Reviewerly/exHarmony huggingface dataset:
Developing an Undergraduate Course Curriculum for Ethical Hacking

An Ethical Hacking (EH) course not only is a critical component for a Cybersecurity program but also an essential preparation for CS/IT majors towards career paths as security professionals. We face two major challenges when developing an undergraduate EH course, including the setup and choice of the lab design, and the choice and organization of covered topics for this course. On one hand, we have limited space, budget and technical support for a course that relies heavily on hands-on exercises. Given the nature of this course, the lab activities are often 'offensive' and lab operations demand administrative privileges, which cause compliance issues with the university's IT policies. On the other hand, given the vast variety of topics and the fast pace of the field, it is difficult to select and organize an essential set of knowledge units to ensure that students are exposed to current technologies and prepared to be industry-ready. We adopt two major design principles to address these challenges correspondingly. First, our choice of a hybrid Virtual Machine (VM)-based and Web-based labs provides students the full set of privileges to perform lab activities without posing threats to the campus network. The Web-based labs remove high cost of hardware and avoid overwhelming installations and configurations for the lab. Second, given the diversity of topics and fast developments in this field, we choose topics based on four criteria: representative, current, certification-related, and foundations for other covered concepts. The chosen topics are aligned with three EH certificates, and organized into twelve modules with clear inter-module and intra-module logic. This paper details the curriculum of this EH course and elaborates how our design principles are entailed in the course.
Research on Photovoltaic Subsidy System Based on Alliance Chain

At present, the photovoltaic subsidy system is based on the centralized trusted third-party operation, which has the problems of data tampering, loss and non-traceability. Blockchain technology has the characteristics of decentralization. The application based on blockchain technology does not need the intervention of a trusted third party. Any two nodes can directly trade, making the transaction data not easy to be tampered with, lost, and traceable. In this paper, the blockchain technology is applied in the field of photovoltaic subsidy, and a photovoltaic subsidy system based on the alliance chain is developed, which solves the above shortcomings of the original photovoltaic subsidy system and makes the data in the system real and reliable.
Image Processing and Location based Image Querier(LBIQ)

With the rapid amount of data being generated, circulated and reused over the internet. Image processing is one of the major fields in which an application like ‘LBIQ’ would be able to query an image and process it for various variables that can be extracted out of it and the extracted values can be used to improve the quality of service given in any business to consumer given field ‘LBIQ’ is configured in a way so that it can be optimized for commercial use. It mainly uses a raspberry pi for monitoring a live feed and further particular attribute values can be obtained to process continuous feed results. ‘LBIQ’ also acts as an object search engine for searching content or processing out an object from the image. All these objectives can be merely achieved through the use of trained set of images and objects. An LBIQ takes into account only a single input that is an image and is able to give out a set of attributes which can be further processed for convenience of service.
Lessons learned—How to make them stick

Abstract After an incident investigation we gather the facts, assign causes and compile a set of lessons that should, if learned well, prevent a similar incident from occurring. Unfortunately, too often we see the same type of incident happening, sometimes in the same company that had the original incident, although not necessarily in the same facility, or in a company using the same process technology as the company that had the incident. What happened? We do not learn by listening to a talk or reading a bulletin, at least not for the long term. Our memory fades with time. Even if not, we still need to relate the incident findings to the planning and execution of our work. Thus, the way to remember those lessons is by making them vivid when training, incorporating them in our technology and daily operations, and periodically reinforcing them. If we manage to do that, there is a good chance that the lessons will be woven into the fabric of the company and incidents will not be repeated.
Design of In-depth Security Protection System of Integrated Intelligent Police Cloud

In order to strengthen the security protection of the police cloud platform, ensure the safe and stable operation of the cloud infrastructure, core-data and key-applications, and ensure the overall security and controllability of the police cloud, Based on the four layer architecture composed of IaaS, PaaS, DaaS and SaaS (i.e. infrastructure, platform service, data service and application service layer), this paper constructs the infrastructure guarantee environment for cloud computing and big data, and builds a multi-level, cross network, wide area network police cloud vertical and deep security protection system.
Research on Content-Based Reciprocal Recommendation Algorithm for Student Resume

At present, the emergence of this kind of recruitment website promotes the recommendation calculation method’s rapid progress. However, the old recommendation algorithm is based on the preferences or requirements of individual participants. The current job-hunting situation should give not only excellent employees but also give suitable jobs to job seekers. So there is this kind of Reciprocal Recommendation Method. The purpose of such a way is to recommend customers one-to-one. There is a similarity between the two, and use this similarity to ensure the suitability and stability of the job. In this system, enterprises can confirm the keywords needed within the range of resume selection setting, and the system will select students’ resumes according to the keywords set. In this paper, we combine the characteristics of Chinese resume, analyze the two-way reciprocal recommendation mode of school enterprises and students, and finally use the simulation test to test the accuracy and effect of the content-based recommendation algorithm.
Optimization based path planning for a two-body articulated vehicle

An articulated vehicle is a two-body design capable of precise maneuvering around obstacles, while carrying heavy loads over rough terrain. In the context of path planning for automated articulated vehicles, it is desirable to fully utilize the maneuverability of the vehicle to enable autonomous operation in confined areas. In this paper we study the impact of model accuracy in an optimization based path planner for an articulated vehicle. For this purpose, we compare the traditional kinematic bicycle model with a two-body articulated model. We evaluate performance in terms of path length, path quality, success rate and computation time through performing test queries in artificial environments and through experiments on a full scale articulated hauler. Results show that for simple, unidirectional maneuvers, performance differences are small, but for more difficult bidirectional maneuvers, the articulated model produces shorter and higher quality paths at a higher success rate. However, the articulated model has 2.75 times longer computation time on average.
Evaluating Student Participation in Undergraduate Information Technology Programs in the U.S.

Enrollment, retention, and graduation rates of undergraduate students in Information Technology (IT) programs are useful measures of institutional performance. Disaggregated by demographics characteristics, such as gender, race, and ethnicity, analysis of student data across IT programs in the U.S. supports the exploration of the breadth and diversity of student participation in IT. Evaluating undergraduate IT programs is particularly challenging for multiple factors, including: IT programs are not always titled "Information Technology"; IT programs are not always ABET-accredited; and IT programs may be housed in various academic units, such as business, computing, engineering, technology, or information sciences.
BIVS: Block Image and Voting Strategy for Weather Image Classification

Timely and accurate weather information is important for smart grid systems, autopilot systems and intelligent surveillance systems. This paper studies how to obtain weather information from a single image. The biggest challenge of weather image classification task is that there can be the same objects and features in images representing different weather conditions. To address this problem, first of all, this paper constructs the weather image dataset under outdoor transmission line scene, including images of foggy, rainy, snowy and sunny. Then, a weather image classification method based on block image and voting strategy is proposed. The method of block image and voting strategy achieves 98.74% classification accuracy in weather image dataset.
Design and Implementation of Handwritten Digit Recognition Based on K-Nearest Neighbor Algorithm

In this paper, a handwritten digit recognition system based on K-nearest neighbor algorithm is designed and implemented. By analyzing the application principle and steps of k-nearest neighbor algorithm in machine learning classifier, we design handwritten digit recognition in Python language. In order to improve the accuracy and accuracy of handwritten character recognition, the method of smooth denoising and character segmentation methods are used to preprocess the data, extract the structure and statistical features. The experiments had shown that the accuracy of the algorithm for handwritten character recognition can reach over 97%.
Feature Selection based Classification of Spams Using Fuzzy Support Vector Machine

E-mail is a safe way for the online form of communication as well as for transferring data or messages through the web. A standard increase in popularity, which gives rapid growth to unsolicited emails in bulk amount Numerous methods are available to filter the data which is to detect automatically as well as eliminate such untenable messages. There aoe a large number of techniques for email spam filtering such as clustering techniques, learning-based technique, knowledge-based technique, heuristic processes and many more. The research of spam filtering by using the feature selection through relief feature selection technique and then classifies data by using a fuzzy support vector machine to deal with the factors of uncertain type. The experimental outcome illustrates that given algorithms can improve the filtering rate parameters and spam filtering speedmore efficiently.
RuKBC-QA: A Framework for Question Answering over Incomplete KBs Enhanced with Rules Injection

The incompleteness of the knowledge base (KB) is one of the key issues when answering natural language questions over an incomplete knowledge base (KB-QA). To alleviate this problem, a framework, RuKBC-QA, is proposed to integrate methods of rule-based knowledge base completion (KBC) into general QA systems. Three main components are included in our framework, namely, a rule miner that mines logic rules from the KB, a rule selector that selects meaningful rules for QA, and a QA model that aggregates information from the original knowledge base and the selected rules. Experiments on WEBQUESTIONS dataset indicate that the proposed framework can effectively alleviate issues caused by incompleteness and obtains a significant improvement in terms of micro average Fl score by 2.4% to 4.5% under different incompleteness settings.
A Multi-Protocol Smart Sensing Method Based on Lightweight Deep Neural Network for Edge Computing

Correctly recognizing protocol types of packets from different communication channels and implementing protocol analysis is fundamental to data acquisition for edge computing in power Internet of Things. Such studies have so far been largely restricted to specified type of protocol, because only when the type is known can the protocol be parsed. Targeting more protocol types requires methods that can automatically recognize various types of protocols with both high recognition rate and high efficiency. To be suitable for edge computing in power Internet of Things, this paper proposes a light-weight deep neural network model named PPRNet trained with data frames from protocols of different types. The experimental results show that the technique is very effective on sensing 6 protocol types applied in power IoT containing DL/T645-2007, Q/GDW1376.1, and IEC62056. This model allows us to sense different types of protocol when previously unknown data frames entering in a system, and overcome the dilemma of economically deploying an artificial intelligence system to an edge device. This method has the ability of learning and expanding, can be extended to identify and apply other application protocols. It may ultimately help speed up the recognition of protocol types, and promotes the interconnection and interoperation of access devices and the plug-and-play.
Modern, Decentralized Blockchain-Based Solutions for Saving Video Footage

The decentralized, cloud-based data storage spaces provide greater data security compared to centralized cloud-based data storage spaces, as the data is not kept at extensive giant servers, like at Amazon, Google and Microsoft, instead, the data is distributed and mirrored with blockchain-based nodes. Any footage from the security cameras is classified as sensitive data, prone to be exposed to numerous risks of danger and threats, so their secure storage has nowadays become an essential asset. In the case of video footages, the high resolution like Full HD, and 4K are at disposal, and 8K will be reachable soon. The higher resolution results in greater data quantity. The decentralized, cloud-based storage spaces may be declared as a safe solution; those seem to be a good choice. On the other hand, their drawback is that they are incapable of storing greater data quantities. Hence, the present article seeks answers to the question, if there is a blockchain-based solution nonetheless, which would be suitable for the safe storage of video footage.
SciNER: A Novel Scientific Named Entity Recognizing Framework

There is an increasing number of scientific publications produced by the booming science community. It is very important for automatic scientific analysis to extract entities such as tasks and methods from unstructured scientific publications. At present, the span-based methods are the best way for scientific NER tasks, which usually generate a few entities by searching hundreds of candidate spans in a sentence. However, these existing methods have a few drawbacks. Firstly, the span extractor obtains more negative samples than positive samples, and thus it makes the input extremely imbalance. Secondly, the pruner has no predictive ability at the beginning of the joint training process in an end-to-end model. To tackle the above problem, in this paper, we propose a novel scientific named entity recognizing pipeline framework, called SciNER. Specifically, in the first stage, there is a pruner to filter out most illegal entities. The span extractor in the pruner performs under-sampling to balance the positive and negative samples. In the second stage, the entity recognizer is trained by the pruned spans. Extensive experiments demonstrate that SciNER outperforms state-of-the-art baselines on several datasets in both computer science and biomedical domains (Code is available at: https://github.com/ethan-yt/sciner).
A Text Emotion Analysis Method Using the Dual-Channel Convolution Neural Network in Social Networks

In order to solve the problem that the existing deep learning method has insufficient ability in feature extraction in the text emotion classification task, this paper proposes a text emotion analysis using the dual-channel convolution neural network in the social network. First, a double-channel convolutional neural network is constructed. Combined with emotion words, parts of speech, degree adverbs, negative words, punctuation, and other word features that affect the text’s emotional tendency, an extended text feature is formed. Then, using the CNN’s multichannel mechanism, the extended text features based on the word vector features and the semantic features based on the word vectors are, respectively, input into the CNN model. After each convolution operation of the convolution channel, the BN technology is used to normalize the internal data of the network and the padding technology is used to improve the ability of the model to extract edge features of the data and the speed of the model. Finally, a dynamic k-max continuous pooling strategy is adopted to realize the dimensionality reduction of features and enhance the model’s ability to extract features. The experimental results show that the accuracy and F1 values obtained by the proposed method can be as high as 94.16% and 92.61%, respectively, which are better than several comparison algorithms.
Reporting Strategy for VR Design Reviews

Design reviews are an established component of the product development process. Especially, virtual reality design reviews (VRDRs) can generate valuable feedback on the user’s perception of a virtual product. However, the user’s perception is subjective and contextual. Since there is a lack of strategies for recording, reproducing VRDRs is an intricate task. User feedback is therefore often fleeting. This makes deriving meaningful results from VRDRs a diffuse task.In this paper, we present a strategy for recording VRDRs in structured and coherent reports. We suggest dividing all involved information into structural and process information. While the former describes the content and functionality of the VRDR, the latter represents occurring events.The strategy provides means to store collections of events in the context they occurred in. Additional properties such as timestamps, involved users and tags are provided to create comprehensive VRDR reports. By storing the report in a database, sorting and filtering algorithms can be applied to support efficient data evaluation subsequent to the VRDR. Thus, such reports can be used as a basis for reproducible VRDRs.
Empirical Study on Intelligent Formative Evaluation of Text Guiding Performance Assisted by Java System and Data Mining

Empirical study on intelligent formative evaluation of English guiding performance assisted by Java system and data mining is conducted in this paper. In MDD, the application is abstracted into the high-level model. Through the interpretation or execution of the model, the abstract model can be then automatically converted into executable software code. Based on this technique background, we introduce the intelligent data mining model to optimize the traditional pipeline. The test on the massive data sets have proven the effectiveness. For the scenario demonstration, we apply the proposed model into the intelligent formative evaluation of English guiding performance with J2EE.
Matching Face Images for Biometric Authentication

The use of biometric features, to authenticate users of different applications, is growing rapidly in recent years, according to the high sensitivity of the protected information and the good security that biometric authentication provides. In this study, a method is proposed to measure the similarity between two face images, using convolutional neural networks, directly from the raw pixel values of the input image. As the neural network is used for matching instead of classifications, modifying the users that the proposed method can recognize is a matter of adding or removing model images of the user's faces, without the need to retrain the model. The similarity between the face image and every model image is measured in order to select the user with the highest similarity to the input image as the recognized user, where that similarity measure is compared to a threshold value in order to authenticate that user. The evaluation results of the proposed method, using ORL shows that the proposed method has 99.48% accuracy with 0.22% False Acceptance Rate (FAR) and 3.2% False Rejection Rate (FRR). Hence, the proposed method has been able to maintain high accuracy while eliminating the vulnerabilities of biometric authentication systems imposed by the use of separate stages for features extraction and similarity measurement.
Performance Analysis of Vehicle Detection Algorithm in Aerial Traffic Videos

This paper aims at the problem of vehicle detection in UAV aerial videos and tries to investigate the performance of different kinds of object detection algorithms under such a condition. We chose ViBe, HOG+SVM, Faster R-CNN and YOLOv3 as the typical detection algorithms. After applying them on UAV aerial videos with different height and traffic scenes and testing their precision and recall, we make a theoretical analysis of their performance. Our experiments showed that Faster R-CNN is the best method. Meanwhile, ViBe, with the advantages of less calculation and relatively low hardware requirements, is a good choice for a simple scene where the vehicles keep moving all the time.
Combining Knowledge Graph Embedding and Network Embedding for Detecting Similar Mobile Applications

With the popularity of mobile devices, large amounts of mobile applications (a.k.a.“app”) have been developed and published. Detecting similar apps from a large pool of apps is a fundamental and important task because it has many benefits for various purposes. There exist several works that try to combine different metadata of apps for measuring the similarity between apps. However, few of them pay attention to the roles of this service. Besides, existing methods do not distinguish the characters of contents in the metadata. Therefore, it is hard to obtain accurate semantic representations of apps and capture their fine-grained correlations. In this paper, we propose a novel framework by knowledge graph (KG) techniques and a hybrid embedding strategy to fill above gaps. For the construction of KG, we design a lightweight ontology tailored for the service of cybersecurity analysts. Benefited from a defined schema, more linkages can be shared among apps. To detect similar apps, we divide the relations in KG into structured and unstructured ones according to their related content. Then, TextRank algorithm is employed to extract important tokens from unstructured texts and transform them into structured triples. In this way, the representations of apps in our framework can be iteratively learned by combining KG embedding methods and network embedding models for improving the performance of similar apps detection. Preliminary results indicate the effectiveness of our method comparing to existing models in terms of reciprocal ranking and minimum ranking.
A Study of Big Data Security on a Partitional Clustering Algorithm with Perturbation Technique

Partitional Clustering Algorithm (PCA) on the Hadoop Distributed File System is to perform big data securities using the Perturbation Technique is the main idea of the proposed work. There are numerous clustering methods available that are used to categorize the information from the big data. PCA discovers the cluster based on the initial partition of the data. In this approach, it is possible to develop a security safeguarding of data that is impoverished to allow the calculations and communication. The performances were analyzed on Health Care database under the studies of various parameters like precision, accuracy, and F-score measure. The outcome of the results is to demonstrate that this method is used to decrease the complication in preserving privacy and better accuracy than that of the existing techniques.