Reviewerly/exHarmony huggingface dataset:
Innovative lightweight encryption algorithm for real-time video

The issues of security and privacy in multimedia data have become of most extreme significance particularly in the applications associated to medical and military sectors. In this paper, an innovative encryption algorithm for real time videos is proposed. The proposed algorithm consists of two phas es in both sender and receiver sides. In these two phases, the tasks of compression/encryption and decompression /decryption are performed at sender and receiver sides respectively. The proposal uses H.261 codec in compression/decompression processes and chaotic logistic map system in encryption/decryption processes for private – key generation. The suggested algorithm takes the features of H.261 codec into consideration to achieve high security with less processing time by encrypt the produced intra frame (I frame) and leaving inter-frame (P-frame) without any changing. Proposed algorithm has innovated new strategy in selecting bytes for encryption by encrypt half of bytes in each I-frame or all of them. Most common statistical tests were performed on the obtained results from applying the proposed algorithm and the results were promising in terms of security level and required time for performing two states.
Local Forum as a Knowledge Source

An internet forum can be used as a crowdsourcing tool for searching for new knowledge and the latest non-standard ideas being a result of forum members' joint creative work. On the basis of these regularities, criteria of the forum maturity in processes of its formation and advance are elaborated. Basing on these results on a 2015 analysis of the forum, formed on the ground of the Universidad Tecnologica de la Mixteca (UTM), the State of Oaxaca, Mexico, a moment when the forum reaches its maturity was revealed. These conceptions are suitable for forming a thesaurus and taxonomy prototype of socio-cultural, environmental, and economic problems in Mixteca region. In its turn, a forum thesaurus gives opportunities for the regional community to understand a degree of its involvement into revealing and discussing the most actual regional problems on the internet.
Executing a Real-Time Response in an Agile Information System

Agile information systems (AIS) is a current topic of interest in the IS industry. An AIS is defined as one that has ability to sense a change in real time, diagnose it in real time, and select and execute an action in real time. This study focuses on the properties or attributes of an AIS to execute an action in real time. The properties outlined in this research enable an AIS to select a response in real time and then execute a response in real time. The attributes are derived using industry literature, refined using interviews with industry practitioners and then verified for importance using a survey. From the exercise it is concluded that most properties or attributes are important for real-time execution in an AIS. Dimensions underlying these attributes are identified using EFA. Some recent frameworks and paradigms related to IS configurations that can respond to changes in real time are discussed. These frameworks incorporate many of the properties that were arrived for executing a change in real time in an agile IS and hence provide additional validation for the research.
Off-line Chinese Signature Verification Using Convolutional Neural Network

Background: The unique individual biological characteristics is used for identification in biometrics, which is safe and difficult to forge. Therefore, it can help to enhance the safety of access control system. Since the developing of modern information science and technology, computerbased signature verification system enables signature verification more efficiently and automatically in comparison with traditional human identification method. Methods: In order to improve the accuracy of Chinese signature verification, an off-line Chinese signature verification method based on deep convolutional neural network is proposed. First, the machine learning library Tensorflow is build, and the volunteers are invited to establish the offline Chinese signature dataset. Second, the dataset is pre-processed, including denoising, binarization and size normalization. Finally, three different CNN architectures (AlexNet, GoogleNet, VGGNet) are adopted to implement the signature verification. Results: Experimental results show that the performance of AlexNet is better than that of the other two convolutional neural network architectures, the accuracy of classification has been up to 99.77%, and verification rate is 87.5%. Conclusion: Compared with the traditional offline Chinese signature recognition method, the method based on the convolutional neural network Alex Net-f is better than other methods to some extent, and avoids the complicated feature engineering.
Tell You a Definite Answer: Whether Your Data is Tainted During Thread Scheduling

With the advent of multicore processors, there is a great need to write parallel programs to take advantage of parallel computing resources. However, due to the nondeterminism of parallel execution, the malware behaviors sensitive to thread scheduling are extremely difficult to detect. Dynamic taint analysis is widely used in security problems. By serializing a multithreaded execution and then propagating taint tags along the serialized schedule, existing dynamic taint analysis techniques lead to under-tainting with respect to other possible interleavings under the same input. In this paper, we propose an approach called DSTAM that integrates symbolic analysis and guided execution to systematically detect tainted instances on all possible executions under a given input. Symbolic analysis infers alternative interleavings of an executed trace that cover new tainted instances, and computes thread schedules that guide future executions. Guided execution explores new execution traces that drive future symbolic analysis. We have implemented a prototype as part of an educational tool that teaches secure C programming, where accuracy is more critical than efficiency. To the best of our knowledge, DSTAM is the first algorithm that addresses the challenge of taint analysis for multithreaded program under fixed inputs.
Visibility study in strategizing for web marketing and webometric university ranking in malaysia

The increasing of universities to compete in the higher education institution has gone global as knowledge is growing rapidly in the internet. Webometric has introduced a ranking system for the universities to analyze their website on the content of the universities' website according to their indicator of presence, visibility, openness and scholars. The amount of performed universities in Malaysia is microscopic compare to most universities in the U.S. The study analyzes a selection of universities from webometric in accessing the website data and traces the relationship between the indicators and the ranking. The present study contributes to the enhancement of University performance through a detail identification and strategy using Webometric dimensions.
Information Structure Parsing for Chinese Legal Texts

Information processing is one of the main concerns in the field of artificial intelligence, because it can benefit many related downstream tasks. To facilitate information processing, information structure parsing is assumed to be of great significance. This article proposes a discourse analysis based approach so that information structure of Chinese legal texts can be recognized automatically. This article employs Discourse Information Theory to explore information features of Chinese legal texts. The texts used in this study include 6 types, each type containing 60 training texts and 30 testing texts. After that, a set of rules is formulated to classify legal texts and identify the categories of information units. Finally, to examine the performance of the rules, a comparison is made by designing a Support Vector Machine classifier and a Viterbi algorithm decoder. The experiment demonstrates that the rule based approach outperforms the statistics based approaches. This research suggests that discourse analysis may provide some linguistic features conducive to discourse parsing.
DCF with high‐speed spatial constraint

Spatially regularised discriminative correlation filters (SRDCFs) introduce spatial regularisation weights to mitigate the boundary effects caused by circular convolution which obtains superior performance. However, spatial regularisation is computationally expensive; this limits the real-time performance of SRDCF. This study proposes high-speed spatial constraint to DCFs (HSCDCFs) for tracking. Using a large area of the sample to learn a CF, then, the authors introduce the spatial constraint to penalise CF coefficients. Their method formulation allows the CFs to efficiently learn a mass of negative samples and high-quality positive samples. They perform experiments on two benchmark datasets: OTB-2013 and OTB-2015. Compared to SRDCF, they provide a slightly reduce of 2.7 and 3.1%, respectively, in mean overlap precision, their method obtains the real-time speed of 62.5 fps which is ten times faster than SRDCF.
On Soft-Information-Based Error and Erasure Decoding of Reed–Solomon Codes in Burst Rayleigh Fading Channels

In this paper, two new decoding algorithms to decode Reed-Solomon codes during transmission over burst Rayleigh fading channels with additive white Gaussian noise (AWGN) are proposed. They only conduct error correction for coded symbols located in the pure AWGN region and conduct error and erasure correction for those symbols located in the burst fading region by treating those coded symbols that are very likely erroneous as erasures. The first algorithm does not need to know the fading locations in advance, while the second algorithm assumes that the fading locations are known. In addition, the performance of such two algorithms is studied when a pre-computed threshold is used to determine the erasures of the code. Simulation results show that our proposed algorithms not only significantly perform better than the classic Berlekamp-Messay algorithm with a comparable computational complexity but also achieve a better tradeoff between the performance and the computational complexity when compared with other existing algorithms. In particular, our algorithms exhibit excellent robustness for tested various code parameters and fading configurations. Furthermore, a more detailed mathematical analysis is also developed in this paper in order to estimate the performance of the new algorithms in the burst Rayleigh fading channels. We observe that the performance of the first algorithm can only be estimated relatively accurately when encountering burst deep-fading, whereas the performance prediction for the second algorithm is always in agreement with the simulation results for various fading cases.
Digital Content Consumption Trends in the Website of Singapore's National Information Literacy Programme

The objective of this article is to utilise web statistics to analyse the consumption patterns of digital content - such as blog articles, videos, online learning courses, infographics and cheat sheets - found on the official website of Singapore's National Information Literacy Programme, with a view of informing forward planning. The secondary objective would be to gauge the effectiveness of the SURE Facebook page in marketing the content of the SURE portal.
PPFilter: Provider Privacy-Aware Encrypted Filtering System

Filtering refers to an operation to determine whether the concerned data should be accepted and transferred, or be blocked and marked as a malicious traffic flow. It mitigates the inter-domain bandwidth overhead, local computational cost and storage cost for data identification. In many sensitive applications, the identity of the data provider needs to be hidden. This creates challenges how to filter the transmitted data packet with an encrypted form. It is non-trivial to hide this data provider's identity while enabling filtering, as the policy used as a matching criteria will need to determine whether the data needs to be transferred or not without knowing the origin of that data. In this work, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPFilter</i> , a privacy-aware encrypted filtering mechanism which allows the filtering to be conducted without the need to know the identity of the data provider. PPFilter achieves the integrity protection of the data packets and the provider privacy Level 3. PPFilter is built on top of a novel notion called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">identity-based encryption with sender search (IESS)</i> , which supports anonymous sender identity in an encrypted searching. We present a provably secure IESS instantiation, and apply it to achieve a PPFilter protocol. PPFilter allows the data provider's identity to be hidden from both the transferred data and policy while enabling the filtering capability, which solves the aforementioned problem. The analysis and evaluation show that PPFilter maintains cost-reasonable filtering while preserving provider privacy, and hence it guarantees its practicality.
Plan Ceibal as Where Technology Accelerates Pedagogy

This chapter describes how the government of Uruguay believes that all children have the right to have technology at their fingertips and that all children have the right to connectivity and access to the internet, that it is as important to have electricity and running water as to have access to the internet, and that it would have a high impact on the country's technological deployment and, obviously, on education and teaching. The parts of the chapter are concerned with the technology and pedagogy relationship: how to improve pedagogy through technology, the importance of teaching English and math online with help of education inspectors and the teachers using modern platform—virtual classrooms, books in digital format, digital technology laboratories—that allows collaborative work, work on projects, logical thinking, an online assessment system. All these integrated tools transfer to the biggest investments which the author calls “global learning network.”
Exact quantum query complexity of weight decision problems via Chebyshev polynomials

The weight decision problem, which requires to determine the Hamming weight of a given binary string, is a natural and important problem, with applications in cryptanalysis, coding theory, fault-tolerant circuit design and so on. In particular, both Deutsch-Jozsa problem and Grover search problem can be interpreted as special cases of weight decision problems. In this work, we investigate the exact quantum query complexity of weight decision problems, where the quantum algorithm must always output the correct answer. More specifically we consider a partial Boolean function which distinguishes whether the Hamming weight of the length-$n$ input is $k$ or it is $l$. Our contribution includes both upper bounds and lower bounds for the precise number of queries. Furthermore, for most choices of $(\frac{k}{n},\frac{l}{n})$ and sufficiently large $n$, the gap between our upper and lower bounds is no more than one. To get the results, we first build the connection between Chebyshev polynomials and our problem, then determine all the boundary cases of $(\frac{k}{n},\frac{l}{n})$ with matching upper and lower bounds, and finally we generalize to other cases via a new \emph{quantum padding} technique. This quantum padding technique can be of independent interest in designing other quantum algorithms.
Adaptive locality-constrained regularized robust coding for image recognition

We propose a novel adaptive locality-constrained regularized robust coding (ALRRC) for image recognition by considering both the effects of the test sample's features and the local relationships between the test sample and the training samples in the linear coding procedure. By adaptively calculating out a feature weight matrix of a test sample, ALRRC measures the effects of the test sample's features in the linear coding procedure. Furthermore, ALRRC can obtain the weighted test sample by multiplying the test sample by the feature weight matrix, which has reduced the roles of the aberrant features as far as possible. Similarly, ALRRC can get the weighted training samples by multiplying the training samples by the feature weight matrix. Moreover, using the similarities between the weighted test sample and weighted training samples, ALRRC adaptively calculates out a locality-constrained matrix that can truly characterize the local relationships between the test sample and the training samples. Finally, by incorporating the feature weight matrix and locality-constrained matrix into the linear coding framework, ALRRC is inclined to select the true local training samples to represent the test sample. In addition, we also propose an iterative algorithm to solve the optimization problem of ALRRC. Experimental results on several image databases show that ALRRC is more effective and robust than state-of-the-art linear coding-based methods.
Scene Invariant Virtual Gates Using DNNs

Understanding where people are located and how they are moving about in an environment is critical for operators of large public spaces such as shopping centers, and large public infrastructures such as airports. Automated analysis of CCTV footage is increasingly being used to address this need through techniques that can count crowd sizes, estimate their density, and estimate the through-put of people into and/or out of a choke-point. A limitation of using CCTV based approaches, however, is the need to train models specific to each view which, for large environments with 100s or 1000s of cameras, can quickly become problematic. While there is some success in developing scene-invariant crowd counting and crowd density estimation approaches, much less attention has been given to developing scene-invariant solutions for through-put estimation. In this paper, we investigate the use of convolutional neural network and long short-term memory architectures to estimate pedestrian through-put from arbitrary CCTV viewpoints. To properly develop and demonstrate our approach, we present a new 22 view database featuring 44 h of pedestrian throughput annotation, containing over 11 000 annotated people; and using this proposed approach we show that we are able to outperform a scene-dependant approach across a diverse set of challenging view-points.
Polynomial expressions for non-binomial structures

Following recent paper of the author about polynomial expressions with respect to binomial ideals, the current paper is devoted to the non-binomial case. Beside the established facts in the underlying theory, the correctness and termination of the algorithms are addressed. From the collection of examples, the main large-scale non-binomial one is of particular importance. Application of the new algorithms for the cases of moderately large binomial like cyclic 16-roots and cyclic 18-roots is discussed. A combinatorial analysis for complexity of the proposed algorithms is shown to be at least O ( log ⁡ ( log ⁡ n ) n 2 ( log ⁡ n ) 2 ) .
A Survey on Chaos Based Encryption Technique

Information security is an important field among the pervasive use of applications namely internet banking, mobile services, emails, viz., chaos-based encryption techniques play an important role in many security processes, namely: military systems, robotics, and other real time computing services. The secure transmission of audio, image and video are processed with unique characteristic of a third-party which makes the encryption and decryption highly secure for the users. In this chapter, a detailed survey on the various chaos-based encryption techniques is discussed and analyzed.
Learning Abstract Planning Domains and Mappings to Real World Perceptions

Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed, and is not part of the planning domain. Consequently, the focus is on learning the transitions between states. Current approaches address neither the problem of learning new states of the planning domain, nor the problem of representing and updating the mapping between the real world perceptions and the states. In this paper, we drop such assumptions. We provide a formal framework in which (i) the agent can learn dynamically new states of the planning domain; (ii) the mapping between abstract states and the perception from the real world, represented by continuous variables, is part of the planning domain; (iii) such mapping is learned and updated along the “life” of the agent. We define and develop an algorithm that interleaves planning, acting, and learning. We provide a first experimental evaluation that shows how this novel framework can effectively learn coherent abstract planning models.
Simultaneous variable selection and class fusion with penalized distance criterion based classifiers

Two new methods are proposed to solve the problem of constructing multiclass classifiers, selecting important variables for classification and determining corresponding discriminative variables for each pair of classes simultaneously in the high-dimensional setting. Different from existing methods, which are based on the separate estimation of the precision matrix and mean vectors, the proposed methods construct classifiers by estimating products of the precision matrix and mean vectors or all discriminant directions directly with appropriate penalties. This leads to the use of the distance criterion instead of the log-likelihood used in the existing literature. The proposed methods can not only consistently select important variables for classification but also consistently determine corresponding discriminative variables for each pair of classes. For the multiclass classification problem, conditional misclassification error rates of classifiers constructed by the proposed methods converge to the misclassification error rate of the Bayes rule in probability and rates of convergence are also obtained. Finally, simulations and the real data analysis well demonstrate good performances of the proposed methods in comparison with existing methods.
Analysis of laser M2 determination in a quadriwave lateral shearing interferometer with CCD vertical blooming

Charge-coupled device (CCD) vertical blooming is inevitable in silicon-based cameras when near-infrared lasers are measured. In this study, we analyze the mechanism responsible for the influence of CCD vertical blooming on the beam quality factor (M2) measurement with a quadriwave lateral shearing interferometer (QWLSI). Based on a CCD vertical blooming model, we reconstruct the bloomed interferogram, which is used to retrieve the intensity and phase distributions of the laser. Then, the relationship between the M2 and degree of CCD vertical blooming is revealed. In addition, the M2 of single- and multi-mode lasers is measured with the QWLSI under different degrees of vertical blooming streaks. The results from the experiment are reasonably consistent with the simulation results. Meanwhile, the method of improving the exposure time of CCD camera for suppressing the blooming is also analyzed, indicating that the method can effectively suppress the influence of the blooming on the M2 measurement in a QWLSI.