHE ciphertexts without decrypting them. FHE has two types: one supporting the exact computation and the other supporting the approximate computation. Further the FHE schemes supporting the exact computation have two types, bit-wise FHE, which encrypts a plaintext bit by bit, and integer-wise FHE, which encrypts a plaintext as an integer. Both types of FHE are important depending on the types of computation we need to execute securely. In this work, we focus on integer-wise FHE, and propose improved methods for integer-wise homomorphic comparison and division operations. For a comparison operation, we propose a method that halves the number of necessary homomorphic multiplications by introducing an odd function as an interpolated polynomial to be evaluated, as opposed to the previous work of Narumanchi et al. (AINA ’17). For a division operation, as opposed to the previous work of Okada et al. (WISTP ’18), we propose a simple method to reduce the processing time by introducing an equality function based on Fermat’s little theorem without changing the multiplicative depth, and show the analysis of why this approach can achieve better efficiency in detail. In our homomorphic division, the number of interpolated polynomials is reduced by half, thus also achieving the reduction of the processing time of precomputations and the number of polynomials to be stored. We also implement our improved methods in HElib, which is one of popular FHE libraries using the BGV encryption. As a result, we show that, e.g., in the plaintext space , our homomorphic comparison with the Paterson-Stockmeyer method is faster by a factor of about 5.61 compared with Narumanchi et al. (AINA ’17) and our homomorphic division is faster by a factor of about 1.45 compared with Okada et al. (WISTP ’18).
Processing clause-internal discourse relations in a second language

Abstract Coherence relations are expressed differently across languages, often leading to language learners misusing discourse connectives. We argue that the ability to detect these errors crucially depends on the coherence relation under scrutiny, as errors may remain unnoticed when the relation is clause-internal and marked with a highly optional connective. We focus, therefore, on specifications, a relation that German-speaking learners sometimes struggle to correctly indicate when writing in French. We assessed whether non-native readers detect this error and show preferences for either explicit or implicit marking of specifications. Findings show that non-native speakers were generally able to detect the error in a sentence-evaluation task but did not react to it in a self-paced-reading task, contrary to native speakers. They also judged implicit specifications as more correct than explicitly marked specifications. We conclude that non-native speakers do not always benefit from connectives during text processing, especially when they are highly optional.