Reviewerly/exHarmony huggingface dataset:
The Enlightenment of Toastmasters Training Skills for the College English Course Mode in China

With the construction of the "Golden Course" of College English in our country and the teaching reform attach importance to the cultivation of students' ability to apply English in daily life; College English teachers should not stop at the reform and promotion of the original curriculum model. Toastmasters as a training model in the development process in the past hundred years has become an extremely effective international talent training mode can be applied to our College English "Golden Course" construction. The author tries to apply the TM training mode to the English course of our university, and build the innovative "Golden Course" mode to improve the teaching effect.
Overview of Business English Teaching Mode Innovation Supported by Information Technology

Abstract In the 21st century, with the development of information technology, the information age has come. Information technology has become an indispensable technology in people’s life, which brings convenience to people. In the context of global economic integration, information exchange has become the top priority. It also has an impact on the current business English education in China, and the traditional education system must change its thinking and make innovation and reform. This paper analyzes the current situation and existing problems of business English teaching, and explores the innovative strategies and paths of business English teaching mode.
AI-Powered Teaching Behavior Analysis by Using 3D-MobileNet and Statistical Optimization

Artificial intelligent technology can realize multi-angle analysis and feedback of teaching process. This paper provides an innovative auxiliary for classroom teaching evaluation and fills in the lack of teacher behavior analysis in AI application. Firstly, a 3D-MobileNet framework is proposed for behavior recognition, which can process time-domain information for the video through layered training. Next, we design a comprehensive model by using both the analytic hierarchy process and entropy weight method (AHP-EW) to output the quantitative results of the teaching evaluation in three dimensions. This model combines the subjective and objective weights through a statistical optimization strategy to improve the credibility. Finally, we test our model on a 45-min teaching video, and compare it with the existing model in various aspects, proving that our method is highly feasible and competitive.
VLSI Design for High‐Precision Three‐Dimensional Depth Perception Chip

This paper presents a Very large scale integration (VLSI) design method for Three-dimensional (3D) depth perception chip based on infrared coding structure light. The primary sub-modules on the chip contain the speckle pattern preprocessing module, block-matching disparity estimation, depth mapping and post-processing. The chip employs pipelining technology, and after Application specific integrated circuit (ASIC) verification, it proves that our chip has more advantages in performance of depth precision (12bits, 1mm @ 1m), image resolution (1280×960), time delay (less than 17ms), range limit (0.4~6m). It also can generate more stable and smooth depth map in real-time, which can be used in 3D recognition, motion capture or scene perception.
A Systematic Hardening of Java's Information Hiding

The Java runtime is installed on billions of devices worldwide, and over years it has been a primary attack vector for online criminals. In this work, we address that many attack vectors exploit weaknesses in Java's information hiding, making use of illegal access to private members of system classes. To study to what extent such attacks can be mitigated, and at what cost, this paper demonstrates a proof-of-concept solution to strengthen information hiding. Experiments show that this approach is backward compatible, and that it blocks 84% of all information-hiding attacks in a large-scale sample set at an average performance overhead below 2%. Based on our experiments, we suggest a solution to strengthen information hiding for productive use that has the potential to outperform our proof of concept in terms of robustness and performance, and also would block the remaining information-hiding attacks. Finally, we conclude with general advice on the design of secure software.
SIP Bruteforcing in the Wild - An Assessment of Adversaries, Techniques and Tools

Over the last two decades, Voice-over-IP (VoIP) and specifically SIP have become standard solutions to realize voice telephony in residential, commercial, and telecom environments. As by now, an abundance of SIP endpoints exist, it has become financially lucrative for cybercriminals to systematically search for VoIP installations, with for example the aim to abuse them for billing fraud or to hide their criminal activities behind a legitimate connection and phone number. By now, this has made SIP one of the most scanned UDP protocols on the Internet. In this paper, we take a look at the actors behind these attacks. Using a large network telescope, we collect over 822 million SIP brute-forcing attempts from 5,691 sources over 187 countries and analyze who is searching for and attacking VoIP endpoints. As each tool and campaign exhibits specific implementation differences, we can relate individual attempts into campaigns and can thereby provide a detailed view into different actors in the ecosystem, different techniques and tooling, and how these are developing over 5 years. We show that we can fingerprint different SIP scanning tools, show that actors hardly ever change their toolkit, and identify an increase in highly distributed and coordinated scanning.
Research on Face Recognition Access Control System in Universities Based on Convolutional Neural Network

Abstract With the development of technology, Internet technology can be seen everywhere in people’s lives. Of course, advanced technology is not only used for things that are conducive to social development. Some people also use technology to do some illegal things, such as robbery or theft. Therefore, in order to make the anti-theft system more secure at home, people have developed a face recognition technology, which depends on the Internet technology. This technology uses facial features to determine whether a person is a resident or a stranger. This technology protects people’s safety and property more effectively. This paper makes research and analysis on face recognition, which is based on convolutional neural network.
DecFL: An Ubiquitous Decentralized Model Training Protocol and Framework Empowered by Blockchain

Machine learning has become ubiquitous across many fields in the last decade and modern real world applications often require a decentralized solution for training such models. This demand sprouted the research in federated learning, which solves some of the challenges with centralized machine learning, but at the same times raises further questions in regard to security, privacy and scalability. We have designed and implemented DecFL, an ubiquitous protocol for decentralized model training. The protocol is machine-learning-model-, vendor-, and technology-agnostic and provides a basis for practitioner's own implementations. The implemented DecFL framework presented in this article is an exemplary realization of the carefully designed protocol stack based on Ethereum and IPFS and offers a scalable baseline solution for decentralized machine learning. In this article, we present a study based on the proposed protocol, its theoretical bounds and experiments based on the implemented framework. Using open-source datasets (MNIST and CIFAR10), we demonstrate key features, the actual cost of training a model (in euro) and the communication overhead. We further show that through a proper choice of technologies DecFL achieves a linear scaling, which is a non-trivial task in a decentralized setting. Along with discussing some of the security challenges in the field, we highlight aggregation poisoning as a relevant attack vector, its associated risks and a possible prevention strategy for decentralized model training through DecFL.
First-order and High-order Information Fusion over Heterogeneous Information Network for Top-N Recommendation System

In recent years, more and more researchers pay attention to the recommendation system based on heterogeneous information network(HIN), because HIN is rich in various kinds of information, which can significantly improve the performance of the recommendation system. But the HIN based recommendation system faces the following problems: how to leverage high-order information to get semantic-level interaction characteristics between users and items; How to deeply fuse first-order and high-order information to enhance the representation ability of the system. To address these issues, we propose a novel model: First-order and High-order Information Fusion over Heterogeneous Information Network for Top-N Recommender System(FHRec). For first-order information, we use graph neural networks to generate the latent vectors of users and items. And for high-order information, we use a meta-path based semantic-level aggregation to get the interaction between users and items. Then we deeply integrate the first-order and high-order information and use neural collaborative filtering to improve the recommendation performance. Finally, we conduct comparative experiments of our model with other baseline algorithms on three real world datasets, and the experimental results prove the superiority of our model.
S2CE

The explosive increase in volume, velocity, variety, and veracity of data generated by distributed and heterogeneous nodes such as IoT and other devices, continuously challenge the state of art in big data processing platforms and mining techniques. Consequently, it reveals an urgent need to address the ever-growing gap between this expected exascale data generation and the extraction of insights from these data. To address this need, this position paper proposes Stream to Cloud & Edge (S2CE), a first of its kind, optimized, multi-cloud and edge orchestrator, easily configurable, scalable, and extensible. S2CE will enable machine and deep learning over voluminous and heterogeneous data streams running on hybrid cloud and edge settings, while offering the necessary functionalities for practical and scalable processing: data fusion and preprocessing, sampling and synthetic stream generation, cloud and edge smart resource management, and distributed processing.
PredictRoute: A Network Path Prediction Toolkit

Accurate prediction of network paths between arbitrary hosts on the Internet is of vital importance for network operators, cloud providers, and academic researchers. We present PredictRoute, a system that predicts network paths between hosts on the Internet using historical knowledge of the data and control plane. In addition to feeding on freely available traceroutes and BGP routing tables, PredictRoute optimally explores network paths towards chosen BGP prefixes. PredictRoute's strategy for exploring network paths discovers 4X more autonomous system (AS) hops than other well-known strategies used in practice today. Using a corpus of traceroutes, PredictRoute trains probabilistic models of routing towards prefixes on the Internet to predict network paths and their likelihood. PredictRoute's AS-path predictions differ from the measured path by at most 1 hop, 75% of the time. We expose PredictRoute's path prediction capability via a REST API to facilitate its inclusion in other applications and studies. We additionally demonstrate the utility of PredictRoute in improving real-world applications for circumventing Internet censorship and preserving anonymity online.
A decentralized adaptive momentum method for solving a class of min-max optimization problems

Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs). However, most of the recent efforts for solving them are limited to special regimes such as convex-concave games. Further, it is customarily assumed that the underlying optimization problem is solved either by a single machine or in the case of multiple machines connected in centralized fashion, wherein each one communicates with a central node. The latter approach becomes challenging, when the underlying communications network has low bandwidth. In addition, privacy considerations may dictate that certain nodes can communicate with a subset of other nodes. Hence, it is of interest to develop methods that solve min-max games in a decentralized manner. To that end, we develop a decentralized adaptive momentum (ADAM)-type algorithm for solving min-max optimization problem under the condition that the objective function satisfies a Minty Variational Inequality condition, which is a generalization to convex-concave case. The proposed method overcomes shortcomings of recent non-adaptive gradient-based decentralized algorithms for min-max optimization problems that do not perform well in practice and require careful tuning. In this paper, we obtain non-asymptotic rates of convergence of the proposed algorithm (coined DADAM$^3$) for finding a (stochastic) first-order Nash equilibrium point and subsequently evaluate its performance on training GANs. The extensive empirical evaluation shows that DADAM$^3$ outperforms recently developed methods, including decentralized optimistic stochastic gradient for solving such min-max problems.
Polynomial matrices, splitting subspaces and Krylov subspaces over finite fields

Let T be a linear operator on a vector space V of dimension n over Fq. For any divisor m of n, an m-dimensional subspace W of V is T-splitting ifV=W⊕TW⊕⋯⊕Td−1W, where d=n/m. Let σ(m,d;T) denote the number of m-dimensional T-splitting subspaces. Determining σ(m,d;T) for an arbitrary operator T is an open problem that is closely related to another important problem on Krylov spaces. We discuss this connection and give explicit formulae for σ(m,d;T) in the case where the invariant factors of T satisfy certain degree conditions. A connection with an enumeration problem on polynomial matrices is also discussed.
Deep Reinforcement Learning Visual Navigation Model Integrating Memory-prediction Mechanism

Deep reinforcement learning (DRL) has been widely used in the field of visual navigation. However, due to the lack of adaptability of DRL to the new tasks, the generalization ability of current visual navigation model using DRL is not desired. In order to improve this deficiency, we introduce the memory-prediction mechanism. By enhancing the memory of the scene, and combining the past experience of navigation to predict the next state, a more reasonable action can be obtained. First, we pass the image features extracted during the navigation process to an LSTM, and use LSTM to memorize the scene information in the image features. Then, we combine all the information (including state, target, and action) of each time step in the navigation process, and pass the historical information of multiple time steps to another LSTM to predict the next state. The action performed by the robot is determined by the predicted state. We use the AI2-THOR framework to carry out experiments. The results show that the proposed method can improve the navigation performance of the DRL visual navigation model and improve its adaptability to new tasks.
Detecting Sexually Predatory Behavior on Open-Access Online Forums

Technological advancements have turned the world into a global village. Online communication channels have surpassed geographical boundaries and lessened the gap between us. At the same time, the malicious activity persisting in platforms such as online chat rooms cannot be overlooked. It creates an unsafe atmosphere particularly for children on the internet. The objective of this research is to enhance children’s safety by protecting them from social ills that make them vulnerable to sexual predators online. In this study, online conversations are examined using computational linguistics and statistical learning to identify sexually predatory behavior. The research is carried out with a special focus on contextual details, which identifies the sections of conversations that are unique to misbehaving users. The proposed algorithm detects potential misuse of chat rooms with a two-stage classification system and classifies them into three groups of potential levels of maliciousness. F1-score is used to measure the accuracy of this algorithm.
Advancing User Quality of Experience Using Viewport Archives in Viewport-Aware Tile-Based 360-Degree Video Streaming

In tile-based 360-degree video streaming, the video content is temporally divided into segments, wherein the content is further divided spatially into multiple partitions called tiles. As a common technique to reduce the data size for the segment in tile-based streaming, various viewport prediction models are adopted to allocate bandwidth to the tiles within the region predicted for the user, which is also called the quality enhanced region or viewport. However, due to the unpredictable nature of user head movements, it is difficult to accurately estimate the viewport, resulting in great degradation in the Quality of Experience (QoE) for the user. In this paper, to improve user QoE for even when there are estimation errors, we propose extending the viewport to include the direction(s) holding the highest likelihood to be seen by the user by assigning weights to each tile of each segment from the viewport archives of past users. From our performance evaluation on two 360-degree videos, the proposed method demonstrated a significant increase in the perceived viewport quality while suppressing quality variation within the viewport and across segments compared to two reference models.
Interactive Technology between OCS and OMS Based on Artificial Intelligence Technology

Abstract In order to realize the interaction between OCS and OMS. The author gives the artificial intelligence technology to analyze the interaction technology between CS and OMS. High-speed data bus and general service bus are deployed in OCS and OMS systems respectively. Through real-time data interaction, service invocation such as static security analysis, sensitivity analysis and the construction of integrated operation and maintenance management subsystem, the cross-region interaction method is validated. The test results show that through the construction of this scheme, it not only meets the secondary security requirements of power, but also improves the real-time and reliability of information exchange between OMS and OMS, and ensures the effective circulation of various dispatching services.
Low-Power U-Net for Semantic Image Segmentation

Digital image segmentation especially semantic segmentation of RGB images is a computationally intensive task in digital image processing as it needs low-level dimensional information. Image segmentation finds its applications in science and as well as in industries. Deep learning-based segmentation is by now robustly established as a strong mechanism in image segmentation. Nowadays, graphics processing units (GPUs) are widely used for the acceleration of deep learning applications. GPUs are often costly and power hungry. Field programmable gate arrays (FPGAs) provide a low-cost, low-power and high power-efficient alternative for inference of deep learning architectures to GPUs. U-Net is a deep learning architecture for semantic image segmentation among many other architectures used for image segmentation. This work demonstrates the usage of an end-to-end system to develop and deploy a low-power deep learning architecture on FPGA. For the aforementioned purpose, U-Net, a popular convolutional neural network for semantic segmentation of images, is chosen. The network when tested on images of size \(1024\times 512\) shows that FPGA consumes roughly one-third of the power that is consumed by GPU with minimal reduction in accuracy. The network uses Xilinx\(^{\textregistered }\) deep learning processor unit (DPU) for implementing convolutional neural networks on the programmable logic of the FPGA.
Construction of Smart Library System Based on Book Information Retrieval

The information retrieval process involves the multiple links such as determining goals, selecting the retrieval systems, and also formulating strategies. Based on this, the construction of the smart library system based on the book information retrieval is studied in this paper. With the improvement of people's search requirements, search engines are constantly adapting to users' requirements. In addition to some simple specialized search and classified navigation, there are also more professional search engines. To improve the search accuracy and construct the efficient system, this paper proposes the intelligent information retrieval model and designed the smart library system. The sensing models and the data analytic pipelines are combined to study the performance of the designed system. The proposed system is implemented on hardware platforms to show the performance.
Comparison of action recognition from video and IMUs

The aim of the study is to create an intelligent control system for automatic assistance to the operator of a semi-passive exoskeleton. This work solves the problem of recognizing human movements, since it is necessary to build a control system based on human movements in an exoskeleton. An experimental comparative study of two approaches to create human models for action recognition is considered. The first approach is based on the reconstruction of a simplified human kinematic model from IMU data, represented in special format. The second approach is to obtain 2D coordinates of a person’s key points from video from two different views. The sets of human models obtained from these approaches are classified using the XGBoost algorithm. The resulting classifiers are compared in terms of accuracy.
Massive AI based cloud environment for smart online education with data mining

Under the background of the deep integration of Internet technology and artificial intelligence technology with the field of education, the traditional teacher centered teaching mode is facing a historic change. How to guide students to learn and communicate actively, and explore and improve the new student-centered teaching mode, has become the key problem to be solved. In the mixed cloud environment, the data stream is disturbed, and the error of mining association data is large. Aiming at the problem of poor anti-interference of scattered point cloud adaptive compression mining algorithm, this paper analyzes the data mining technology in cloud environment. Firstly, the time series analysis model of big data information flow in hybrid cloud environment is constructed to analyze the data structure, and then the high-dimensional phase space of data information flow in hybrid cloud environment is reconstructed. In the reconstructed phase space, the association rules are extracted, and the extracted features are used as pheromones to guide data location mining, so as to improve the data mining algorithm.