Reviewerly/exHarmony huggingface dataset:
TStego-THU: Large-Scale Text Steganalysis Dataset

In recent years, with the development of natural language processing (NLP) technology, linguistic steganography has developed rapidly. However, to the best of our knowledge, currently there is no public dataset for text steganalysis, which makes it difficult for linguistic steganalysis methods to get a fair comparison. Therefore, in this paper, we construct and release a large-scale linguistic steganalysis dataset called TStego-THU, which we hope to provide a fair enough platform for comparison of linguistic steganalysis algorithms and further promote the development of linguistic steganalysis. TStego-THU includes two kinds of text steganography modes, namely, text modification-based and text generation-based modes, each of which provides two latest or classical text steganography algorithms. All texts in TStego-THU come from three common transmitted text medias in cyberspace: News, Twitter and commentary text. Finally, TStego-THU contains 240,000 sentences (120,000 cover-stego text pairs), each steganographic sentence is generated by randomly choosing one of these four steganographic algorithms and embedding random bitstream into randomly extracted normal texts. At the same time, we also evaluate some latest text steganalysis algorithms as benchmarks on TStego-THU, the detail results can be found in the experiment part. We hope that TStego-THU will further promote the development of universal text steganalysis technology. The description of TStego-THU and instructions will be released here: https://github.com/YangzlTHU/Linguistic-Steganography-and-Steganalysis .
Analyzing crosstalk error in the NISQ era

Noisy Intermediate-Scale Quantum (NISQ) hardware has unavoidable noises, and crosstalk error is a significant error source. When multiple quantum operations are executed simultaneously, the quantum state can be corrupted due to the crosstalk between gates during simultaneous operations, decreasing the circuit fidelity. In this work, we first report on several protocols for characterizing crosstalk. Then, we discuss different crosstalk mitigation methods from the hardware and software perspectives. Finally, we perform crosstalk injection experiments on the IBM quantum device and demonstrate the fidelity improvement with the crosstalk mitigation method.
Malicious URL Detection Using Machine Learning and Ensemble Modeling

Websites are software applications that allow us to connect and interact with the data located in the web servers. Websites allow the user to capture, store, process, and exchange sensitive data like banking details and personal details. Web pages are accessed by merely entering the required URL in the browser. To prevent sensitive information from users, the attackers/hackers make duplicate websites and send them to victims through phishing emails. In this article, the machine learning framework is used to find malicious URLs. Here, five different machine learning algorithms such as the logistic regression algorithm, K-nearest neighbor algorithm, decision tree algorithm, random forest algorithm, and support vector machine algorithm have been used. An ensemble modeling has been done using these algorithms, and the performance of each algorithm has been compared.
Hybrid intelligent telemedical monitoring and predictive systems

Healthcare systems need to overcome the high mortality rate associated with cardiovascular disease and improve patients’ health by using decision support models that are both quantitative and qualitative. However, existing models emphasize mathematical procedures, which are only good for analyzing quantitative decision variables and have failed to consider several relevant qualitative decision variables which cannot be simply quantified. In solving this problem, some models such as interval type-2 fuzzy logic (IT2FL) and flower pollination algorithm (FPA) have been used in isolation. IT2FL is a simplified version of T2FL, with a reduced computation complexity and additional design degrees of freedom, but it cannot naturally achieve the rules it uses in making decisions. FPA is a bio-inspired method based on the process of pollination, executed by the flowering plants, with the ability to learn, generalize and process numerous measurable data, but it is not able to describe how it reaches its decisions. The hybrid intelligent IT2FL-FPA system can conquer the constraints of individual approaches and strengthens their robustness to cope with healthcare data. This work develops a hybrid intelligent telemedical monitoring and predictive system using IT2FL and FPA. The main objective of this paper is to find the best membership functions (MFs) parameters of the IT2FL for an optimal solution. The FPA technique is employed to find the optimal parameters of the MFs used for IT2FLSs. The authors tested two data sets for the monitoring and prediction problems, namely: cardiovascular disease patients’ clinical and real-time datasets for shock-level monitoring and prediction.
Comparison of Performances of Jaya Algorithm and Cuckoo Search Algorithm Using Benchmark Functions

Nowadays, selecting the best possible solution among several solutions becomes an important skill for engineering and research. Therefore, engineers are turning to optimization methods as a complementary alternative strategy of exhaustive searching. Metaheuristic algorithms have been used successfully for solving different optimization problems. To help engineers select the best metaheuristic algorithms for their problems, there is a need to evaluate the performance of different metaheuristic algorithms against each other using common case studies. This paper aims to compare the performance of two metaheuristic algorithms which are Jaya Algorithm (JA) and Cuckoo Search (CS) using some common benchmark functions. CS and JA have implemented in the same platform (Intellij IDEA Community Edition 2020.2.3) using the same language (Java). The experimental results show that JA has better and consistent performance as compared to CS in most cases in terms of execution time and test suite size; however, the performance of JA is still within acceptable ranges.
Research on online and offline mixed teaching mode—Taking the teaching of single chip microcomputer course of Internet of things engineering as an example

Internet of things engineering specialty has the characteristics of interdisciplinary, wide range of knowledge and focusing on engineering practice. As a professional basic course of Internet of things engineering, Microcontroller Unit (MCU) course should focus on cultivating students' ability of solving practical engineering problems and application innovation. According to the students' cognitive law, the project tasks based on the working process are jointly developed through school enterprise cooperation. The mainstream 32-bit MCU contents are integrated into 16 teaching tasks, 24 teaching contents and 43 knowledge points by calling library functions. From easy to difficult, from simple to complex, the students are taught in accordance with their aptitude through the online and offline mixed teaching mode, and the ability and thinking of solving practical projects are cultivated In order to meet the needs of students' personalized development, we should promote teaching and learning by competition, stimulate students' interest in learning MCU and build up their confidence in learning the perception layer of Internet of things, and cultivate innovative and applied talents with strong practical ability.
Static Analysis of Source Code Vulnerability Using Machine Learning Techniques: A Survey

With the rapid increase of practical problem complexity and code scale, the threat of software security is increasingly serious. Consequently, it is crucial to pay attention to the analysis of software source code vulnerability in the development stage and take efficient measures to detect the vulnerability as soon as possible. Machine learning techniques have made remarkable achievements in various fields. However, the application of machine learning in the domain of vulnerability static analysis is still in its infancy and the characteristics and performance of diverse methods are quite different. In this survey, we focus on a source code-oriented static vulnerability analysis method using machine learning techniques. We review the studies on source code vulnerability analysis based on machine learning in the past decade. We systematically summarize the development trends and different technical characteristics in this field from the perspectives of the intermediate representation of source code and vulnerability prediction model and put forward several feasible research directions in the future according to the limitations of the current approaches.
Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks

Understanding the 3D world from 2D projected natural images is a fundamental challenge in computer vision and graphics. Recently, an unsupervised learning approach has garnered considerable attention owing to its advantages in data collection. However, to mitigate training limitations, typical methods need to impose assumptions for viewpoint distribution (e.g., a dataset containing various viewpoint images) or object shape (e.g., symmetric objects). These assumptions often restrict applications; for instance, the application to non-rigid objects or images captured from similar viewpoints (e.g., flower or bird images) remains a challenge. To complement these approaches, we propose aperture rendering generative adversarial networks (AR-GANs), which equip aperture rendering on top of GANs, and adopt focus cues to learn the depth and depth-of-field (DoF) effect of unlabeled natural images. To address the ambiguities triggered by unsupervised setting (i.e., ambiguities between smooth texture and out-of-focus blurs, and between foreground and background blurs), we develop DoF mixture learning, which enables the generator to learn real image distribution while generating diverse DoF images. In addition, we devise a center focus prior to guiding the learning direction. In the experiments, we demonstrate the effectiveness of AR-GANs in various datasets, such as flower, bird, and face images, demonstrate their portability by incorporating them into other 3D representation learning GANs, and validate their applicability in shallow DoF rendering.
Top-K Deep Video Analytics

The impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data. Although efficient and accurate, the latest video analytic systems have not supported analytics beyond selection and aggregation queries. In data analytics, Top-K is a very important analytical operation that enables analysts to focus on the most important entities. In this paper, we present Everest, the first system that supports efficient and accurate Top-K video analytics. Everest ranks and identifies the most interesting frames/moments from videos with probabilistic guarantees. Everest is a system built with a careful synthesis of deep computer vision models, uncertain data management, and Top-K query processing. Evaluations on real-world videos and the latest Visual Road benchmark show that Everest achieves between 14.3x to 20.6x higher efficiency than baseline approaches with high result accuracy.
Generative adversarial network for video analytics

A generative adversarial network (GAN) is a framework composed of a generator and a discriminator. GAN learns the deep attributes without the need for hugely annotated training data. This learning is attained by back-propagation approach through competition between generator and discriminator network. Since 2014, GAN has been used in a wide variety of applications such as game development, security, image editing, data generation, attention prediction, and so on. In this article, we explore the fundamental working principle, architecture of GAN, and the effectiveness of GAN for video analytics. Video analytics has become a crucial research area for the academic world and the industry thanks to the availability of IP CCTV as well as the progressive growth in the video content analysis algorithms. Automation in video analytics has come into practice due to the extensive growth of deep learning algorithm. The prime intention of this chapter is to provide a comprehensive review of the GAN variants available for video analytics, explore different architectures, and further the techniques used in those respective applications.
Research on UAV path planning obstacle avoidance algorithm based on improved artificial potential field method

Abstract In this paper, we study the path planning obstacle avoidance problem of UAV based on improved artificial potential field method (APF). By introducing dynamic adjustment coefficients, the gravitational force and repulsive force functions in the traditional APF are improved to make the obstacle avoidance safety factor higher and the final path smoother; for the target unreachability problem, a new attractive potential field is built in the gravitational force function to balance the changes of the traditional attractive force and repulsive force; a longitudinal random factor is used to solve the problem of getting into local minima. Through simulation comparison with other methods, this method can solve the path planning obstacle avoidance problem of UAV more efficiently.
Capturing Semantics for Imputation with Pre-trained Language Models

Existing imputation methods generally generate several possible fillings as candidates and determine the value from the candidates for imputing. However, semantics are ignored in these methods. Recently, pre-trained language models achieve good performances in various language understanding tasks. Motivated by this, we propose IPM that captures semantics for Imputation with Pre-trained language Models. A straightforward idea is to model the imputation task as a multiclass classfication task, named IPM-Multi. IPM-Multi predicts the missing values by fine-tuning the pre-trained model. Due to the low redundancy of databases and large domain sizes, IPM-Multi may suffer the over-fitting problem. In this case, we develop another approach named IPM-Binary. IPM-Binary first generates a set of uncertain candidates and fine-tunes a pre-trained language model to select candidates. Specifically, IPM-Binary models the candidate selection task as a binary classification problem. Unlike IPM-Multi, IPM-Binary computes the probability for each candidate filling respectively, by accepting both complete attributes and a candidate filling as input. The attention mechanism enhances the ability of IPM-Binary to capture semantic information. Moreover, negative sampling from neighbors rather than domains is employed to accelerate the training process and makes the training more targeted and effective. As a result, IPM-Binary requires fewer data to converge. We compare our proposal IPM to the state-of-the-art baselines on multiple datasets. And the extensive experimental results show that IPM outperforms existing solutions. The evaluation of IPM validates our intuitions and demonstrates the effectiveness of the proposed optimizations.
Analysis of Educational Transformation at Astana IT University (AITU)

Higher education is going through digital transformation all over the world. This change accelerated by COVID-19 and increased demand for best practices of educational systems. Competitive landscape requires conversion of the future competences and methodologies of teaching. Recently established Astana IT University (AITU) adopted the national development strategy "Digital Kazakhstan" to serve better the needs of future graduates. AITU strives to become one of the leaders in higher education, which leads to new economy of Central Asia region. University's online courses with the use of modern teaching methodologies are helping to support the digital economy of our country. Digital transformation affects labor market by making some professions obsolete and creating new ones. University offers contemporary courses in IT industry that are competitive in new economy. This article analyzes implementation of such approach as a case study of AITU.
Super-resolution reconstruction of unmanned aerial vehicle image based on deep learning

Abstract Due to the influence of weather, air flow and other environmental factors, unmanned aerial vehicle (UAV) aerial photography often leads to problems such as blur, defocus and so on. In order to solve the problem of low definition of UAV aerial images, this paper proposes a super-resolution reconstruction algorithm based on improved residual learning blocks (IRB) to reconstruct aerial images to achieve high definition. This method can effectively solve the problem of gradient explosion which is often encountered in deep learning network. At the same time, it can suppress the learning of useless information and make full use of important feature information. Through the establishment of UAV aerial data set for cross training of the network, the network can better adapt to the UAV aerial environment. The experimental results show that, compared with the existing neural network algorithm, the improved residual network algorithm can reconstruct UAV aerial image well, and the subjective visual effect of the reconstructed image is better while the edge information is greatly preserved.
Measurement Noise Model for Depth Camera-Based People Tracking

Depth cameras are widely used in people tracking applications. They typically suffer from significant range measurement noise, which causes uncertainty in the detections made of the people. The data fusion, state estimation and data association tasks require that the measurement uncertainty is modelled, especially in multi-sensor systems. Measurement noise models for different kinds of depth sensors have been proposed, however, the existing approaches require manual calibration procedures which can be impractical to conduct in real-life scenarios. In this paper, we present a new measurement noise model for depth camera-based people tracking. In our tracking solution, we utilise the so-called plan-view approach, where the 3D measurements are transformed to the floor plane, and the tracking problem is solved in 2D. We directly model the measurement noise in the plan-view domain, and the errors that originate from the imaging process and the geometric transformations of the 3D data are combined. We also present a method for directly defining the noise models from the observations. Together with our depth sensor network self-calibration routine, the approach allows fast and practical deployment of depth-based people tracking systems.
Using the Naive Bayes as a discriminative model

For classification tasks, probabilistic graphical models are usually categorized into two disjoint classes: generative or discriminative. It depends on the posterior probability p(x|y) of the label x given the observation y computation. On the one hand, generative models, like the Naive Bayes or the Hidden Markov Model (HMM), need the computation of the joint probability p(x, y), before using the Bayes rule to compute p(x|y). On the other hand, discriminative models compute p(x|y) directly, regardless of the observations’ law. They are intensively used nowadays, with models as Logistic Regression or Conditional Random Fields (CRF). However, the recent Entropic Forward-Backward algorithm shows that the HMM, considered as a generative model, can also match the discriminative one’s definition. This example leads to question if it is the case for other generative models. In this paper, we show that the Naive Bayes can also match the discriminative model definition, so it can be used in either a generative or a discriminative way. Moreover, this observation also discusses the notion of Generative-Discriminative pairs, linking, for example, Naive Bayes and Logistic Regression, or HMM and CRF. Related to this point, we show that the Logistic Regression can be viewed as a particular case of the Naive Bayes used in a discriminative way.
Multiple Attention Mechanism Neural Network in Garment Image Segmentation

Instance segmentation of clothing images is a task that fashion analysts are paying more and more attention to in recent years. The segmentation of different clothing components allows designers to better design new fashion items and allows consumers to better understand design concepts. Most of the current methods are based on deep convolutional neural networks (DCNN). However, most of the current instance segmentation neural networks are limited by the size of the receptive field and cannot capture the global dependence. For clothing images, the use of contextual information between different clothing and collocations can obtain fine-grained and higher clothing segmentation images. Previous studies have shown that attention-based methods can obtain non-local dependencies of the whole image and are mostly used for panoramic segmentation of aerial images. For instance segmentation of clothing images, we propose a new dual-branch attention module based on the Non-local attention mechanism, called Multiple Attention MaskRCNN (HAMaskRCNN). Specifically, for the attention module, we use two branches: position attention and channel attention. After feature fusion, the FPN module and the attention module are connected in parallel to form a multiple attention module. We use the Imaterialist-fashion (2019) dataset to conduct experiments and compare with the benchmark to prove the effectiveness of our HAMaskRCNN.
A Novel Self-adaptive Salp Swarm Algorithm for Dynamic Optimization Problems

Many real-world applications can be cast as dynamic optimization problems where it is required to locate and track the trajectory of the changing global optima while finding the global best solution in a dynamic and uncertain environment. In this article, we present a novel nature-inspired meta-heuristic optimizer to solve dynamic optimization problems, namely self-adaptive salp swarm algorithm. The self-adaptive parameter control technique is used with a multi-population and ageing mechanism, in which individuals have to maintain diversity during the optimization process in SA-SSA. The evaluation is conducted to examine the overall performance of SA-SSA on widely known generalized dynamic benchmark problems provided in the CEC’09 competition. Preliminary results showed that the proposed SA-SSA is promising.
JUWELS Booster - Early User Experiences

Over the last few years, GPUs became ubiquitous in HPC installations around the world. Today, they provide the main source of performance in a number of Top500 machines - for example Summit, Sierra, and JUWELS Booster. Also for the upcoming Exascale era, GPUs are selected as key enablers and will be installed numerously. While individual GPU devices already offer plenty of performance (O (10) TFLOP/sFP64), current and next-generation super-computers employ them in the thousands. Using these machines to the fullest extend means not only utilizing individual devices efficiently, but using the entire interconnected system of devices thoroughly.