Reviewerly/exHarmony huggingface dataset:
A Method of Malicious Data Flow Detection Based on Convolutional Neural Network

Network intrusion detection can protect the devices and data in a network from the threat of network attacks. Nowadays, network intrusion detection research based on traffic data mostly uses the statistical features of traffic data to design algorithms, which has problems such as heavy dependence on expert experience and low accuracy. This paper has proposed a CNN model using the characteristics of traffic packet contents to detect network intrusions. The CNN model is used to extract content features from the traffic packets and then identify the malicious payloads in the packets. The performance of this method is evaluated by the experiments of 9 common categories of malicious payloads and normal traffic. and the results show that the model has the ability to accurately detect network intrusions with high performance.
Survey on Knowledge Graph Embedding Based on Hyperbolic Geometry

It is a prevalence to store human knowledge in knowledge graphs that connect entities with relations. Recently, temporal knowledge graph has received more attention, since real-world events are dynamic. The representation learning of temporal knowledge graph can better satisfy the needs of practical applications. Traditional Euclidean embedding methods are shown to incur large distortion when represent hierarchical or scale-free network. Luckily, hyperbolic geometry provides powerful tools for tackling this problem and is capable of efficiently representing knowledge graphs with these properties. In this survey, we summarize and compare the existing work of the Euclidean embeddings and the hyperbolic embeddings, and demonstrate temporal knowledge graph embedding based on hyperbolic geometry is a promising research direction.
Investigations on Answer Selection Based on Co-attention Guided Stacked BiLSTM with BERT

With the rapid development of the Internet, massive amounts of data accumulate on the Internet. Users need to quickly obtain effective text information from these large amounts of documents. The automatic question answering technology can quickly analyze the input questions and find the answers. In this paper, we focus on answer selection, one kind of automatic question answering. Different from the traditional word2vector embedding representation, we use the output representation of the BERT pre-training model. we propose a multi-layer LSTM network with a co-attention mechanism. The common attention mechanism allows questions to directly affect the representation of answers. On the contrary, answers also affect questions, It strengthens the connection between the questions and its correct answers. Our model was evaluated on the NLPCC 2016 DBQA dataset, the MRR score of baseline model is 78.53&#x0025;. Experimental results show that co-attention guided stacked BiLSTM(Bi-directional Long Short-Term Memory) are obvious effective. Compared with the word2vector model, the BERT pre-training model has greatly improved in terms of word embedding. The highest MRR of the Co-attention Guided Stacked BiLSTM with BERT model proposed in this paper is 83.73&#x0025;, which is an increase of 5.2&#x0025;.
Research on Label Based Data Flow Control Mechanism

Aiming at the problem of low evaluation efficiency caused by complex and large number of policies in the complex network, in view of the characteristics of a label are flexible, lightweight, and efficient policy matching, we propose a label-based data flow control mechanism. We build the system model and design the label description method around the operation of data. Our model covers a variety of control models based on roles, relationships and attributes, which can be adapted to more application scenarios. The label only includes the attribute rules necessary for flow control and allows users to customize it according to security requirements, so as to achieve the purposes of lightweight, fine-grained, and high flexibility. The label constraint rules are formulated and the corresponding algorithm for generating a visitor&#x0027;s permission set is proposed. The selection of the attribute rule type is added to the label, which makes the policy evaluation more efficient. Finally, performance analysis and use case analysis illustrate the availability and effectiveness of our mechanism.
Detecting Adversarial Samples in Neural Network with Statistical Metrics: A Practical Approach

The inherent vulnerability of deep learning systems, which is subject to adversarial attacks, limits its application in the safety-critical domain such as automatic driving, military application and so on. More seriously, the implementations of adversarial perturbation in the physical world are proved feasible by a considerable body of works. Hence, it is crucial to develop the capability of defense that matches adversarial attacks. In this paper, we firstly investigate various defensive methodologies and find that the detection-only approach shows its superiority in terms of robustness and efficiency when facing strong attack strategies. Next, detection approaches with different statistical metrics are presented, such as Kernel Density Estimation(KDE), Local Intrinsic Dimensionality (LID) and Neural Network Invariant Checking (NIC), to prove the hypothesis that adversarial examples can be totally detected if choose the reasonable statistical metrics. Hence, we believe that the research on finding more proper metrics is a promising and practical direction. Moreover, given the absence of a systematic evaluation framework, we proposed a threat model and evaluation criteria as the first step for evaluating various defense measures. Finally, it also remains a huge challenge and needs further researches to accomplish a trade-off between security and efficiency in this area.
Sensitive Image Information Recognition Model of Network Community based on Content Text

With the advent of the multimedia era and the age of picture-reading, the recognition effect of sensitive content of image data has become the key to maintain the information security of network communities. At present, the image classification and recognition technology for sensitive images in network communities cannot obtain the semantic content of images, and it is difficult to combine the image information with the knowledge in network communities, resulting in low recognition accuracy and poor interpretability, and it is difficult to trace the transmission and fermentation of image information in network communities. To solve this problem, this paper proposes a sensitive image information recognition model of network community based on content text by using image caption technology. Through the text description of the image content of the network community, and the integration of a large number of network community text knowledge, the model can finally identify the images containing sensitive content more accurately and more understandable, and the transmission of image information on the network can be traced through the content text. In this paper, MSCOCO(Microsoft Common Objects in Context) dataset and sensitive image self-made dataset of network community are used as the training set. The experimental results show that the method presented in this paper is significantly better than the model based on image classification task in terms of accuracy and traceability of image sensitive information recognition results, which proves the feasibility and effectiveness of sensitive image information recognition in network communities based on content text.
A Manifold Learning Method to Passage Retrieval for Open-Domain Question Answering

Passage retriever plays an important role for obtaining answers in open-domain textual question answering system, which selects candidate contexts from a large collection of documents and feed to the machine reader. Traditional defacto methods usually construct sparse vectors to match the rules of co-occurrence of words between passages and questions, such as TF-IDF or BM25. And some more advanced methods model word-level contextual semantics similarities to match the text. In this work, we presents a method of encoding text by short sliding windows with built-in continuity, and applying manifold learning method on it to model continuous representation of semantics, so as to represent the similarity features at the passage-level and reduce the directional sparsity difference caused by the difference of text length. Compared with the traditional Lucene BM25 system in the top-20 paragraphs retrieval, the accuracy of our method is 5&#x0025;-16&#x0025; higher, and the recall rate is 8&#x0025;-16&#x0025; higher.
Multi-Object Tracking Using Gradient-Based Learning Model in Video Surveillance

On accomplishing an efficacious object tracking, the activity of an object concerned becomes notified in a forthright manner. An accurate form of object tracking task necessitates a robust object tracking procedures irrespective of hardware assistance. On the other hand, the tracking gets affected owing to the existence of varied quality diminishing factors such as occlusion, illumination changes, shadows etc novel background normalization procedure articulated on the basis of a textural pattern is proposed in this paper. Environmental Succession Prediction algorithm for discriminating disparate background environment by background clustering approach. Probability based Gradient Pattern (PGP) approach for recognizing the similarity between patterns obtained so far. Comparison between standardized frame obtained in prior and those processed patterns detects the motion exposed by an object and the object concerned gets identified within a blob.
An Overview of Entity Alignment: Cross-lingual and Graph Approaches

Knowledge Graph is a technology for more effective knowledge sharing and makes the web smarter. By building Knowledge Graphs, information resources can be more easily calculated, understood and evaluated, effectively and accurately contributing to further development in various fields. Research on Knowledge Graph fusion aims to generate new knowledge by using models to fuse knowledge graphs from various domains, in which entity alignment is one of the fundamental researches in this field. For state-of-the-art studies, the hotspot of entity alignment primarily focus on cross-lingual and graph learning approaches. In this paper, we first introduce the main problems faced in the field of entity alignment research. Then the acquisition and usage of relevant models of entity alignment for cross-lingual and graph learning approaches are introduced. Next, the most frequently used evaluation indexes of entity alignment are presented. Finally, the challenges and future research directions of entity alignment in these two fields are further discussed.
The Experiences of First-Time Honor Band Clinicians: A Multiple Case Study

Abstract The purpose of this multiple case study was to explore the honor band experiences of first-time honor band clinicians. I used intensity sampling to select two participants who had both served as an honor band clinician for the first time in 2019. Using Glaser's (1996) three-stage theory of expertise as a theoretical framework, I conducted individual semistructured interviews to investigate participants’ honor band experiences. In the first stage of external support, both participants used various means of obtaining information and advice, such as calling mentors and doing research online. The second stage of transitional mentorship was mentioned least by the participants, perhaps because this is not very common for honor band clinicians. Both participants seemed to spend the majority of their first honor band experience in Glaser's third self-regulatory stage, with less-than-adequate preparation from the previous stages. They both mentioned making quick adjustments to solve problems throughout their time as an honor band clinician. I suggest creating more training experiences and resources for first-time honor band clinicians in order to make honor band events more beneficial for students and clinicians.
Software Testing Strategies for Detecting Hypercall Handlers' Aging-related Bugs

With the continuing rise of cloud technology hypervisors play a vital role in the performance and reliability of current services. As long-running applications, they are susceptible to software aging. Hypervisors offer so-called hypercall interfaces for communication with the hosted virtual machines. These interfaces require thorough testing to ensure their long-term reliability. Existing research deals with the aging properties of hypervisors in general without considering the hypercalls. In this work, we share our experience that we collected during trying to understand hypercalls and their parameters and use them to construct test cases for hypervisor aging of Microsoft Hyper-V. We present a bug that we detected, which was reported and acknowledged by Microsoft. Further, based on our manual binary code analysis, we propose the idea of automating the analysis process to detect valid parameter ranges and execution conditions of hypercalls without manual effort.
A Decentralized Access Control Framework For DDS

To avoid problems such as single point of failure caused by relying on central authorization, for Data Distribution Service (DDS), this paper presents the DAFD (&#x201C;Decentralized Authorization Framework for DDS&#x201D;) authorization framework. Compared with existing solutions and the DDS Security Framework, DAFD does not rely on permissions Certification Authority (CA) for authority management, which reduces the threat of malicious modification of permissions. DAFD also allows fast, automatic and low-traffic cross-administrative-domain authority verification to control the behavior of DDS participants and the subscription and publication of information made by DDS participants.
Towards Informative Tagging of Code Fragments to Support the Investigation of Code Clones

Investigating the code fragments of code clones detected by code clone detection tools is a time-consuming task, especially when a large number of reference source files are available. This paper proposes (i) a method for clustering a clone class, which is detected by code clone detection tools using syntactic similarity, based on topic similarity by considering its code fragments as sequences of words and (ii) a method for assigning short tags to clusters of the clustering result. We also report an experiment of applying the proposed method to packages of an open source operating system.
Towards a pluralistic, dynamic definition of usability

Usability has a rich and dynamic history coming into existence in the late 1970s, but usability goals, practices, and testing methods were engaged with by technical communicators long before usability had a name. This paper discusses the evolution of usability as a quality and as major design objective for information products and technology by interdisciplinary practitioners. Multiple definitions and usability criteria will be provided, as well as a framework for designing quality usability studies, which allows researchers the flexibility to choose their own definition of usability and design a rigorous usability study.
Interframe Variance in Vehicle Re-identification

Convolutional Neural Networks (CNNs) are usually treated as backbones in vehicle re-identification (ReID). CNNs are considered to be translation-invariant because features captured by pooling layers are position-independent. However, several authors point out that CNNs can detect objects&#x0027; spacial position within images, and some real-world samples cause a cliff descent on CNNs&#x0027; precisions. In this paper, we find out that these phenomena also occur in vehicle ReID. As vehicle ReID datasets are obtained by extracting successive frames from city road surveillance videos, alias introduced from inconspicuous changes between frames, which is call interframe variance in this paper, is even more severe than variance between different vehicle identities. After comparing experiments on different types of networks with varied depths, we conclude that it is quite common for CNNs, especially for deep CNNs. We finally give out solutions to reduce interframe variance in vehicle ReID by changing the classification problem into retrieval problem. Also, these findings provide a novel clue for designing black box adversarial samples, which posts a potential threat for the security of artificial intelligence.
Rooted Subtrees Recursive Neural Networks on Graphs

Graph classification is a fundamental task via rightly recognizing patterns of graph structure or sub-structures. We propose a novel end-to-end framework, RSRNN, for classifying general node-labeled graphs, which is composed of extracting Rooted Subtrees from graphs and learning Recursive Neural Network on subtrees. For generalizing the existed Tree-LSTMs to graph problems, we develop a new branch attention mechanism for fitting RSRNN to arbitrary rooted subtrees structure. This framework is tested on benchmark data sets and major results prove that it is competitive with the state-of-the-art of a convolutional neural network and graph kernels.
A Universal Targeted Attack Method against Image Classification

Rapid advancement in deep neural networks (DNNs) has enhanced the prospect of image classification technologies like facial recognition. The emergence of adversarial examples, however, revealed the vulnerability of DNNs. A large number of attack methods have been proposed against DNNs and these methods can be divided into two types: targeted attacks and untargeted attacks. Specifically, targeted attacks misclassify the detection object into a specified target, whereas untargeted attacks only aim to lead the classifier (a DNN classifier, for instance) to misclassification of the object by adding adversarial examples. Most adversarial attack methods add different perturbations to different images, which is costly and inefficient. In this paper, we explore a universal targeted attack method against image classification, in which a universal perturbation is fabricated and added to all images to generate adversarial examples. These generated adversarial examples are classified by the classifier as a specified target with high probability. Through black-box and white-box attack experiments against the ResNet model on both CIFAR 10 and CIFAR100 datasets, we verified the effectiveness of our method.
Corpus-based teaching materials for specialised courses: the case of English for mathematicians

This paper demonstrates a usage-based approach, drawing on language corpora, to creating teaching materials. The need for such materials stems from the growing demand for courses of English for specific purposes on one hand, and the surprising lack or inadequacy of available materials for these courses on the other hand. On the example of English for mathematicians, we present three different types of exercises. First, we show deductive exercises for practising an already familiar rule. Second, we focus on the creation of an exercise where students start with a general rule but derive more specific information from specifically chosen examples. Finally, we present a fully inductive exercise, aimed at familiarising the students with the structures specific of the texts from their discipline. We believe that these exercises may serve as a source of inspiration for other teachers of English for specific purposes who find themselves in a similar situation.
PROCESSING OF THE TEXTS INCLUDED IN METAMARKING ON THE SPECIAL COMPUTER SOFTWARE

The article deals with the problem of metalinguistic markup of texts within the National Corpus. The meta-markup systems of the proposed Kazakh texts are described. Meta-markup includes a link to the text of parameters that characterize the text as a whole. Meta-markup, allows the user to search and select texts to compose an internal structure with specified properties. Meta-tagged materials are an indispensable source in research work on the collection of materials related to a certain period, style, author, topic, etc. One of the main tasks when choosing a literature when creating a corpus is taking into account the genre diversity of texts. The necessity of introducing meta-markings in the manufacture of the case is not controversial. They allow the user of the corpus, especially the correspondent researchers, to quickly and easily find any information they need. In addition, linguistic information is extracted whenever possible from all language levels. But in the corpus, the provision of linguistic information at all levels at once is extremely difficult and the possibilities are limited. Nevertheless, a program of metatext markings and linguistic markings – morphological, word-formation, phonetic, morpho-semantic markings, has been launched in the “corpus of Kazakh texts”. The article describes the types of metatext on the corpus (23 parameters), the mechanisms of functioning of the program of linguistic markup.
Predictive Sentimental Analysis of Spam Detection using Machine Learning

The development of technology in recent years, a surge in the marketing content and the inexpensive choice of sending text messages for promotional and other advertising purposes has made the practice of SMS (Short Message Service) on cell phones escalate to such a prominent manner that cellphones are constantly overburdened through spam SMS. As a result, important messages like bank or work-related information can get lost among the unimportant spam messages. Moreover, these spam messages are extremely harmful since they can breach our privacy and expose our personal information to hackers and other potentially hazardous sources. This issue can be mitigated by employing the Sentiment Analysis and variety of Machine Learning Algorithms that are appropriate for separating spam from important communication. This paper analyses the methodology of intelligent spam filtering approaches in the SMS paradigm with respect to mobile text message spam. It tests some of the most prominent spam filtering algorithms on publicly available SMS spam datasets to discover which ones perform best in this situation.
OpenStack Cloud Deployment for Scientific Applications

Cloud computing technology in recent years has seen rapid growth with a number of institutions and organizations adopting it, for its scalable, extensible and rapidly available services. Many scientific institutions over the years have been executing high performance jobs on traditional high-performance computing (HPC) clusters, but the ever-increasing use of resources calls for optimizing the existing infrastructure to deliver better ubiquitous services. This paper presents the implementation of OpenStack cloud computing platform for executing scientific applications at IISER, Pune. This platform additionally can be tailored to serve the institute&#x0027;s need and requirements. The paper also analyses and discusses the effectiveness of our deployment method, concluding with some feasible scenarios that are achievable to make the cloud scalable and heterogeneous.