Reviewerly/exHarmony huggingface dataset:
ASPGen: an Automatic Security Policy Generating Framework for AppArmor

The security of the operating system has always been the basis of information systems. Several security frameworks have been proposed to enhance the security of the operating system, such as SELinux and AppArmor. However, the major drawback of these solutions is the complexity of the security policy configuration in which strong professionalism is required. Therefore, there are many related studies on optimizing the process of configuring security policies, but so far the involvement of security experts is still required. In this paper, we aim to further optimize the AppArmor's security policy generating process and propose ASPGen, which is a novel framework for generating AppArmor security policies automatically. ASPGen can autogenerate security policy with the least privilege and RBAC (Role-Based Access Control) for applications, and effectively alleviate the complexity and subjectivity in manually configuring AppArmor's security policy, as well as the security threats that result from the improper policy. We implement the prototype of ASPGen in Ubuntu 16.04. Unlike previous approaches, ASPGen does not depend on experts after the expert system is built. In our experimental evaluation, several typical applications are chosen and generate their AppArmor security policies with ASPGen. The completeness and precision of the generated policies are evaluated and a case of mysql-server is thoroughly analyzed by comparing the default AppArmor security policies with the policies generated by ASPGen. The evaluation demonstrates that the policy generated by ASPGen is complete, precise, and fine-grained, even without expert intervention. Our contribution can be further improved with a more general and intelligent security policy generating mechanism and is being extended to support other security frameworks including SELinux and SEAndroid.
On the computation of the endomorphism rings of abelian surfaces

We extend the result of Bisson [4] to compute the endomorphism rings of principally polarized, absolutely simple, and ordinary abelian surfaces defined over finite fields in subexponential time in the size of the base field. The abelian surfaces covered here were excluded in [4]. This is accomplished by using techniques introduced in [17] to efficiently determine overorders for Bass and Gorenstein orders. In addition we show that the endomorphism rings of certain principally polarized, absolutely simple, and ordinary abelian surfaces are not computable in subexponential time with current methods [4], [21], including ours.
User profiles’ image clustering for digital investigations

Sharing images on Social Network (SN) platforms is one of the most widespread behaviors which may cause privacy-intrusive and illegal content to be widely distributed. Clustering the images shared through SN platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The Sensor Pattern Noise (SPN) caused by camera sensor imperfections due to the manufacturing process has been proved to be an effective and robust camera fingerprint that can be used for several tasks, such as digital evidence analysis, smartphone fingerprinting and user profile linking as well. Clustering the images uploaded by users on their profiles is a way of fingerprinting the camera sources and it is considered a challenging task since users may upload different types of images, i.e., the images taken by users’ smartphones (taken images) and single images from different sources, cropped images, or generic images from the Web (shared images). The shared images make a perturbation in the clustering task, as they do not usually present sufficient characteristics of SPN of their related sources. Moreover, they are not directly referable to the user’s device so they have to be detected and removed from the clustering process. In this paper, we propose a user profiles’ image clustering method without prior knowledge about the type and number of the camera sources. The hierarchical graph-based method clusters both types of images, taken images and shared images. The strengths of our method include overcoming large-scale image datasets, the presence of shared images that perturb the clustering process and the loss of image details caused by the process of content compression on SN platforms. The method is evaluated on the VISION dataset, which is a public benchmark including images from 35 smartphones. The dataset is perturbed by 3000 images, simulating the shared images from different sources except for users’ smartphones. Experimental results confirm the robustness of the proposed method against perturbed datasets and its effectiveness in the image clustering.
Virtual Teaching Board Using Computer Vision

In the software industry many whiteboard software are available. Each of them has its own pros and cons. One of the main disadvantages of these whiteboard software is the need of accurate pointing devices. Here we introduce the Smart virtual Board, which is a hand movements based writing software. It uses the hand movements for writing. User doesn’t need an external hardware pointing device to draw. Instead the different hand movements of the fingertips are used for writing purposes. We designed this system as a primary level software product. The software can be further upgraded by improving the current features. The disadvantage of the traditional writing software is that they use traditional writing devices like mouse light, pens etc. Now it overcomes by Computer vision technology. The next evolutionary technology that will take over the world will be the hand movement technology. By using hand movements for communicating with system.
A Practical Workflow in Cleaning Master Data

Master data is the blood of industry. Due to the complicated environment of materials flow - materials are frequently exchanged from one place to another, collecting or cleaning data from such environment becomes a challenging task. Unlike the common NLP tasks which already have massive labeled data, labeled data only share a small proportion compared to thoes unlabeled dirty data in the task of cleaning industry master data. Workers who take response in recording data are inclined to make the work done as fast as possible, this may cause the data records vary in thousands ways: abbreviation of material's name, lack of basic properties, wrong measurement unit or number, etc. This paper introduces some practical methods to handle the data cleaning problem. In data preprocessing and alignment, traditional NLP technics such as trie [1], Aho-Corasick(AC) [2] and wordnet [3] can take advantage of hidden statistical informations; we use tree search to help solving data correcting problem which can construct all possible combinations of data records; finally lattice-LSTM [4] are applied in splitting and reconstucting data properties.
Research on Application of Computer Big Data Technology in Blockchain Computing

Abstract Blockchain originated in 2009, it was the underlying “ledger” recording technology of Bitcoin. After several years of development and improvement, it has gradually become a distributed, decentralized, and trustless technical solution. In the past two years, blockchain has become a new decentralized distributed computing paradigm. We apply blockchain technology into cloud computing, using the former’s security mechanism to improve the latter’s secure storage and secure computing performance. This is a promising research topic. The paper mainly discusses the design and implementation of blockchain from a technical prospect, it summarizes the advantages and disadvantages of the blockchain, and it also analyzes the possible application scenarios.
Capelin: Data-Driven Compute Capacity Procurement for Cloud Datacenters Using Portfolios of Scenarios

Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners with merely rule-of-thumb judgement. We derive requirements from a unique survey of experts in charge of diverse datacenters in several countries. We propose Capelin, a data-driven, scenario-based capacity planning system for mid-tier cloud datacenters. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. Capelin compares alternative topologies and for each gives detailed quantitative operational information, which could facilitate human decisions of capacity planning. We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. The results give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption.
Machine Learning Technique for Target-Based Sentiment Analysis

The study covered with machine learning strategies utilized in the analysis of sentiments at both the sentence and viewpoint levels. It likewise tended to the advantages and inconveniences of the different condition of-the-craftsmanship procedures. This paper perceived that the arrangements viability contrasted by the technique. As far as estimation system, there is an absence of institutionalization. Accordingly, it is beyond the realm of imagination to expect to reach determinations from the present condition of-the-workmanship approaches where strategy gives the best result to every area. In any case, a move from the customary word-based way to deal with semantic-based idea driven methodology is obviously apparent in the measurement level sentiment analysis. Machine learning approaches alongside semantic idea driven methodology can create another age of calculations that can react to language and significant level settings. This gap will likewise leave an enormous region for the scientists to find out experimental and emotional views of a human
Efficient Method for Storing Health Record in Cloud Using Integrity Auditing and Data Sharing

Abstract Identity based cloud storage authorization is proposed in this paper to provide protection of cloud data. Many sensitive information is stored in the cloud, and it must be kept safe from unauthorized access. By hiding the sensitive information’s, privacy for an individual or organization is provided. The data in the cloud are secured by hiding the sensitive information. In the proposed system when the user and data analyzer sign-in they will receive the verification mail to assure as a valid user or data analyzer. The verification key for the user and the data analyzer is generated by the Private Key Generator (PKG). The user and the data analyzer enter the cloud using the verification key to upload and retrieve data from the cloud. Until uploading data to the cloud, the user encrypts the data, concealing confidential details. The Third Party Auditor is in charge of the cloud auditing process (TPA). This scheme allows data files stored in the cloud to be shared and used by data analyzers under the condition that confidential information is hidden, allowing the auditing system to operate more efficiently.
Towards Revealing Parallel Adversarial Attack on Politician Socialnet of Graph Structure

Socialnet becomes an important component in real life, drawing a lot of study issues of security and safety. Recently, for the features of graph structure in socialnet, adversarial attacks on node classification are exposed, and automatic attack methods such as fast gradient attack (FGA) and NETTACK are developed for per-node attacks, which can be utilized for multinode attacks in a sequential way. However, due to the overlook of perturbation influence between different per-node attacks, the above sequential method does not guarantee a global attack success rate for all target nodes, under a fixed budget of perturbation. In this paper, we propose a parallel adversarial attack framework on node classification. We redesign new loss function and objective function for nonconstraint and constraint perturbations, respectively. Through constructing intersection and supplement mechanisms of perturbations, we then integrate node filtering-based P-FGA and P-NETTACK in a unified framework, finally realizing parallel adversarial attacks. Experiments on politician socialnet dataset Polblogs with detailed analysis are conducted to show the effectiveness of our approach.
Ionograms Scaling by Using the Convolutional Neural Network

Ionosphere in F layer has the most irregularity for phenomenon occurrence of amplitude scintillation which leads to the problem in the satellite signals. Ionosphere can be observed by Ionosonde to study F2 layer critical frequency (foF2) parameter and height of F layer (h'F) parameter from the ionogram. This paper presents the convolutional neural network (CNN) to determine foF2 and h'F parameters. The simulation start from passing the ionogram images to the proposed CNN model with 2,000 epoch training. The simulated accuracies of both foF2 and h'F parameters are equal to 92.8% and 98.4%, respectively.
Metric Learning via Penalized Optimization

Metric learning aims to project original data into a new space, where data points can be classified more accurately using kNN or similar types of classification algorithms. To avoid trivial learning results such as indistinguishably projecting the data onto a line, many existing approaches formulate metric learning as a constrained optimization problem, like finding a metric that minimizes the distance between data points from the same class, with a constraint of ensuring a certain separation for data points from different classes, and then they approximate the optimal solution to the constrained optimization in an iterative way. In order to improve the classification accuracy as much as possible, we try to find a metric that is able to minimize the intra-class distance and maximize the inter-class distance simultaneously. Towards this, we formulate metric learning as a penalized optimization problem, and provide design guideline, paradigms with a general formula, as well as two representative instantiations for the penalty term. In addition, we provide an analytical solution for the penalized optimization, with which costly computation can be avoid, and more importantly, there is no need to worry about the convergence rates or approximation ratios any more. Extensive experiments on real-world data sets are conducted, and the results verify the effectiveness and efficiency of our approach.
Designing Conversations: Using Life Stories to Improve Dementia Care

Dementia is a long-term condition that impacts on various aspects of a person’s functioning. The person-centred care approach has been shown to be integral to quality of life however the communication challenges facing people with dementia makes understanding the needs of the person difficult. Our research shows that this is a big issue for people with dementia and their families and carers and can be compounded as they navigate different elements of the care. This paper explores the role of design in the context of creating tools to support communication with people living with dementia and personalise their care, with a particular focus on the use of co-design methods for drawing out and sharing people’s life experiences and stories. Two case studies are presented which describe how designers and design educators in Australia and Canada have approached this topic.
IN-LDA: An Extended Topic Model for Efficient Aspect Mining

In last decade, LDA is extensively used for unsupervised topic modeling, and various extension of LDA has also been proposed. This paper presents a semi-supervised extension IN-LDA, which uses very few influential words related to the domain for providing supervision in the topic generation process. IN-LDA also improves the performance of the LDA generation process in two ways. First, it deals with multi-aspect terms by passing N-grams vectors, and second simulated annealing-based algorithm is used for tuning hyperparameters of LDA for more coherent output. The experiment is conducted on two popular datasets, movie reviews and 20Newsgroup. IN-LDA is showing improved results when compared with others on coherence value. It also shows a better interpretation of output due to influential words.
Petrography, whole-rock geochemistry and mineral chemistry of clinopyroxene and amphibole in basic alkaline igneous rocks of Shemshak Formation, S Azadshahr, Eastern Alborz

Petrography, whole-rock geochemistry and mineral chemistry of clinopyroxene and amphibole in basic alkaline igneous rocks of Shemshak Formation, S Azadshahr, Eastern Alborz
Mining biomedical texts based on statistical method and association rules

Relation extraction in biomedical texts intends to uncover interactive features about concepts from these texts. This paper focuses on the extraction of relations between diseases and treatments. For this purpose, an approach combining association rules mining, and a statistical method, TFIDF is proposed. We suggest an extension of Apriori, the most popular algorithm used in association rules mining. The novel algorithm Apriori-TFID generates all frequent item sets. Nonetheless, pruning the infrequent item sets is performed according to the support, and the TFIDF weight. The algorithm is implemented and integrated on the platform Weka. The evaluations show, that the algorithm Apriori-TFID outperforms the ancient Apriori algorithm.
The role of localizable concurrence in quantum teleportation protocols

Teleporting an unknown qubit state is a paradigmatic quantum information processing task revealing the advantage of quantum communication protocols over their classical counterpart. For a teleportation protocol using a Bell state as quantum channel, the resource has been identified to be the concurrence. However, for mixed multi-partite states the lack of computable entanglement measures has made the identification of the quantum resource responsible for this advantage more challenging. Here, by building on previous results showing that localizable concurrence is the necessary resource for controlled quantum teleportation, we show that any teleportation protocol using an arbitrary multi-partite state, that includes a Bell measurement, requires a non-vanishing localizable concurrence between two of its parties in order to perform better than the classical protocol. By first analyzing the GHZ channel and GHZ measurement teleportation protocol, in the presence of GHZ-symmetric-preserving noise, we compare different multi-partite entanglement measures with the fidelity of teleportation, and we find that the protocol performs better than the classical protocol when all multi-partite entanglement measures vanish, except for the localizable concurrence. Finally, we extend our proof to an arbitrary teleportation protocol with an arbitrary multi-partite entangled channel.
Teaching Design of Online Course for Film and Television Art Majors based on Cloud Learning

Online Learning is a way to build an online education platform for learners to learn. It realizes the requirement that learners can receive education anytime and anywhere and breaks the limitation of time and space in traditional teaching. With the rapid development of information technology, the Internet and cloud technology have injected new vitality into education and teaching, and courses based on cloud learning platform represented by MOOC, SPOC and Micro courses have emerged at the historic moment. Based on the service and characteristics of cloud computing, this paper proposes the teaching design scheme of online course based on cloud computing, and takes 89 undergraduates majoring in film and television art who take the course of Micro-video Creativity and Production as the investigation objects to conduct the research by combining qualitative and quantitative research. Firstly, the status of cloud learning platforms at home and abroad is sorted out through literature review to grasp the operation of cloud learning platforms at home and abroad. Secondly, the course teaching based on cloud learning platform is implemented for H students who take micro-video Creativity and Production. Thirdly, further statistical analysis is conducted on the data collected from the background of the cloud learning platform through early interviews and data provided by the background of the cloud learning platform. Finally, put forward relevant Suggestions.
Features of Software Implementation of Logical Tasks in Prolog

One of the promising areas for using Prolog-systems is to solve logical tasks. This study outlines a solution approach based on the state generation procedure and the verification procedure. A solution to a logical task is presented, which demonstrates in practice the proposed approach and method of specifying a procedure for generating states. In the proposed example, a bit chain is generated that defines the code of a particular letter in the solution of the applied problem. Building a solution by means of code generation with verification allows not storing in the knowledge base a binary tree of all possible codes. The process of generating new states can be associated with the training of the program, with the dynamic formation of the knowledge base. The approach is based on the capabilities of software environments for adding facts and rules to existing ones, which were obtained as the results of the program or its stages. In this case, the entire program is the generating rule. An analysis of the constructed and tested procedures for the dynamic generation of states and the generation of facts allows us to talk about the applicability of such a solution for certain applied problems.
Image Encryption Algorithm Based on Hyperchaotic System and DNA Coding

Aiming at the problems of large amount of data, strong correlation between adjacent pixels and high redundancy in the process of color image encryption, a color image encryption algorithm based on hyperchaotic system and DNA coding operation is proposed, which combines with the neural network to generate a better fusion random sequence as key stream, and uses DNA transformation rules and block encryption mechanism to realize image scrambling diffusion encryption in pixel level. The simulation results show that the encryption algorithm has high security integrity and strong anti-attack ability.