Reviewerly/exHarmony huggingface dataset:
Lossless Compression for Hyperspectral Images based on Adaptive Band Selection and Adaptive Predictor Selection

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images.Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images.The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context.According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS).Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map.Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band.Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel.In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation.Finally, the prediction residuals are entropy encoded by arithmetic encoder.Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively.The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.
Adaptive deep convolutional neural network for thermal face recognition

An individual's face is a biometric feature that can be used in a computerized security system to identify or authenticate that particular person. The main challenge, while identifying a face through the use of a machine, is to match precisely the captured person's face with the image of the same individual's face already existing in the system's face database. Visual spectrum face images are affected by variations in lighting, head orientation, aging, and disguise resulting in poor visual face detection performance. Infrared imaging is used to help overcome some of these limitations. In this work, we propose a deep Deep Convolutional Neural Network architecture based on the FaceNet architecture and the MTCNN model to perform face recognition on a set of thermal data. Tests conducted on the USTC-NVIE dataset show promising results and the possibility of using deep learning in thermal face recognition.
3D Vehicle Information Recognition Algorithm of Monocular Camera Based onSelf-Calibration in Traffic Scene

<p indent="0mm">Obtaining 3D information of vehicles as the basis for accurate classification of vehicles has become an increasingly important research direction. However, most of the current traffic monitoring cameras are monocular cameras, which cannot directly obtain 3D information of vehicles like pose and size due to perspective factors. According to the above problem, this paper proposes a 3D vehicle information recognition algorithm of monocular camera based on self-calibration in traffic scene. Firstly, this paper builds up a monocular camera model and a stable single vanishing point calibration model according to the typical traffic scene, and completes camera calibration. Then it uses the YOLO deep learning convolution neural network for 2D vehicle detection. Based on this, it puts forward a diagonal and vanishing point constrained non-linear optimization algorithm, combining with the calibration information to complete 3D vehicle information recognition and the best 3D vehicle detection. Finally, the experiment was carried out on the public dataset called BrnoCompSpeed and in highway traffic scenes, and the results show that the algorithm can effectively complete 3D vehicle information recognition in various traffic scenarios with an average recognition accuracy of more than 90%.
Image steganography with visual illusion

Human eyes are often “cheated” by an optical illusion (or visual illusion) so that the perceived image differs from the physical reality. But various optical illusions have been seldom investigated for technological applications such as image processing and optical display in the past. As a unique attempt of combining information technology with optical illusion, we propose a novel image steganography scheme based on a color assimilation illusion. A synthesized image containing a grayscale background and a saturated color line (or point) grid can be perceived as a color image, with external secret data hidden simultaneously.
Review on steganography methods in multi-media domain

Steganography is one area in information security that is able to conceal the secret message in any media to avoid the intruders. In this paper, the review of steganography is done in certain media such as image, text, audio, and video. It analyses some of the techniques that applied steganography to discover the development of the techniques to cover a secret message. It is expected that this paper is able to describe the implementation of steganography by previous researchers on their efforts.
Training and domain adaptation for supervised text segmentation

Unlike traditional unsupervised text segmentation methods, recent supervised segmentation models rely on Wikipedia as the source of large-scale segmentation supervision. These models have, however, predominantly been evaluated on the in-domain (Wikipedia-based) test sets, preventing conclusions about their general segmentation efficacy. In this work, we focus on the domain transfer performance of supervised neural text segmentation in the educational domain. To this end, we first introduce K12Seg, a new dataset for evaluation of supervised segmentation, created from educational reading material for grade-1 to college-level students. We then benchmark a hierarchical text segmentation model (HITS), based on RoBERTa, in both in-domain and domain-transfer segmentation experiments. While HITS produces state-of-the-art in-domain performance (on three Wikipedia-based test sets), we show that, subject to the standard full-blown fine-tuning, it is susceptible to domain overfitting. We identify adapter-based fine-tuning as a remedy that substantially improves transfer performance.
Lane Detection of Smart Car based on Deep Learning

Abstract Deep learning brings higher accuracy to lane detection, and the speed of the model is becoming faster and faster. The lane detection is regarded as the classification task after image gridding, and a simplified lane structure loss function is proposed, which is more suitable for the car track. The lightweight SqueezeNet is used as the backbone network, and the deep learning model is applied to the intelligent car of raspberry pie. Through the experiment, the real-time detection speed reaches 23FPS, which can complete the task of lane tracking. Compared with traditional methods, deep learning model has better robustness.
Characterizing Bell state analyzer using weak coherent pulses

Bell state analyzer (BSA) is one of the most crucial apparatuses in photonic quantum information processing. While linear optics provide a practical way to implement BSA, it provides unavoidable errors when inputs are not ideal single-photon states. Here, we propose a simple method to deduce the BSA for single-photon inputs using weak coherent pulses. By applying the method to Reference-Frame-Independent Measurement-Device-Independent Quantum Key Distribution, we experimentally verify the feasibility and effectiveness of the method.
Cross-Lingual Transfer Learning for Hate Speech Detection

We address the task of automatic hate speech detection for low-resource languages. Rather than collecting and annotating new hate speech data, we show how to use cross-lingual transfer learning to leverage already existing data from higher-resource languages. Using bilingual word embeddings based classifiers we achieve good performance on the target language by training only on the source dataset. Using our transferred system we bootstrap on unlabeled target language data, improving the performance of standard cross-lingual transfer approaches. We use English as a high resource language and German as the target language for which only a small amount of annotated corpora are available. Our results indicate that cross-lingual transfer learning together with our approach to leverage additional unlabeled data is an effective way of achieving good performance on low-resource target languages without the need for any target-language annotations.
Anomaly detection of unstructured big data via semantic analysis and dynamic knowledge graph construction

There is an increasing need for both governments and businesses to discover latent anomalous activities in unstructured publicly-available data, produced by professional agencies and the general public. Over the past two decades, consumers have begun to use smart devices to both take in and generate a large volume of open-source text-based data, providing the opportunity for latent anomaly analysis. However, real-time data acquisition, and the processing and interpretation of various types of unstructured data, remains a great challenge. Recent efforts have focused on artificial intelligence / machine learning (AI/ML) solutions to accelerate the labor-intensive linear collection, exploitation, and dissemination analysis cycle and enhance it with a data-driven rapid integration and correlation process of open-source data. This paper describes an Activity Based Intelligence framework for anomaly detection of open-source big data using AI/ML to perform semantic analysis. The proposed Anomaly Detection using Semantic Analysis Knowledge (ADUSAK) framework includes four layers: input layer, knowledge layer, reasoning layer, and graphical user interface (GUI)/output layer. The corresponding main technologies include: Information Extraction, Knowledge Graph (KG) construction, Semantic Reasoning, and Pattern Discovery. Finally, ADUSAK was verified by performing Emerging Events Detection, Fake News Detection, and Suspicious Network Analysis. The generalized ADUSAK framework can be easily extended to a wide range of applications by adjusting the data collection, modeling construction, and event alerting.
The Great Misalignment Problem in Human Evaluation of NLP Methods

We outline the Great Misalignment Problem in natural language processing research, this means simply that the problem definition is not in line with the method proposed and the human evaluation is not in line with the definition nor the method. We study this misalignment problem by surveying 10 randomly sampled papers published in ACL 2020 that report results with human evaluation. Our results show that only one paper was fully in line in terms of problem definition, method and evaluation. Only two papers presented a human evaluation that was in line with what was modeled in the method. These results highlight that the Great Misalignment Problem is a major one and it affects the validity and reproducibility of results obtained by a human evaluation.
Development of English Classroom Teaching Innovation Under the Background of Artificial Intelligence

With the development of the times, our country’s comprehensive strength is gradually improving. In order to better meet the huge demand for innovative talents in our country’s development, we need to start with education and carry out innovative reforms in our country’s education. With the development of big data technology and artificial intelligence technology, we have been able to gradually use these technologies in our country’s education and contribute to the innovation and reform of education. Through these technologies, our country’s traditional teaching model can be improved. Profound changes can impart more knowledge to students on an inherent basis. While educating, the relationship between students and teachers can be more harmonious and friendly, which is conducive to the development of learning. The advent of the era of big data has brought us more information, and teachers can also obtain more teaching resources from it. Making good use of relevant resources can bring richer content to classroom teaching, and teachers can also improve in continuous learning. Own ability and overall quality. However, the use of big data in English teaching in our country is still in the initial stage, and there are still many problems that need to be solved. In order to better study the English teaching model of higher vocational industry under the big data perspective, this article starts from the background of the rapid development of artificial intelligence, analyzes and studies the advantages and disadvantages of our country’s traditional teaching model, integrates relevant information and finally summarizes present the research results of English teaching mode. The experimental results show that the introduction of artificial intelligence technology into our country’s traditional English teaching model has a huge promotion effect on our country’s English teaching reform and innovation. It can improve the quality of school English teaching and bring more rich knowledge to students.
Generating explanations for answer set programming applications

We present an explanation system for applications that leverage Answer Set Programming (ASP). Given a program <i>P</i>, an answer set<i> A</i> of <i>P,</i> and an atom <i>a</i> in the program<i> P</i>, our system generates all explanation graphs of a which help explain why<i> a</i> is true (or false) given the program<i> P</i> and the answer set <i>A</i>. We illustrate the functionality of the system using some examples from the literature.
Initial coin offerings

Recently, a new fundraising method has been consolidated, namely initial coin offerings or ICOs. Despite its detractors, critics and associated risks, its success is uncontestable. Tokens and cryptocurrencies offerings are made using the blockchain technology and based on the use of Bitcoin or Ethers as a payment method. Currently these ICOs are not expressly subject to any regulation in any country; however it seems that this situation will soon change. American, Asian and European regulators have already made known that this phenomenon will be regulated in the future in line with the current regulations applicable to securities markets taking into account the specificities of these offerings.
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data

Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missingness in real-world applications due to heterogeneity and noise of input sources. In this work, we propose the variational selective autoencoder (VSAE), a general framework to learn representations from partially-observed heterogeneous data. VSAE learns the latent dependencies in heterogeneous data by modeling the joint distribution of observed data, unobserved data, and the imputation mask which represents how the data are missing. It results in a unified model for various downstream tasks including data generation and imputation. Evaluation on both low-dimensional and high-dimensional heterogeneous datasets for these two tasks shows improvement over state-of-the-art models.
An ICT Strategy Based upon E-Teaching and E-Learning in Response to the COVID-19 Crisis in Africa

The COVID-19 pandemic is a crisis that has gripped the world, causing governments and development agencies to search for critical measures to protect their people. The situation not only represents a significant health risk but has resulted in school closures that have disrupted agricultural education. This impedes the attainment of Africa’s larger food security and rural transformation agendas. Six months before the advent of the pandemic, the International Institute of Tropical Agriculture initiated a pilot project, Start Them Early Program (STEP) whose goal is to reinforce pathways to careers in agriculture within secondary schools in DR Congo, Kenya, and Nigeria. The project has now been forced to rethink its approach while embracing information and communication technologies due to the school closures. This paper describes the process involved in that operational pivot, particularly concerning the shift from electronic teaching by instructors towards distance electronic learning by students. Key issues addressed are the consolidation of digital applications, development of a mobile-based toolbox for use by young farmers, and constraints to device ownership. The means of addressing these concerns through working with instructors and their larger school systems are explained. Action points and resources that are recommended include the distribution of upgraded instructor workstations, a listing of relevant software applications, and the design of a mobile-based all-in-one toolkit for agriculture students and young farmers. The latter two developments have wider application in the reform of agricultural extension amongst the tech-savvy youth taking up agribusiness.
Micro-Expression Recognition using 3D - CNN

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.
Metode PCQ dan Queue Tree untuk Implementasi Manajemen Bandwidth Berbasis Mikrotik

Bandwidth problems are the most frequently encountered problems in sharing information traffic or internet access on computer networks. The factor that causes slow internet access is the large number of devices connected to the internet that are not matched by the availability of sufficient bandwidth and the lack of available bandwidth. This can be accommodated with bandwidth management methods. Bandwidth management in Mikotik uses several bandwidth sharing methods such as PCQ (Per Connection Queue), Queue Tree, and HTB (Hierchichal Token Bucket). This research uses 2 (two) bandwidth management methods, namely PCQ and Queue Tree because these methods can divide bandwidth automatically according to the number of active users and are more effective in sharing bandwidth based on Mikrotik. PCQ is a method aimed at optimizing QoS for large-scale internet networks where all queues are the same for all sub-streams, while Queue Tree is a method designed to carry out more complex queuing tasks for network traffic. The purpose of this research is to optimize the limited internet bandwidth so that it can be accessed by all users in the Local Area Network and automate the queue of devices connected to the network according to user needs so as to produce a more stable computer network performance using the network development method, namely NDLC. The results of the tests were carried out 10 times using a bandwidth of 10 Mbps which resulted in an average jitter of 1.64 ms, ping 36.8 ms, 2 Mbps throughput, and 0.1% packet loss so that the QoS of internet access was categorized as satisfactory. It is hoped that from this research the company will be able to save on internet access expenses by maximizing a small bandwidth without having to increase the existing bandwidth.&#x0D;
A Comorbidity Knowledge-Aware Model for Disease Prognostic Prediction

Prognostic prediction is the task of estimating a patient’s risk of disease development based on various predictors. Such prediction is important for healthcare practitioners and patients because it reduces preventable harm and costs. As such, a prognostic prediction model is preferred if: 1) it exhibits encouraging performance and 2) it can generate intelligible rules, which enable experts to understand the logic of the model’s decision process. However, current studies usually concentrated on only one of the two features. Toward filling this gap, in the present study, we develop a novel knowledge-aware Bayesian model taking into consideration accuracy and transparency simultaneously. Real-world case studies based on four years’ territory-wide electronic health records are conducted to test the model. The results show that the proposed model surpasses state-of-the-art prognostic prediction models in accuracy and c-statistic. In addition, the proposed model can generate explainable rules.
A systematic review on security of E-commerce systems

ABSTRACTWith the emergence of digitalization, making the use of Internet almost for everything is not a new trend. The maximum use of it is in the E-commerce systems. Most of the customers are opting for Internet based banking, shopping, sales, purchase and many others. But with the numerous advantages and benefits that are delivered by the E-commerce systems, there comes the challenges as well. One of the biggest challenges in it is security. Implementing the adequate security measures, while making use of E-commerce is one of the crucial tasks. The main aim of this paper is to have an analyzation of the security in the E-commerce systems. To achieve this, last 10 years literature survey has been done and year-wise publication of various attacks on E-commerce sites is illustrated. Along with this various security measures and challenges are also depicted. This paper will be beneficial to the researchers who are working in the domain of security of E-commerce systems.
Image Augmentation Using Hybrid RANSAC Algorithm

The process of augmenting the number of images in a dataset is called Image Augmentation. Data volume is essential to process and generate digital outputs from a variety of features. This work focuses on the image augmentation using a hybrid RANSAC algorithm. The features extracted is used to join or merge the images by the blending of images. The proposed RANSAC algorithm is used to extract features from four images and produce the desired mosaiced image. A mosaiced picture is best suited for aerial photos and real-world objects. The blur metric of the proposed method is 185.2587 and which is 2.86% higher than the feathering blending algorithms. The total number of images in the dataset is 2100. The number of images after augmentation is 6300 with average accuracy of 95.6%. The reported remarkable results are beneficial to all the stakeholders on image augmentation.