Reviewerly/exHarmony huggingface dataset:
A multiparty watermarking protocol for cloud environments

Abstract The advances in multimedia and networking technologies together with the wide spread of social networks and user generated content platforms have turned ordinary web users into actual multimedia content generators. This has given rise to the copyright protection problem of multimedia contents published or distributed on the Internet. In fact, dishonest web users or administrators of networks and platforms can easily duplicate the digital images and videos they host without reducing their perceptual quality. Then, such contents can be maliciously modified and/or re-distributed, thus damaging the reputation of their legitimate owners, or revealing their private information, or causing economic loss. A possible solution to such a problem consists in protecting digital content by adopting mechanisms of copyright identification and content tracking by fingerprinting based on digital watermarking technologies used in conjunction with watermarking protocols. However, most of the watermarking protocols documented in the literature have been designed as two-party protocols between classic buyers and sellers. On the contrary, the complexity of the distribution models of digital contents on the Internet needs new watermarking protocols based on multi-party approaches, which should make it possible to securely involve actors different from buyers and sellers, such as, for example, cloud computing operators. The paper presents a multi-party watermarking protocol that enables content owners to exploit cloud computing environments to protect the digital contents that they want to publish or distribute on the Internet. Such a result is achieved by enabling the different actors involved in the online transactions needed to support web content protection and distribution to securely interact according to a protocol that can mediate among conflicting interests.
Predicting the Severity of Open Source Bug Reports Using Unsupervised and Supervised Techniques

The severity of the bug report helps for the bug triagers to prioritize the handling of bug reports for giving more importance to high critical bugs than less critical bugs, since the inexperienced developers and new users can make mistakes while assigning the severity. The manual labeling of severity is labor-intensive and time-consuming. In this article, both unsupervised and supervised learning algorithms are used to automate the prediction of bug report severity. Because the data was unlabeled, the Gaussian Mixture Model is used to group similar kinds of bug reports. The result is labeled data with the severity level given for each bug reports. Then, the training of classifiers is performed to predict the severity of new bug reports submitted by the user using Multinomial Naïve Bayes Classifier, Logistic Regression Classifier and Stochastic Gradient Descent Classifier. Using these methods, around 85% accuracy is obtained. More accurate predictions can be done using the authors approach.
Design and Implementation of Low-Power High-throughput PRNGs for Security Applications

Pseudo-Random Number Generators (PRNGs) are an integral part of cryptographic applications, such as key generations, digital signatures, Internet-of-Things (IoT) security, etc. These applications require low-power and high-throughput PRNGs along with statistically secure random numbers generation capability. In this paper, we propose two PRNG methods based on Blum-Blum-Shub (BBS), Xorshift and Permuted Congruential PRNGs. The first PRNG is preferred for general purpose applications while the second is preferred for low-power IoT applications. The proposed PRNG methods are implemented on Xilinx FPGA ZedBoard Zynq^TM-7000 and generate 4.83e7 and 4.29e7 random numbers per-second, respectively. The total dynamic power consumption of the proposed PRNGs is 17mW at 48.31Mhz and 16mW at 42.90Mhz with a maximum throughput of 184.288MBps and 163.651MBps, respectively. The proposed PRNGs are tested on Diehard battery and US National Institute of Standard and Technology (NIST) SP 800 - 22 suites for analyzing the randomness quality.
Polynomial Time Algorithms for Constructing Optimal AIFV Codes

Huffman Codes are "optimal" Fixed-to-Variable (FV) codes if every source symbol can only be encoded by one codeword. Relaxing this constraint permits constructing better FV codes. More specifically, recent work has shown that AIFV codes can beat Huffman coding. AIFV codes construct a set of different coding trees between which the code alternates and are only "almost instantaneous" (AI). This means that decoding a word might require a delay of a finite number of bits. Current algorithms for constructing optimal AIFV codes are iterative processes that construct progressively "better sets" of code trees. The processes have been proven to finitely converge to the optimal code but with no known bounds on the convergence time. This paper derives a geometric interpretation of the space of AIFV codes. This permits the development of new polynomially time-bounded iterative procedures for constructing optimal AIFV codes. For the simplest case we show that a binary search procedure can replace the current iterative process. For the more complicated cases we describe how to frame the problem as a linear programming problem with an exponential number of constraints but a polynomial time separability oracle. This permits using the Grotschel, Lovasz and Schrijver ellipsoid method to solve the problem in a polynomial number of steps.
Agile Implementation-A milestone for Academics using Software Engineering Industry Practices

Agile methods are getting popular in project development. When it comes to software engineering techniques, the methodology is to be selected according to the requirements of the software being developed or the project in consideration. There is always a question as how well a technique implemented in industry can be effectively be applied in academics also to train students as if in industry. This paper aims to increase the understanding of how pair programming and scrum can be adopted in academia. A case study approach using 12 university students, supplemented with feedback form, ethical forms on pair programming in academia. The methodology for pair programming was performed in the university lab. The Scrum approach was performed in a Group projects for national level. We proposed two frameworks SIA and SEAM that uses the industry practiced models with the focus on pair programming and scrum in academics. Thus we propose two novel models named Scrum in Academia (SIA) and Software Engineering Agile Model (SEAM) in Academics while students engage in group projects as a team to get quality work to be submitted meeting deadline as if in industry. We argue that the approach of using pair programming and scrum methodology can be adopted in the academics.
PSO-ANE: Adaptive Network Embedding With Particle Swarm Optimization

Network embedding plays an important role in various network applications, such as node classification and link prediction. Lots of structure-based network embedding methods have been proposed. Yet, they suffer from unsteady embedding performances due to the parameter sensitivity. How to extract valuable attribute information in networks with less parameter influence is still a challenge. In this paper, we propose a novel algorithm, named adaptive network embedding with particle swarm optimization (PSO-ANE), which is based on the second-order dynamic random walk and PSO method, for learning network representations. A second-order dynamic random walk is designed to search a suitable strategy for each node based on the structure-based transition probability, the centrality-based transition probability, and the static link weights. To reduce the parameter dependence, PSO is adopted for key-parameter optimization to get global steady network embedding. The experiments validate that the proposed method outperforms the existing state-of-the-art techniques on multilabel classification, multiclass classification, and link prediction tasks.
Building Scholars and Communities of Practice in Digital Heritage and Archaeology

Abstract As the role of digital methods in heritage and archaeology has increased in prominence, so has the question of capacity and community building. Who should receive training in digital methods? How should training take place? What concepts, platforms, technologies should be taught? These are relevant questions requiring careful planning and thoughtful implementation; yet beyond these questions, there is an issue of even greater importance: the planned development of communities of practice. The teaching of digital methods has a greater chance of success if it takes place in an ecosystem of scholars who are connected to one another through shared perspectives on those methods. This article presents and discusses the details of a model developed at Michigan State University that speaks to teaching digital archaeology and heritage methods, and to the development of communities of practice in which those methods are shared and relevant. The model is driven and informed by the activities of three projects: the National Endowment for the Humanities-funded Institute on Digital Archaeology Method &amp; Practice, the Cultural Heritage Informatics Graduate Fellowship Program, and the Department of Anthropology Digital Cultural Heritage Fieldschool.
Singular race models

As machine learning based predictive systems pervade many aspects of our lives, an inherent bias and unfairness surface from time to time in the form of mispredictions in various domains. Recidivism, the tendency of offenders to reoffend after release from prison on parole, is one such domain where one race-based sub-population has been found to be treated more harshly than others. Current practices have focused on eliminating race information from datasets to reduce the predictive bias. In contrast to this, we built Singular Race Models, a novel approach of segmenting the dataset based on race, to train and test single race-based models to increase prediction accuracy and reduce racially inspired bias by considering only one race at a time. We created Singular Race Models for four different crime categories and compared these with base models created using all crimes and all races. This modeling choice helped us increase accuracy and analyze race related discrimination. A three-layered artificial neural network was utilized to do the heavy weight-lifting of recidivism prediction. With the help of several suitable metrics, in this paper, we demonstrate the increase in predictive accuracy of these Singular Race Models in various crime categories and analyze the causes and the secondary effect on bias.
Model-Based Book Recommender Systems using Naïve Bayes enhanced with Optimal Feature Selection

Book recommender systems play an important role in book search engines, digital library or book shopping sites. In the field of recommender systems, processing data, selecting suitable data features, and classification methods are always challenging to decide the performance of a recommender system. This paper presents some solutions of data process, feature and classifier selection in order to build an efficient book recommender system. The Book-Crossing dataset, which has been studied in many book recommender systems, is taken into account as a case study. The attributes of books are analyzed and processed to increase the classification accuracy. Some well-known classification algorithms, such as, Naïve Bayes, decision tree, etc., are utilized to predict user interests in books and evaluated in several experiments. It has been found that Naïve Bayes is the best selection for book recommendation with acceptable run-time and accuracy.
Improved Video Stabilization using SIFT-Log Polar Technique for Unmanned Aerial Vehicles

The video taken from unmanned aerial vehicles are usually not stabilized by dynamic conditions of the camera platform. This paper presents an improved solution for video stabilization of aerial vehicles by combining “scale invariant feature transform (SIFT)” with “log-polar transform”. The input video frame is transformed into log-polar that provides extended scale and rotation invariance. Afterward, key-points are extracted using the SIFT method and tracked in consecutive frames to eliminate the unwanted motion in a video. Hence, the presented technique performs better even if the object's scale and orientation vary in video frames when unmanned aerial vehicles change its altitude and orientation during the flight. The results of SIFT and log-polar method are compared with the standard SIFT-ME algorithm to analyze the scale and rotation invariance capability for video stabilization. The proposed technique shows the better capacity to stabilize video even when the UAV changes its attitude and orientation (i.e. scale change tolerance: 44.5% and Rotation tolerance: 53%).
Composite learning adaptive backstepping control using neural networks with compact supports

Summary The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a class of strict‐feedback nonlinear systems with mismatched uncertainties, where raised‐cosine radial basis function NNs with compact supports are applied to approximate system uncertainties. Both online historical data and instantaneous data are utilized to update NN weights. Practical exponential stability of the closed‐loop system is established under a weak excitation condition termed interval excitation. The proposed approach ensures fast parameter convergence, implying an exact estimation of plant uncertainties, without the trajectory of NN inputs being recurrent and the time derivation of plant states. The raised‐cosine radial basis function NNs applied not only reduces computational cost but also facilitates the exact determination of a subregressor activated along any trajectory of NN inputs so that the interval excitation condition is verifiable. Numerical results have verified validity and superiority of the proposed approach.
Combinatorial Iterative Algorithms for Computing the Centroid of an Interval Type-2 Fuzzy Set

Computing the centroid of an interval type-2 fuzzy set (IT2 FS) is an important type-reduction method. The aim of this paper is to develop a new method to calculate the centroid of an IT2 FS when the problems of centroid computation of an IT2 FS are continuous. For the continuous centroid computation problems, the structures of optimal solutions are strictly proven from mathematics for the first time in this paper. Furthermore, we also prove that the structures of the optimal solutions are unique in the sense of almost everywhere equal, i.e., if there are two optimal solutions f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (x) and f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (x), the Lebesgue measure of {x|f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (x) ≠ f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (x)} is equal to 0. Subsequently, a combinatorial iterative (CI) method is proposed to solve the roots of the sufficiently differentiable objective functions. It is proven that the convergence of the proposed iterative method is at least sixth order. Based on the proposed iterative method, two algorithms, called CI algorithms, are devised to compute the centroid of anIT2 FS. The efficiencies of CI algorithms are demonstrated by comparing the continuous Karnik-Mendel algorithms and the Hallye's methods with the CI algorithms through three numerical examples.
SDCNN: An Efficient Sparse Deconvolutional Neural Network Accelerator on FPGA

Generative adversarial networks (GANs) have shown excellent performance in image generation applications. GAN typically uses a new type of neural network called deconvolutional neural network (DCNN). To implement DCNN in hardware, the state-of-the-art DCNN accelerator optimizes the dataflow using DCNN-to-CNN conversion method. However, this method still requires high computational complexity because the number of feature maps is increased when converted from DCNN to CNN. Recently, pruning has been recognized as an effective solution to reduce the high computational complexity and huge network model size. In this paper, we propose a novel sparse DCNN accelerator (SDCNN) combining these approaches on FPGA. First, we propose a novel dataflow suitable for the sparse DCNN acceleration by loop transformation. Then, we introduce a four stage pipeline for generating the SDCNN model. Finally, we propose an efficient architecture based on SDCNN dataflow. Experimental results on DCGAN show that SDCNN achieves up to 2.63 times speedup over the state-of-the-art DCNN accelerator.
On some measures of ordinal variation

A family of variation measures for ordinal data or ordinal time series is considered. The asymptotic distribution of these measures is derived under different scenarios, and the performance of the resulting approximations is investigated with simulations. Possible applications include the bias correction of point estimates, the computation of confidence intervals as well as a test for maximal dispersion. A couple of real-data examples are presented to illustrate the application and interpretation of the ordinal variation measures.
Unsupervised Feature Extraction – A CNN-Based Approach

Working with large quantities of digital images can often lead to prohibitive computational challenges due to their massive number of pixels and high dimensionality. The extraction of compressed vectorial representations from images is therefore a task of vital importance in the field of computer vision. In this paper, we propose a new architecture for extracting such features from images in an unsupervised manner, which is based on convolutional neural networks. The model is referred to as the Unsupervised Convolutional Siamese Network (UCSN), and is trained to embed a set of images in a vector space, such that local distance structure in the space of images is approximately preserved. We compare the UCSN to several classical methods by using the extracted features as input to a classification system. Our results indicate that the UCSN produces vectorial representations that are suitable for classification purposes.
User Behaviour-Based Mobile Authentication System

Android is one of the most popular operating systems being used in smartphones and is facing security issues. Many authentication techniques are being used, and most of them are based on username, password or PIN. These techniques are considered weak because of several drawbacks such as passwords and PIN can be guessed, forgotten or stolen. When the mobile phone is stolen, misplaced or in the possession of the third party, an unauthorized user can get access to the applications and features of the mobile phone. Furthermore, after a one-time login, no further credentials are required. Therefore, a better security authentication system is needed to overcome this problem. This study proposes an identity management framework that can secure mobile application’s data from an unauthorized user. The proposed framework consists of various features that are extracted from phone swiping behaviour. The framework is comprised of enrolment and authentication phases. In the enrolment phase, the system learns the user behaviour, and in the authentication phase, it is able to accept or reject the current user based on his behaviour.
A First-Order Approach to Accelerated Value Iteration

Markov decision processes (MDPs) are used to model stochastic systems in many applications, but computing good policies becomes hard when the effective horizon become very large. In “A First-Order Approach to Accelerated Value Iteration,” Goyal and Grand-Clément present a connection between value iteration (VI) algorithms and gradient descent methods from convex optimization and use acceleration and momentum to design faster algorithms, with convergence guarantees for the computation of the value function of a fixed policy for reversible MDP instances. The authors provide a lower bound on the convergence properties of any first-order algorithm for solving MDPs, where no algorithm can converge faster than VI. Finally, the authors introduce safe accelerated value iteration (S-AVI), which alternates between accelerated updates and value iteration updates. The algorithm S-AVI is worst-case optimal and retains the theoretical convergence properties of VI while exhibiting strong empirical performances and providing significant speedups when compared with classical approaches for a large test bed of MDP instances.
FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images

In social image retrieval, the main goal is to offer a relevant but also diverse result set of images to the user. To address relevance and diversity at the same time, we propose a multi-modal procedure. This approach deals with the diversification problem using a two-step procedure based on the application of Formal Concept Analysis (FCA) to organize the text content of the images, followed by a Hierarchical Agglomerative Clustering (HAC) step to find the topics addressed by the images. FCA detects the latent concepts covered by the images in the result set, organizing them according to these concepts. In the second step, clustering is carried out to group together the ones with a similar concept. To assess the relevance, we use an adaptive multi-model relevance feedback algorithm which uses the low-level visual features to estimate a relevance measurement for all the images in the dataset. Several local logistic regression models are automatically adjusted to select the best performance for each topic. Finally, the images are ranked by selecting the highest probability image at each text cluster, generating a relevant but diverse ranked list. Diverse social images 2013, 2014, 2015, 2016 and 2017 datasets from the MediaEval benchmark are used to test the performance of our approach in a real scenario. Experimental results show that the proposed joint multimedia procedure works well on multi-concept and complex collections without adjusting parameters among collections, achieving the best results with a user-based relevance feedback algorithm. Our challenge is to achieve similar results with our automatic version.
Adaptive Chosen-Plaintext Collision Attack on Masked AES in Edge Computing

Edge computing handles delay-sensitive data and provides real-time feedback, while it brings data security issues to edge devices (such as IoT devices and edge servers). Side-channel attacks main threaten to these devices. Collision attack represents a powerful category of side-channel analysis in extracting security information from embedded cryptographic algorithms. Since its proposition in 2003, plenty of collision detection algorithms are presented, most of which enumerate all the values of target plaintext byte to find a collision. In this paper, we establish a relation between “Euclidean distance between traces” and “Hamming distance between values,” and take advantage of the distance information leaked from the power traces of encrypting an adaptively chosen plaintext to reduce the candidate plaintext space. Consequently, the collision is detected at a high pace. Moreover, this improvement is fault-tolerant, and its self-correction feature promotes the efficiency of attacks based on our method significantly. We take AES implemented with masks, which is usually employed in edge computing devices, for instance, to introduce our method and conduct experiments to verify its efficiency. According to the experimental results, for whole key recovery attacks, our method requires only 26.5% plaintexts, 32.2% traces, and much less than 10% computations of the collision-correlation attack launched by Clavier et al.