Reviewerly/exHarmony huggingface dataset:
Ultra-lightweight face activation for dynamic vision sensor with convolutional filter-level fusion using facial landmarks

As dynamic vision sensors can operate at low power while having a fast response, they can mitigate the disadvantages of gyro sensors when used for turning on mobile devices. Therefore, we propose an ultra-lightweight face activation neural network that combines handcrafted convolutional landmark filters extracted from facial features with randomly initialized trainable convolutional filters. Face activation is the task of identifying the presence or absence of a face intended to activate the mobile device. Our proposed model, F-LandmarkNet, has four steps. First, we construct customized landmark filters that can effectively identify numerous facial features. Second, F-LandmarkNet is constructed by using a convolutional layer that fuses handcrafted landmark filters and trainable convolution filters. Third, a compact version is constructed by selecting only the four most influential face filters according to their importance. Finally, performance is improved through knowledge distillation. The fusion of handcrafted landmark filters and trainable convolutional filters is quite effective in extremely lightweight models. It is observed that the classification accuracy of our proposed model is similar to that of existing lightweight convolutional neural network models, while the number of floating-point operations and parameters are markedly lower. Our model also runs faster under a central processing unit environment than comparison models. Thus, the proposed model shows high potential for use in actual mobile systems.
Data Preparation For Machine Learning Modelling

The world today is on revolution 4.0 which is data-driven. The majority of organizations and systems are using data to solve problems through use of digitized systems. Data lets intelligent systems and their applications learn and adapt to mined insights without been programmed. Data mining and analysis requires smart tools, techniques and methods with capability of extracting useful patterns, trends and knowledge, which can be used as business intelligence by organizations as they map their strategic plans. Predictive intelligent systems can be very useful in various fields as solutions to many existential issues. Accurate output from such predictive intelligent systems can only be ascertained by having well prepared data that suits the predictive machine learning function. Machine learning models learns from data input using the ‘garbage-in-garbage-out’ concept. Cleaned, pre-processed and consistent data would produce accurate output as compared to inconsistent, noisy and erroneous data.
An Approach to Oral English Assessment Based on Intelligent Computing Model

“English craze” has become a key topic of concern to the majority of the people. Apart from setting English as a compulsory course like Chinese and Mathematics in schools, various English training institutions outside the school are also emerging one after another. Due to the English teaching mode in class, dumb English appears. In recent years, with the popularity of virtual electronic devices, more and more researchers try to use virtual reality (VR) to create an immersive English learning environment. Oral English teaching is an important part of the whole English teaching. In the traditional English classroom teaching practice, teachers’ pronunciation is not standard, and students are difficult to learn the correct pronunciation standard, which makes oral English very passive. The most important problem in oral English teaching is to improve students’ interest in oral English, make students willing to speak and realize English communication. Oral English teaching is an important link in both primary and secondary schools and universities. Teachers are afraid of nonstandard pronunciation in limited classrooms, and they are afraid to speak and unwilling to speak, which leads to passive oral English teaching. Therefore, this paper will set up an intelligent computing model to evaluate and analyze spoken English in a standard and accurate way. Artificial intelligence speech synthesis and imitation of voice change are typical applications of decoupling representation learning in speech, the oral evaluation is based on the proposition that speech is a dynamic and complex process. With the help of the rapidly developed computer speech synthesis and imitation technology, an oral evaluation path based on speech synthesis and imitation is proposed, that is, oral evaluation is carried out by using the network parameters and output of deep learning of computer speech imitation.
On Theoretical Foundations of Human and Robot Vision

Abstract A set of cognitive, neurological, and mathematical theories for human and robot vision has been recognized that encompasses David Hubel’s hypercolumn vision theory (The Nobel Prize in Physiology or Medicine 1981 [1]) and Dennis Gabor’s wavelet filter theory (The Nobel Prize in Physics 1971 [2]). This keynote lecture presents a theoretical framework of the Cognitive Vision Theory (CVT) [3-6] and its neurological and mathematical foundations. A set of Intelligent Mathematics (IM) [7-13] and formal vision theories developed in my laboratory is introduced encompassing Image Frame Algebra (IFA) [3], Visual Semantic Algebra (VSA) [4], and the Spike Frequency Modulation (SFM) theory [5]. IM is created for enabling cognitive robots to gain autonomous vision cognition capability supported by Visual Knowledge Bases (VKBs). Paradigms and case studies of robot vision powered by CVTs and IM will be demonstrated. The basic research on CVTs has led to new perspectives to human and robot vision for developing novel image processing applications in AI, neural networks, image recognitions, sequence learning, computational intelligence, self-driving vehicles, unmanned systems, and robot navigations.
Hardness for triangle problems under even more believable hypotheses: reductions from real APSP, real 3SUM, and OV

The 3SUM hypothesis, the All-Pairs Shortest Paths (APSP) hypothesis and the Strong Exponential Time Hypothesis are the three main hypotheses in the area of fine-grained complexity. So far, within the area, the first two hypotheses have mainly been about integer inputs in the Word RAM model of computation. The “Real APSP” and “Real 3SUM” hypotheses, which assert that the APSP and 3SUM hypotheses hold for real-valued inputs in a reasonable version of the Real RAM model, are even more believable than their integer counterparts.
Safety Helmet Detection Dynamic Model Based on the Critical Area Attention Mechanism

In the substation monitoring environment, monitoring the helmet wearing of workers is an important approach to ensure safety. Because the helmet size in entire surveillance images is often small and the characteristic information is unclear, existing target detection algorithms present the problem of missed detection and omission. A safety helmet detection dynamic model based on the critical area attention mechanism first detected the human target in the image, and then, locked the human head area through the critical area attention mechanism network. Finally, the feature map of the critical areas of the head was up-sampled many times to increase the proportion of the helmet area in the image to highlight the characteristic information of the helmet in the image. The algorithm used the dynamic model method to match the optimum up-sampling times for the helmets of different scales, which improved the recognition speed of the algorithm while ensuring the recognition accuracy. The experimental results showed that the recognition rate of algorithm for helmets was 92.68%, which is considerably higher than that of other target detection algorithms in the same field.
Speech Steganography based on Double Approximation of LSFs Parameters in AMR Coding

A steganographic method for VoIP applications based on line spectral frequency (LSF) modification, is proposed in this paper. The aim of this research is to securely transmit secret binary data into AMR cover bitstream. The proposed scheme is Newton-based interpolation of the LSF coefficients of the four sub-frames, alongside with the original linear interpolation. Newton interpolation gives a perfect similarity to linear interpolation when there is no secret information is being transmitted. Besides that, Newton interpolation provides a high flexibility, which doesn’t exist in linear interpolation, to modify the eight LSF coefficients from within ten, excluding the first and the tenth LSFs. The results show that after a slight modification of ±0.01 (D) during LSF interpolation using our proposed scheme, the intelligibility is perceived, the maximum steganographic quality change is 0.08 MOS. The proposed scheme provides a high capacity of 1.05 kbps, a high undetectability, and security level against statistical steganalysis. Hence, our proposed steganographic method is effective, secure and, avoid rising the suspicious to eavesdropper who is listening to the channel.
Ballistic missile tracking in the presence of decoys using space base IR sensors

This paper addresses one of the key requirements for a successful terminal phase defense intercept, namely the ability to discriminate between the reentry vehicle (RV) and decoys, using space-based infrared (IR) sensors. In the terminal phase, light objects (decoys) slow down faster due to atmospheric drag and follow substantially different trajectories than heavy objects (RV). Therefore, the targets' velocity information will be used to differentiate between the RVs and decoys trajectories within a validating time window. The evaluation of the corresponding Cram&eacute;r-Rao Lower Bound (CRLB) on the covariance of the estimates, and the statistical tests on the results of simulations show that this method is statistically efficient.
Research and Application of Key Technologies of Ocean Virtual Scene Display Based on Digital Image

Digital imaging technology originated from scientific and technological competitions in the military field, allowing continuous high-intensity digital photography work. The ocean virtual scene display of digital images is more flexible and more tolerant. The ocean virtual scene can be used to model the structure of different regions, create a diversified ocean experience pavilion as much as possible, and create a multi-dimensional visual product. Through this research, we get: 1. The best frame rate is 100.607 when High Dynamic Range (HDR) is off and 98.47 when HDR is on. When A =0.0161, Me =26, K1 = 0.6488, G =28, K2 = -0.0072, α =58, Omega =2.5229, β =78. The simulation experiment of ocean scene construction shows that it only needs to calculate the ocean virtual scene of LONC area, and repeatedly set and splice the ocean virtual scene to form a large modeling scene. 2. When the FFT model is optimized, the parameters are set as follows: Meta Flight =0.946, Open Flight =0.441, α =0.828, Omega =089, β =0.754; The optimal parameters of multiple kernel learning model in ocean virtual scene are: Meta Flight =0.757, Pen Flight =0.818, α =0.781, Omega =0.157, β =1.739; The best parameters of the ocean virtual scene of Vega Prime model are: Meta Flight =0.285, Open Flight =0.803, α =0.701, Omega =0.725, and β =0.757. And the constructed ocean virtual scene can achieve the best effect. 3. In the FFT model, when the related technical parameters are set optimally, the two-dimensional animation is integrated into the digital image as a visual special effect element or an animation subtitle, so that the digital image is more interesting, creative and propagable.
Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification

Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they are computationally very demanding. The aim of our study is to reduce their computational cost to enable their use with large tissue image datasets.We propose a method called Network Auto-Reduction (NAR) that simplifies a Convolutional Neural Network (CNN) by reducing the network to minimize the computational cost of doing a prediction. NAR performs a compound scaling in which the width, depth, and resolution dimensions of the network are reduced together to maintain a balance among them in the resulting simplified network. We compare our method with a state-of-the-art solution called ResRep. The evaluation is carried out with popular CNN architectures and a real-world application that identifies distributions of tumor-infiltrating lymphocytes in tissue images.The experimental results show that both ResRep and NAR are able to generate simplified, more efficient versions of ResNet50 V2. The simplified versions by ResRep and NAR require 1.32× and 3.26× fewer floating-point operations (FLOPs), respectively, than the original network without a loss in classification power as measured by the Area under the Curve (AUC) metric. When applied to a deeper and more computationally expensive network, Inception V4, NAR is able to generate a version that requires 4× lower than the original version with the same AUC performance.NAR is able to achieve substantial reductions in the execution cost of two popular CNN architectures, while resulting in small or no loss in model accuracy. Such cost savings can significantly improve the use of deep learning methods in digital pathology. They can enable studies with larger tissue image datasets and facilitate the use of less expensive and more accessible graphics processing units (GPUs), thus reducing the computing costs of a study.
Cryptocurrency Shock and Exchange Rate Behaviour in Nigeria

This study examined the relationship between cryptocurrency shocks and exchange rate behaviour in Nigeria. Selected cryptocurrencies for the study are Bitcoin, Ethereum, Litecoin, Ripple and Binance coin which are the most traded cryptocurrencies in Nigeria. Augmented Dickey-Fuller (ADF), Johansen Cointegration and Vector Autoregressive (VAR) tests were used to analyze the monthly data of exchange rate and selected cryptocurrencies for four years (45 months). The result of the cointegration test revealed the existence of a long-run relationship among the variables. ECM result showed that about 6% of the short-run disequilibrium are being corrected and integrated into the long-run equilibrium relationship. In addition, the Variance Decomposition result showed that Ripple has the highest variations to exchange rate in the short and long runs. The present value of exchange rate adjusts slightly to changes in cryptocurrency. Ripple and Bitcoin have the highest shocks on the exchange rate. Therefore, monetary authorities should give adequate attention to cryptocurrency transactions and make policy decisions on how to reduce the prevailing high exchange rate in Nigeria by integrating crypto transactions in their systems. Transaction in cryptocurrency is still at the early stage, especially in Nigeria; only five years data can be gotten on commonly traded cryptocurrencies in Nigeria. This is a limitation to the study in terms of the number of cryptocurrencies used in the study. More cryptocurrencies can be included in future studies.
Hybrid tree model for root cause analysis of wireless network fault localization

Localizing the root cause of network faults is crucial to network operation and maintenance. Operational expenses will be saved if the root cause can be identified accurately. However, due to the complicated wireless environments and network architectures, accurate root cause localization of network falut meets the difficulties including missing data, hybrid fault behaviors, and short of well-labeled data. In this study, global and local features are constructed to make new feature representation for data sample, which can highlight the temporal characteristics and contextual information of the root cause analysis data. A hybrid tree model (HTM) ensembled by CatBoost, XGBoost and LightGBM is proposed to interpret the hybrid fault behaviors from several perspectives and discriminate different root causes. Based on the combination of global and local features, a semi-supervised training strategy is utilized to train the HTM for dealing with short of well-labeled data. The experiments are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge, and the results show that the global and local feature based HTM achieves the best model performance comparing with other models. Meanwhile, our solution achieves third place in the competition leaderboard which shows the model effectiveness.
Characterizing the expertise of Aircraft Maintenance Technicians using eye-tracking.

Aircraft maintenance technicians (AMTs) play an essential role in life-long security of helicopters. There are two major types of operations in maintenance activity: information intake/processing and motor actions. Modeling expertise of the AMT is the main objective of this doctoral project. Given the constraints of real-world research, mobile eye-tracking appears to be an essential tool for the measurement of information intake, notably concerning the use of maintenance documentation during the maintenance task preparation and execution. . This extended abstract will present the main research objectives, our approach and methodology and some preliminary results.
A Toxic Content Detection Technique in Sentimental Analysis with Convolution Neural Networks

In the recent time, the usage of various social media platforms has drastically increased which involves the positive or negative impact on human lives. One of the aspects is directly associated with comment/opinion which individual user/person passes through different social networking sites. This paper provides the discussion on different approaches to analyze classification and modelling techniques for detecting toxic comments using convolution neural networks (CNN). It also specifies algorithm based on outlier detection on given data stream that can evaluate the posted material on social platforms and check its positive and negative impact on the society.
Human Action Recognition Based on Multi-Scale Feature Augmented Graph Convolutional Network

Nowadays, video has gradually become the mainstream media of communication, and the massive amounts of videos bring challenge to the task of manual review of the videos. So, using computers to understand the videos is of great significance. Among the approaches of automatic action recognition, skeleton-based approach has many advantages, such as strong robustness to light changes, strong action expression ability, small amount of computation time, etc. In this paper, a multi-scale feature augmented graph convolutional network is proposed. It uses the spatial multi-scale GCN module to extract spatial features of different scales, the multi-scale temporal augmentation module to capture temporal features of different scales. To prove the performance of the proposed method, experiments were performed on two public datasets, NTU-RGB+D and The Kinetics-Skeleton. Compared with other advanced action recognition methods, the proposed method can accomplish action recognize effectively, and the recognition accuracy is improved.
A Prioritizing Interdiction Surface-Based Vulnerability Remediation Composite Metric for Industrial Control Systems

Recently, industrial control system (ICS) has gradually been a primary attack target. The main reason is that increasing vulnerabilities exposed provide opportunities for launching multistep and multihost attacks to breach security policies. To that end, vulnerability remediations are crucial for the ICS. However, there exist three problems to be tackled in a sound way. First of all, it is impractical to remove all vulnerabilities for preventing the multistep and multihost attacks in the consideration of the actual ICS demands. Secondly, ranking vulnerability remediations lacks a guidance. The last problem is that there is a lack of a metric for qualifying the security level after each remediation. In this paper, an ICS-oriented assessment methodology is proposed for the vulnerability remediations. It consists of three phases corresponding to the above problems, including (1) prioritizing Interdiction Surfaces, (2) ranking vulnerability remediations, and (3) calculating composite metrics. The Interdiction Surface describes a minimum set of vulnerabilities of which the complete removal may interdict all discovered attack paths in the system. Particularly, it innovates to take the urgent security demands of the ICS into account. Subsequently, ranking the vulnerability in the optimal Interdiction Surface is conducive to guide the remediations with the priority. A composite metric is ultimately given to assess the security level after vulnerability remediations. The effectiveness of the proposed methodology is validated in an ICS scenario which is similar to the real-world practice. Results show that the entire procedure is suitable for the context of the ICS. Simultaneously, the composite metric enhances both the comprehensiveness and the compatibility in contrast with attack path-based metrics. Hence, it overcomes the shortcomings when they are used in isolation.
WRNFS: Width Residual Neuro Fuzzy System, a Fast-Learning Algorithm with High Interpretability

Although the deep neural network has a strong fitting ability, it is difficult to be applied to safety-critical fields because of its poor interpretability. Based on the adaptive neuro-fuzzy inference system (ANFIS) and the concept of residual network, a width residual neuro-fuzzy system (WRNFS) is proposed to improve the interpretability performance in this paper. WRNFS is used to transform a regression problem of high-dimensional data into the sum of several low-dimensional neuro-fuzzy systems. The ANFIS model in the next layer is established based on the low dimensional data and the residual of the ANFIS model in the former layer. The performance of WRNFS is compared with traditional ANFIS on three data sets. The results showed that WRNFS has high interpretability (fewer layers, fewer fuzzy rules, and fewer adjustable parameters) on the premise of satisfying the fitting accuracy. The interpretability, complexity, time efficiency, and robustness of WRNFS are greatly improved when the input number of single low-dimensional systems decreases.
Combinatory logic and combinators in array languages

The array language paradigm began in the 1960s when Ken Iverson created APL. After spending over 30 years working on APL, he moved on to his second array language J, a successor to APL which embeds a significant subset of combinatory logic in it. This paper will look at the existence of combinators in the modern array languages Dyalog APL, J and BQN and how the support for them differs between these languages. The paper will also discuss how combinators can be a powerful language feature as they exist in modern array languages.