Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review, some interesting insights have been found about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of “source-code plagiarism detection in academia.”

Source-code Similarity Detection and Detection Tools Used in Academia: A Systematic Review

Techniques for optimizing data storage are disclosed herein. In particular, methods and systems for implementing redundancy encoding schemes with data storage systems are described. The redundancy encoding schemes may be scheduled according to system and data characteristics. The schemes may span multiple tiers or layers of a storage system. The schemes may be generated, for example, in accordance with a transaction rate requirement, a data durability requirement or in the context of the age of the stored data. The schemes may be designed to rectify entropy-related effects upon data storage. The schemes may include one or more erasure codes or erasure coding schemes. Additionally, methods and systems for improving and/or accounting for failure correlation of various components of the storage system, including that of storage devices such as hard disk drives, are described.

Layered redundancy encoding schemes for data storage

Despite being one of the most basic tasks in software development, debugging is still performed in a mostly manual way, leading to high cost and low performance. To address this problem, researchers have studied promising approaches, such as Spectrum-based Fault Localization (SFL) techniques, which pinpoint program elements more likely to contain faults. This survey discusses the state-of-the-art of SFL, including the different techniques that have been proposed, the type and number of faults they address, the types of spectra they use, the programs they utilize in their validation, the testing data that support them, and their use at industrial settings. Notwithstanding the advances, there are still challenges for the industry to adopt these techniques, which we analyze in this paper. SFL techniques should propose new ways to generate reduced sets of suspicious entities, combine different spectra to fine-tune the fault localization ability, use strategies to collect fine-grained coverage levels from suspicious coarser levels for balancing execution costs and output precision, and propose new techniques to cope with multiple-fault programs. Moreover, additional user studies are needed to understand better how SFL techniques can be used in practice. We conclude by presenting a concept map about topics and challenges for future research in SFL.

Spectrum-based Software Fault Localization: A Survey of Techniques, Advances, and Challenges.

In the current centralized IoT ecosystems, all financial transactions are routed through IoT platform providers. The security and privacy issues are inevitable with an untrusted or compromised IoT platform provider. To address these issues, we propose Hy-Bridge, a hybrid blockchain-based billing and charging framework. In Hy-Bridge, the IoT platform provider plays no proxy role, and IoT users can securely and efficiently share a credit with other users. The trustful end-to-end functionality of blockchain helps us to provide accountability and reliability features in IoT transactions. Furthermore, with the blockchain-distributed consensus, we provide a credit-sharing feature for IoT users in the energy and utility market. To provide this feature, we introduce a local block framework for service management in the credit-sharing group. To preserve the IoT users' privacy and avoid any information leakage to the main blockchain, an interconnection position, called bridge, is introduced to isolate IoT users' peer-to-peer transactions and link the main blockchain to its subnetwork blockchain(s) in a hybrid model. To this end, a k-anonymity protection is performed on the bridge. To evaluate the performance of the introduced hybrid blockchain-based billing and charging, we simulated the energy use case scenario using Hy-Bridge. Our simulation results show that Hy-Bridge could protect user privacy with an acceptable level of information loss and CPU and memory usage.

Hy-Bridge: A Hybrid Blockchain for Privacy-Preserving and Trustful Energy Transactions in Internet-of-Things Platforms.

Even though there are various security systems consuming large power are available in market nowadays, robbery rate is very high. We are proposing a novel system to prevent robbery in highly secure areas with lesser power consumption. T his system has face-recognition technology which grants access to only authorized people to enter that area. If others enter the place without access using some other means, then the system alerts the security personnel and streams the video captured by th e security camera. The facerecognition is done using PCA algorithm. The video transmitted is compressed and transmitted by ENVMT. By using this ENVMT, the video can play with lesser bandwidth consumption, latency and jitter.

/pdf/security-system-w-ith-face-recognition-sms-alert-and-2q2yoibf4n.pdf

Security system w ith face recognition , sms alert and embedded network video m onitoring terminal

Research on CBK(L,K)-Anonymity Algorithm

Diggitalization of network video monitoring system, which has advantages of unlimited distance control, flexible extension and so on, is becoming a new standard of security system. Due to limitations of network bandwidth, video signals collected must be compressed to achieve real-time video transmission on the network. Therefore, how to maintain the high quality of the compressed signal, and meanwhile minimize the amount of data becomes a key technical problem To achieve this goal, an embedded network video monitoring terminal was designed and implemented by us This terminal consists of dedicated MPEG-4 compression ASIC, 32-bit embedded processor, 8-bit micro controller, 4-channel video capturing and decoding chips etc. Its corresponding software has been developed Users can not only watch the real-time monitoring images directly with the built-in embedded web server through the network,but also login central server, watch the monitoring images and control the network video terminal through dedicated software. Test results show that our terminal can better utilize TCP/IP network bandwidth Video can play without latency and fitter, as a result of real-time transmission and reception of digital video signal.

Design and implementation of embedded network video monitoring terminal

An Improved RSLK-Anonymity Algorithm for Privacy Protection of Data Stream

Studies have shown that many students have similar programs in programming class, most of which due to plagiarism. Students may simply modify others' programs as their own. This makes the assessment standards for students' programs with lots of ambiguity and uncertainty, limiting assessment accuracy and efficiency, and reducing the reliability of test results. To solve this problem, a student program plagiarism detection approach is proposed based on a data mining algorithm. Firstly, similar code fragments are mined by the CloSpan algorithm. Then, similarities between programs are calculated. Finally, the plagiarism list is output. Experiments showed that compared with the widely used plagiarism detection tool MOSS, our approach is can not only more accurately give statistical information of the similar program detected, but also be able to visualize the similar code fragments, which can greatly increase detection efficiency.

Detection of plagiarism in students' programs using a data mining algorithm

Software errors can potentially lead to disastrous consequences. Unfortunately, debugging software errors can be difficult and time-consuming. A comprehension oriented software fault location approach (COFL) is proposed in this paper to provide automated assistance in bug location. It not only locates program predicates predicting bugs, but also provides high efficiency demand-driven data flow and control flow analysis to help developers understand the causes and contexts of bugs.

http://ieeexplore.ieee.org/iel5/6175418/6181892/06181971.pdf

Wang Kechao

Papers

Research on CBK(L,K)-Anonymity Algorithm

Design and implementation of embedded network video monitoring terminal

An Improved RSLK-Anonymity Algorithm for Privacy Protection of Data Stream

Detection of plagiarism in students' programs using a data mining algorithm

Comprehension oriented software fault location