Reviewerly/exHarmony huggingface dataset:
Research on Application Layer Security Communication Protocol Based on Lightweight NTRU Public Key Cryptography

The development of mobile Internet technology has made intelligent terminal devices and mobile applications popular, and computing at mobile devices is becoming more and more popular in people's daily lives. However, due to the processing speed and storage capacity limitations of mobile terminal devices, their security has become a major bottleneck in the development of mobile computing. Therefore, security research for mobile platforms is of great significance. The cryptosystem is an important part of realizing the anonymous security of mobile Internet information transmission. In general, the cryptosystem includes a symmetric cryptosystem and a public-key cryptosystem, both of which have advantages and disadvantages. The symmetric cryptosystem algorithm is fast, but the encryption and decryption keys of the two sides of the communication are the same, which hinders the security implementation of the key management security core key negotiation, and cannot be applied with a digital signature. The public key cryptosystem can separate the encryption key from the decryption key, but its operation speed is slow and limited by the mobile hardware platform. The paper proposes communication encryption and signature algorithm for the mobile terminal, which uses the public key cryptography algorithm. Based on ensuring the same security as the traditional public key cryptography algorithm, the computational overhead is reduced, the running speed is increased, and the storage requirement is reduced.
Assessing the Awareness of Cybersecurity Within Entrepreneurship Students: The Cyberpreneurship Project

This study addresses an important gap in both academia and practice related to the lack of a shared language and sometimes conflicting priorities between entrepreneurs and cybersecurity experts. Entrepreneurs are often pressed to create and launch products and services as quickly as possible to achieve a first-mover advantage in the market. In doing so, they tend to overlook cybersecurity threats and risks due to a lack of awareness and insufficient funding. This can lead to theft of intellectual property, project failure, and inaccurate risk assessment. In addition, cybersecurity students who study risk management often struggle to analyze real client issues due to the sensitive nature of the data and assets in most organizations. The Cyberpreneurship Project outlined within this research was designed to tackle to this problem by engaging cybersecurity students in a risk assessment of start-up businesses developed by entrepreneurship graduate students. The project is an interdisciplinary exploratory study developed to enhance awareness of cybersecurity risks and experiential opportunities among both entrepreneurship and cybersecurity students. The success of the project is assessed through a mixed-methods approach. The study also provides the academic community with valuable tools and resources to establish similar educational models at their own institutions.
Fast Adaptive Reparametrization (FAR) With Application to Human Action Recognition

In this letter, a fast approach for curve reparametrization, called Fast Adaptive Reparamterization (FAR), is introduced. Instead of computing an optimal matching between two curves such as Dynamic Time Warping (DTW) and elastic distance-based approaches, our method is applied to each curve independently, leading to linear computational complexity. It is based on a simple replacement of the curve parameter by a variable invariant under specific variations of reparametrization. The choice of this variable is heuristically made according to the application of interest. In addition to being fast, the proposed reparametrization can be applied not only to curves observed in Euclidean spaces but also to feature curves living in Riemannian spaces. To validate our approach, we apply it to the scenario of human action recognition using curves living in the Riemannian product Special Euclidean space <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(3)^n$</tex-math></inline-formula> . The obtained results on three benchmarks for human action recognition (MSRAction3D, Florence3D, and UTKinect) show that our approach competes with state-of-the-art methods in terms of accuracy and computational cost.
Long bone fracture type classification for limited number of CT data with deep learning

High-energy collisions and sports injuries may result in long bone fractures. To treat and manage fracture patients, it is necessary for trauma surgeons and physicians to first classify their fracture type because their treatment is different according to their type. Facture classification demands high level of expertise and careful examination. To assist the fracture treatment in medical practice, this paper proposes a convolutional neural network-based classification method to classify lower long bone fractures in which output class label can be multiple, data size is small compared to the number of class labels, and classes in the data set are imbalanced. It presents how to organize the deep network model, how to prepare the data, how to augment data, how to determine the classes from the network output, and how to evaluate the performance of model. It also explains the characteristics of the computed tomography (CT) image data. In the experiments, the proposed method showed 80.6% precision and 92.0% recall for a data set of CT fracture images.
End-to-end DeepNCC framework for robust visual tracking

In this paper, we propose an NCC-based object tracking deep framework, which can be well initialized with the limited target samples in the first frame. The proposed framework contains a pretrained model, online feature fine-tuning layers and tracking processes. The pretrained model provides rich feature representations while online feature fine-tuning layers select discriminative and generic features for the tracked object. We choose normalized cross-correlation as a template tracking layer to perform the tracking process. To enable the learned features representation closely coordinated to the tracked target, we jointly train the feature representation network and tracking processes. In online tracking, an adaptive template and a fixed template are fused to find the optimal tracking results. Scale estimation and a high-confidence model update scheme are perfectly integrated into the framework to adapt to the target appearance changes. The extensive experiments demonstrate that the proposed tracker achieves superior performance compared with other state-of-the-art trackers.
Hardware Implementation of ECIES Protocol on Security SoC

This paper describes a hardware implementation of ECIES (Elliptic Curve Integrated Encryption Scheme) protocol using a security SoC (System on Chip). The security SoC based on Cortex-M0 microcontroller integrates four cryptographic cores including ECC (Elliptic Curve Cryptography) core, SHA3 (Secure Hash Algorithm 3) core, UAAP (United ARIA and AES Processor) core, and TRNG (True Random Number Generator) core, which are connected to AHB-Lite. The ECC core is used for public key generation and key agreement function, and the SHA3 core is used for key derivation function. In addition, the block cipher AES is used for encryption/decryption and CBC-MAC. The security SoC was implemented in FPGA device, and the ECIES protocol was validated by hardware-software co-verification.
Efficient Pseudo-chaotic Number Generator for Cryptographic Applications

A secure and efficient pseudo-random number generator is crucial for the security of a stream cipher or block cipher in a cryptosystem.This paper is devoted to the design and analysis of a new robust Pseudo-Chaotic Number Generator (PCNG) based on discrete chaotic maps over a finite field, and a multiplexing mechanism.The proposed PCNG contains three discrete chaotic maps: Piece-Wise Linear Chaotic Map (PWLCM), skew tent and logistic map.XOR operators are applied on these maps to form three optional intermediate outputs that are selected by the multiplexing mechanism to produce the final pseudo-chaotic sequence.The XOR operators and multiplexing mechanism can increase the dynamic pseudo-chaotic properties significantly.The PCNG is designed over a finite 2 32 field, has a simple structure and can be easily implemented.It has been implemented on a new stream cipher.Security analyses and statistical experiments have been carried out and the results have proven that the proposed PCNG can generate effective pseudorandom numbers and the stream cipher exhibits good security properties with large secret key space and high key sensitivity.Therefore, the proposed PCNG can be successfully applied in cryptosystems.
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism

In this paper we propose a new cure rate survival model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes which may be responsible for the occurrence of the event of interest is assumed to follow a geometric distribu tion while the time to event is assumed to follow a Birnbaum-Saunders distribution. As an advantage our approach may scan all underlying activation mechanisms from the first to last one based on order statistics. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given. In particular, case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which includes the Kullback-Leibler (K-L), J-distance, L1 norm and χ2-square divergence measures as particular cases. Simulation studies are performed for study frequentist properties of the Bayesian estimates. The methodology is illustrated on a real malignant melanoma data.
Sequential Authentication Using Handwriting Biometrics for Free Response e-Testing

To prevent proxy-test taking among examinees in unsynchronized e-Testing, a previous work proposed an online handwriting authentication. That method was limited to applied for end of each answer. For free response tests that needed to authenticate throughout the answer, we used the Bayesian prior information to examine a sequential handwriting authentication procedure. The evaluation results indicate that the accuracy of this procedure is higher than the previous method in examinees authentication during mathematics exam with referring the Chinese character.
Image decomposition by bidimensional ensemble patch transform

This paper proposes a new algorithm for image decomposition, termed bidimensional ensemble patch transform that adopts a patch process and an ensemble process. The proposed method is based on a nontrivial two-dimensional extension of the one-dimensional ensemble patch transform. It is required to have a successful recognition of local characteristics of the image. The proposed method is capable of efficiently decomposing an image into two-dimensional intrinsic mode functions considering spatial patterns and yields decomposition results that are robust to random fluctuations of the image such as noises. Furthermore, the proposed method is computationally easy to implement.
An Unbiased Butterfly Optimization Algorithm

Several bio-inspired optimization techniques have been used to solve multidimensional multimodal optimization problems. Butterfly optimization algorithm (BOA) is a recent bio-inspired technique that emulates butterflies’ foraging which is based on their sense of smell to determine the location of nectar. BOA shows fast convergence compared to other optimization techniques such as particle swarm optimization (PSO), genetic algorithm (GA) and biogeography based optimization (BBO) when compared over several standard optimization problems. In this paper, the basic algorithm of BOA is demonstrated to be biased to search for optimal points near origin. This feature is the main cause of the fast convergence of BOA in standard test problems where the optimal points is at (or near) the origin. Without this biasing effect, BOA suffers from slow convergence and may not converge to the global minimum even for unimodal low dimensionality problem. The evidence of biasing is demonstrated, and an unbiased version of the BOA (UBOA) is proposed to alleviate this problem. UBOA is proved to outperform BOA in case of shifted functions. Test results using several benchmark problems demonstrate that UBOA provides a competitive optimization method with respect to other bio-inspired methods.
An Efficient Analysis of Machine Learning Algorithms in CBIR

It is very challenging task to retrieve accurate image from huge digital images due to which much interest has been gain by CBIR. Feature based methods are prove to be more efficient as compared to text based query methods. As in case of CBIR a similarity between already stored database images and query by user can be extracted through shape, texture, color and structure like low level primitive features. This paper gives the idea about use of image retrieval in various fields and gives the problem related to Image retrieval. Its main problem is the retrieval of user requested images and search from database. A typical CBIR system automatically extracts shape, spatial, texture and colors information like visual attributes of every image in database. These extracted pixel values are stored in a different database within the system is called a feature database. There is increase in number of CBIR systems that result in increase of classification of these systems. One way of classifying these systems are texture, shape, colour and most frequently used features for feature extraction phase of CBIR systems. In last we review various works done by researchers in CBIR classification using Convolution neural network, Support vector machine, Random forest, Decision tree and K-nearest neighbour. This helps user in identifying the use of best classifier according to user need.
Deep Learning Model for Function Point Based Software Cost Estimation -An Industry Case Study

Cost estimation of software engineering is a vital mission for software development organization. Accurate estimation can be valuable for both the developers and the customers. How to make estimation more accurate and reproducible is an important task to be settled in both research and industry area. This paper gives a review of the state of the art in the field of software cost estimation and proposes a Deep Neural Network(DNN) model to facilitate the usage of function point estimation. The results show that the function point based estimation can be a good choice in the early evaluation of the software budget while the deep neural network can significantly improve the labor productivity and the accuracy of the evaluation result .
Linear Complexity of New q-ary Generalized Cyclotomic Sequences of Period $$2p^n$$

In this paper, we construct new q-ary generalized cyclotomic sequences of length $$2p^n$$. We study the linear complexity of these sequences over the finite field of order q and show that they have high linear complexity when $$n\ge 2$$. These sequences are constructed by new generalized cyclotomic classes presented by Zeng et al.
What Ruined Your Cake: Impacts of Code Modifications on Bug Distribution

In the current decade, software systems have been more intensively employed in every aspect of our lives. However, it is disappointing that the quality of software is far from satisfactory. More importantly, the complexity and size of today's software systems are increasing dramatically, which means that the number of required modifications is also increasing exponentially. Therefore, it is necessary to understand how function-level modifications impact the distribution of software bugs. In addition, other factors such as a function's structural characteristics as well as attributes of functions themselves may also serve as informative indicators for software fault prediction. In this paper, we perform statistical methods and logistic regression to analyze the possible factors that are related to the distribution of software bugs. We demonstrate our study from the following five perspectives: 1) the distribution of bugs in time and space; 2) the distribution of function-level modifications in time and space; 3) the relationship between function-level modifications and functions' fault-proneness; 4) the relationship between functional attributes and functions' fault-proneness; and 5) the relationship between software structural characteristics and functions' fault-proneness.
Managing sessions of creative requirements elicitation and assessment

The competitiveness between organizations imposes the growing need to generate differentiated products and services that can provide more value to consumers, or unexpected satisfaction factors. For that, creativity plays an important role and can be better instigated with creativity-based elicitation techniques. However, how to manage a large number of stakeholders and other resources to take part in the elicitation and evaluation sessions? This paper presents an information system that supports this management and assists in eliciting and evaluating requirements using a combinatorial creativity technique. This tool is a configurable environment which implements an elicitation process line. Six case studies were conducted to assess the users experience and the tool's efficiency in supporting the gathering of innovative requirements. We argue the positive and negative points identified by the participants during its use.
An Efficient and Fast VLIW Compression Scheme for Stream Processor

Stream processor has been widely used in multimedia processing because of the high performance gained by parallelism. In order to achieve higher parallelism, the stream processor employs large width structure of VLIW (very long instruction word, VLIW) and multiple parallelizable instructions are organized into one VLIW. Because the width of VLIW is fixed, there are a large number of empty operations (non-operation, NOP) filled in VLIW, which results in serious code size expansion problem. Aiming at this issue, the horizontal code compression and vertical code compression methods are applied on the VLIW of stream processor respectively. First the VLIW is divided into several subfields according to the logic characteristics of VLIW instruction, then the horizontal code compression scheme which based on Huffman coding is applied on each subfield and this method can achieve approximately 78% code size reduction on average. However, the extra-long time required to decode the compressed VLIW before instruction execution may cause system performance penalty. In order to reduce the decompression time consumption, the vertical compression scheme is proposed. The vertical compression can reduce the code size nearly 70% by deleting the NOPs of VLIW in vertical direction. Furthermore the VLIW after vertical compression can be executed directly without decompression operation by using banked instruction memory. Specifically, the vertical compression can compress stream processor VLIW code size significantly and without any negative influence on performance.
Introducing H, an Institution-Based Formal Specification and Verification Language

This is a short survey on the development of the formal specification and verification language H with emphasis on the scientific part. H is a modern highly expressive language solidly based upon advanced mathematical theories such as the internalisation of Kripke semantics within institution theory.
Changes in Technology

This chapter accomplishes three primary goals. First, we review the history of technologies that have been influential in the world of work, from the abacus to innovations of the present day. We then identify eight characteristics that describe modern technologies: power, portability, usability, networking, encryption, ubiquity, immersion, and predictiveness. Finally, we present a SWOT analysis that identifies the implications of these characteristics for the future of work. In doing so, we demonstrate that although technological change is a constant, so too is humans’ ability to adapt to change.
Rate-Distortion Driven Decomposition of Multiview Imagery to Diffuse and Specular Components

In this work, we propose an overcomplete representation of multiview imagery for the purpose of compression. We present a rate-distortion (R-D) driven approach to decompose multiview datasets into two additive parts which can be interpreted as diffuse and specular content. We choose distinct and different sparsifying transforms for the diffuse and specular components and employ an R-D inspired measure as our optimization cost function to drive the decomposition based solely on compressibility. We first describe a framework which performs data separation in a registered domain to avoid the complexity of warping between views. Then a more comprehensive approach is proposed to separate specular data progressively from coordinates of multiple reference views. Experimental results show a coding gain of up to 0.6 dB for synthetic datasets and up to 0.9 dB for real datasets.
Multimedia context interpretation: a semantics-based cooperative indexing approach

The relative ineffectiveness of semantics-based multimedia indexing systems on the Web is caused by the semantic knowledge incompleteness and semantic heterogeneity problems. Nevertheless, the need to search multimedia documents with precision on the Web is persistently growing; pressing the demand for effective and efficient indexing strategies. In this article, we present an ontology-based multimedia indexing approach that cooperatively identifies the semantic and taxonomic relations that exist between annotation words that surround multimedia documents on Webpages. In this context, multiple ontologies are jointly employed for indexing each document. We construct inverted indexes in the form of semantic networks where nodes of each network are identified and added based on a majority-voting technique, while edges represent the semantic and taxonomic relations that hold between those nodes. To alleviate the heterogeneity between the resulting networks, we employ ontology merging algorithms to integrate them into consistent networks. We also utilise concept relatedness measures to enrich the networks with semantically-relevant entities that are not recognised by the used ontologies. To validate our proposal, we have developed a prototype system based on the proposed techniques. The produced results using real-world datasets demonstrate an improvement of the effectiveness against state-of-the-art baseline metrics.
Tile Quality Selection Method in 360-Degree Tile-Based Video Streaming

In this paper, we propose a tile quality selection method in tile-based video streaming. The proposed method suppresses the spatial quality variation within viewport caused by the change of the viewport region due to the movement of the user’s head. In the proposed method, client checks whether the difference of quality level between viewport and around viewport is large. When assigning quality, if the difference of quality level between the viewport and around viewport is large, the difference is reduced. As a result of simulation, when the segment length is long, quality variation can be suppressed without significantly reducing the perceived bitrate. Especially, the quality variation within viewport can be greatly suppressed.