Reviewerly/exHarmony huggingface dataset:
Bots, Bytes, and Briefs: Cyber Investigations and Prosecutions

This chapter explores the laws and unique challenges associated with the investigation and prosecution of cybercrime. Crimes that involve the misuse of computers (e.g., hacking, denial of service, and ransomware attacks) and criminal activity that uses computers to commit the act are both covered (e.g., fraud, theft, and money laundering). This chapter also describes the roles of the various federal agencies involved in investigating cybercrime, common cybercrime terms and trends, the statutes frequently used to prosecute cybercrimes, and the challenges and complexity of investigating cybercrime.
FANE: A Fusion-Based Attributed Network Embedding Framework

Network embedding, which learns a low-dimensional representation for each node in a network, has been proved to be highly effective for a variety of downstream tasks. In this paper, we propose a novel Fusion-based Attributed Network Embedding framework (FANE), which consists of two modules. The first is the feature-learning module, in which we propose a general and scalable method SparseAE to embed different types of information (structure, attribute, etc.) into separate low-dimensional vectors. The second is the feature-fusion module, which learns the fused embedding vector to capture the underlying relationships between different types of information for downstream prediction tasks. Extensive experiments on multiple real-world datasets show that our method can outperform several state-of-the-art methods in many downstream tasks, including node classification and link prediction.
Continuous Self-adaptation of Control Policies in Automatic Cloud Management

Deep Reinforcement Learning has been recently a very active field of research. The policies generated with use of that class of training algorithms are flexible and thus have many practical applications. In this paper we present the results of our attempt to use the recent advancements in Reinforcement Learning to automate the management of resources in a compute cloud environment. We describe a new approach to self-adaptation of autonomous management, which uses a digital clone of the managed infrastructure to continuously update the control policy. We present the architecture of our system and discuss the results of evaluation which includes autonomous management of a sample application deployed to Amazon Web Services cloud. We also provide the details of training of the management policy using the Proximal Policy Optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to further scenarios.
Unsupervised Domain Adaptation Person Re-Identification Method Based on Softened Pseudo Labeling

research-article Unsupervised Domain Adaptation Person Re-Identification Method Based on Softened Pseudo Labeling Share on Authors: Tongyuan Huang Chongqing University of Technology, China Chongqing University of Technology, ChinaView Profile , Liao Chen Chongqing University of Technology, China Chongqing University of Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 100Pages 1–5https://doi.org/10.1145/3469213.3470303Online:18 August 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on University Book Sharing Cloud Platform Based on Blockchain

Through the deep integration of high-tech blockchain, cloud computing and book management technology, a book sharing service platform is designed in this paper by giving full play to the characteristics of blockchain (such as decentralization and non-tampering) and the characteristics of cloud computing (such as virtualization, expandability and configurability). Firstly, it expounded the technical characteristics and combination method of blockchain and cloud computing; Secondly, it designed the logical structure of the platform; Thirdly, it proposed an improved DPoS consensus mechanism algorithm and the specific content of smart contract design. This platform has many advantages, such as breaking down geographical barriers, connecting the book borrowing services among universities, realizing the cross-regional and cross-platform sharing of paper books, solving the problems of repeated construction and poor sharing of university library materials, saving the cumulative construction of university library management cost and improving the service efficiency of books.
Research on College English Teaching Mode Based on Artificial Intelligence

With the continuous development of the times, one of the major challenges facing college English in China is how to reduce or eliminate "dumb English". Over the years, with the continuous progress of artificial intelligence technology, it has been deeply explored in different fields and levels, such as the understanding of language knowledge or the cognition of images. However, how artificial intelligence technology needs to enter into college English teaching mode, and how to effectively and objectively integrate with college English teaching mode still needs further exploration. Based on this, this paper deeply explores the possibility of introducing artificial intelligence technology into college English teaching mode, and analyzes how to get the best effect by better integration, especially the effect of using artificial intelligence when teachers give feedback and evaluation after students' autonomous learning. On this basis, this paper explores the innovation of artificial intelligence in college English teaching mode, and integrates it into the artificial intelligence oral English training system of IFFIF. This artificial intelligence technology is mainly based on the advanced artificial intelligence language technology in Iflytek, and integrates the oral practice content in college English for evaluation, which presents the teaching analysis data more comprehensively, and supports the college English teaching mode and its management mode of different server terminals. According to the results of inquiry and analysis, it can be seen that the integration of artificial intelligence into college English teaching mode can help college English teaching mode to obtain a more novel teaching path and find the most ideal teaching mode, which is of great significance to the achievement of college English teaching.
Relocating the Load-Shedding Strategy in the Data Stream Processing Architecture

Data Stream Processing Strategy (DSPS) is a data stream engine oriented to measurement projects, which could incorporate Internet-of-Thing (IoT) devices as data collectors. Due to the IoT devices could be heterogeneous and even have its own format for data interchanging, the integration of such measures and its traceability increase the system complexity. For that reason, our strategy incorporates the role of the Measurement Adapter (MA), a software component that knowing the measurement project definition is able to match each IoT device to each metric. Previously, MA was responsible for data collecting from its associated sensors (i.e., IoT devices) and informing measures to the gathering function. Fog computing tends to carry out the data processing as close as possible on the fog devices. In this work, a proposal to relocate the load shedding techniques from the gathering function to the measurement adapter is outlined. This allows to avoid the overhead in the gathering function, which is responsible for the global data collecting, increasing the workload in the measurement adapter. Moreover, the synthesis algorithms are coordinated locally in the MA with the load shedding techniques before transmitting the measures’ streams, decreasing the use of the network, optimizing the use of local resources, and releasing resources in the data stream engine. An updated process view related to the MA’s role jointly with the processing architecture is shown. In addition, simulation results for analyzing the new MA’s behavior are presented.
The Influence of Online Games Against the Development of Student Cadre of the Faculty of Education, Makassar State University

Today's development has created a communication tool that is truly sophisticated that can be used to play games anywhere, what more can facilitate many groups in carrying out activities especially students in academic or non-academic activities (Student Affairs). But of course there are negative impacts on the game itself can specifically interfere with psychological, cognitive, and less productive activities when consuming excessive games.
AFQN: approximate Qn estimation in data streams

Abstract We present afqn (Approximate Fast Q n ), a novel algorithm for approximate computation of the Q n scale estimator in a streaming setting, in the sliding window model. It is well-known that computing the Q n estimator exactly may be too costly for some applications, and the problem is a fortiori exacerbated in the streaming setting, in which the time available to process incoming data stream items is short. In this paper we show how to efficiently and accurately approximate the Q n estimator. As an application, we show the use of afqn for fast detection of outliers in data streams. In particular, the outliers are detected in the sliding window model, with a simple check based on the Q n scale estimator. Extensive experimental results on synthetic and real datasets confirm the validity of our approach by showing up to three times faster updates per second. Our contributions are the following ones: (i) to the best of our knowledge, we present the first approximation algorithm for online computation of the Q n scale estimator in a streaming setting and in the sliding window model; (ii) we show how to take advantage of our UDDSketch algorithm for quantile estimation in order to quickly compute the Q n scale estimator; (iii) as an example of a possible application of the Q n scale estimator, we discuss how to detect outliers in an input data stream.
Analysis of Credit Strategy of Small and Medium Enterprises Based on Big Data

At present, big data has the characteristics of “high-dimensional, massive, and real-time.” The analysis based on big data can make people have a better interpretation of data, while the analysis with predictive significance can make some predictive inference based on the results of visual analysis and big data analysis. Therefore, this article mainly studies the bank’s credit strategy for small, medium and micro enterprises based on big data, and conducts quantitative analysis based on publicly available index data. It establishes a credit strategy evaluation system for credit strategies such as whether to lend, loan interest rate, and loan limit. The comprehensive evaluation method is used to analyze the bank’s credit strategy to the enterprise.
&lt;i&gt;Zemlya&lt;/i&gt; almanacs as a Moscow variation on the Petersburg-based &lt;i&gt;Shipovnik&lt;/i&gt;

The article tells the story of a rivalry between two remarkable nonperiodicals of the Russian Silver Age: the literary and artistic almanacs printed by Shipovnik [Wild rose] publishers (Petersburg, 1907–1917) and the Zemlya collections published in Moscow (1908–1917). Using the memoirs (including unpublished) left by employees of the two publishing houses and critical reviews from the early 20th c., the article pinpoints the reasons why readers perceived Zemlya as an analogue of Shipovnik . In the period from 1910 to 1917, the editor-cum-proprietors S. Kopelman ( Shipovnik ) and G. Blumenberg ( Zemlya ), neither with significant previous experience in the publishing business, made sure that their respective almanacs boasted stellar authors, a unique composition and cover design; however, Zemlya was consistently labelled as a cousin of Shipovnik . Such a description was caused by Zemlya directly copying the Petersburg-printed collections in its first issues (1908–1909), the special characteristics of the almanac as a publication type, and critical reviews of Soviet literary scholars.
The Application and Value of Calculus in Daily Life

From the angle of daily life, this paper introduces the practical application [1] of Calculus in the form of examples, and then sums up the value of studying calculus in people's ability and thinking [2]. So as to encourage everyone to study calculus, make it serve our life.
A Survey on Different Arabic Text Steganography Techniques

This research reviewed the different steganography techniques used to hide secrets within Arabic text. In this paper, we aim to provide an overview of Arabic text steganography techniques from literature and previous studies that used Arabic language. Due to the rapid development in steganography techniques and different medium used to hide secrets and send it over internet, review the Arabic text steganography is important. Hiding text within Arabic language is the art of invisible communication to keep the information confidentiality due to the unique characteristics and features of Arabic language. Arabic text Steganography technique has many benefits that can be further used for Arabic-similar languages or any languages written in Arabic letters such as Pashto language (the official of Afghanistan language), Persian language (the official of Iran language) and Urdu language (the official of Pakistan language). Nevertheless, many Arabic text steganography techniques have been introduced in Arabic, some of which will be covered in this paper.
A Simple Supervised Hashing Algorithm Using Projected Gradient and Oppositional Weights

Learning to hash is generating similarity-preserving binary representations of images, which is, among others, an efficient way for fast image retrieval. Two-step hashing has become a common approach because it simplifies the learning by separating binary code inference from hash function training. However, the binary code inference typically leads to an intractable optimization problem with binary constraints. Different relaxation methods, which are generally based on complicated optimization techniques, have been proposed to address this challenge. In this paper, a simple relaxation scheme based on the projected gradient is proposed. To this end in each iteration, we try to update the optimization variable as if there is no binary constraint and then project the updated solution to the feasible set. We formulate the projection step as fining closet binary matrix to the updated matrix and take advantage of the closed-form solution for the projection step to complete our learning algorithm. Inspired by opposition-based learning, pairwise opposite weights between data points are incorporated to impose a stronger penalty on data instances with higher misclassification probability in the proposed objective function. We show that this simple learning algorithm leads to binary codes that achieve competitive results on both CIFAR-10 and NUS-WIDE datasets compared to state-of-the-art benchmarks.
Virtual Reality Grocery Store Tour: Impact on Nutrition Education and Self-Efficacy

Use product labels to evaluate food products, identify ingredients used most in a product, understand unit costing, and understand calories per serving(s). The COVID-19 pandemic has severely limited in-person nutrition education programs, and many are now adapted for virtual learning delivery. Fortunately, previous research suggests online nutrition education programs improve nutrition knowledge similarly to in-person programs. Virtual Reality (VR) is a new technology for nutrition education. Get Cooking: A Virtual Culinary Experience is a four-week worksite wellness program designed to improve basic cooking skills, nutrition literacy, and grocery shopping self-efficacy. This study aimed to examine nutrition literacy and self-efficacy for purchasing healthy foods between two virtual grocery store tour formats. Study participants were employees of a Midwestern university recruited through the employee wellness program. Participants completed a computer-based virtual reality grocery store tour (CBVR) or a virtual PowerPoint® grocery store tour (PP). Online surveys were completed prior to the program, immediately upon completing the program, and three months after completing the program. Participants completing the entire Get Cooking program and all surveys were included in the final data analysis (CBVR n=17 and PP n=19). Both CBVR and PP participants significantly increased self-efficacy for purchasing healthy foods from pre- to post-surveys (p=0.000) and pre- to three-month post-surveys (p=0.000). There was no significant difference in self-efficacy between the CBVR and PP participants. A trend (p=0.083) between age and the post-self-efficacy score was seen with a younger population (30-39) gaining more self-efficacy than an older population (40 and older). Current findings suggest CBVR technology is as effective as a virtually-delivered PP presentation to improve grocery shopping self-efficacy. None
AI Application: Real-time Tracking of a Small Number of Moving Objects by Camera

Ordinary camera is usually fixed at a certain angle, so real-time target tracking is difficult to achieve. However, in practice, fixed-angle monitoring equipment cannot fully meet the needs of the field of security. In this paper, we proposed a monitoring system for tracking the single moving target in order to solve the above problems. The open machine vision (OpenMV) is used to achieve the image acquisition and recognition, and the dynamic targets are detected through a series of image processing algorithms. At the same time, the pan-tilt is used as the bearing tool of the camera, and then the pan-tilt is controlled through the Arduino (a Single-Chip Microcomputer) to ensure that the camera can track the target in real time and provide a better perspective to obtain more information. Experiment results are shown to prove the correctness and effectiveness of the proposed strategy.
Visual-based data exchange system for internal and external networks in physical isolation

How to realize data transmission between Intranet and extranet devices in physical isolation is an important problem. QR code can be used to identify text, image information, can be used as a carrier of information exchange. In this paper, QR code technology is used to propose a data transmission system between the Internet and the Internet under the physical isolation state based on visual recognition, which makes the information transmitted only through visible light, enhances the confidentiality, and can meet the daily office needs. At the sending end, the division of the transferred file is completed and multiple corresponding QR codes are generated. After the receiving end scans and recognizes the QR code, it finally forms a complete file identical to the original file. Then, the multi-QR code picture transmission was upgraded to the QR code stream transmission mode, and the transmission efficiency was improved by more than 10 times compared with similar work. In face of the real needs, we have added the function of retrieving pictures or database information from another computer and sending it back to the sending computer according to the instructions of the sending end. The paper also carries on the test analysis to each work.
Joint extraction of Chinese entitys and relations based on hierarchical labeling

Entitys and relations triples are the basic information units that make up the knowledge graph, and represent some simple and specific information. This paper proposes a hierarchical annotation model based on BERT to jointly extract entity relation triples from text. First, the subject of the triples is marked by the labeling method. On this basis, for each predefined relationship, the half-pointer and half-marking method is used, and the double pointer is used to mark the beginning and end positions of the corresponding object in the text, and then the Complete extraction of triples can effectively solve the problem that traditional methods are difficult to extract triples with overlapping entities.
Multi-view semi-supervised classification overview

research-article Multi-view semi-supervised classification overview Share on Author: Lekang Jiang University of Nottingham, China University of Nottingham, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 180Pages 1–7https://doi.org/10.1145/3469213.3470387Online:18 August 2021Publication History 0citation35DownloadsMetricsTotal Citations0Total Downloads35Last 12 Months35Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Semi-supervised Variational Multi-view Anomaly Detection

Multi-view anomaly detection (Multi-view AD) is a challenging problem due to the inconsistent behaviors across multiple views. Meanwhile, learning useful representations with little or no supervision has attracted much attention in machine learning. There are a large amount of recent advances in representation learning focusing on deep generative models, such as Variational Auto Encoder (VAE). In this study, by utilizing the representation learning ability of VAE and manipulating the latent variables properly, we propose a novel Bayesian generative model as a semi-supervised multi-view anomaly detector, called MultiVAE. We conduct experiments to evaluate the performance of MultiVAE on multi-view data. The experimental results demonstrate that MultiVAE outperforms the state-of-the-art competitors across popular datasets for semi-supervised multi-view AD. As far as we know, this is the first work that applies VAE-based deep models on multi-view AD.
Consortium-Chain-Based Emergency Medical Equipment Anti-Counterfeiting and Source Traceability System

research-article Consortium-Chain-Based Emergency Medical Equipment Anti-Counterfeiting and Source Traceability System Share on Authors: Songhao Lyu Nanjing University of Science and Technology, China Nanjing University of Science and Technology, ChinaView Profile , Peng Zhu Nanjing University of Science and Technology, China Nanjing University of Science and Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 121Pages 1–6https://doi.org/10.1145/3469213.3470324Online:18 August 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access