Reviewerly/exHarmony huggingface dataset:
On Analysis of Lightweight Stream Ciphers with Keyed Update

As the need for lightweight cryptography has grown even more due to the evolution of the Internet of Things, it has become a greater challenge for cryptographers to design ultra lightweight stream ciphers in compliance with the rule of thumb that the internal state size should be at least twice as the key size to defend against generic Time-Memory-Data Tradeoff (TMDT) attacks. However, Recently in 2015, Armknecht and Mikhalev sparked a new light on designing keystream generators (KSGs), which in turn yields stream ciphers, with small internal states, called KSG with Keyed Update Function (KSG with KUF), and gave a concrete construction named Sprout. But, currently, security analysis of KSGs with KUF in a general setting is almost non-existent. Our contribution in this paper is two-fold. 1) We give a general mathematical setting for KSGs with KUF, and for the first time, analyze a class of such KSGs, called KSGs with Boolean Keyed Feedback Function (KSG with Boolean KFF), generically. In particular, we develop two generic attack algorithms applicable to any KSG with Boolean KFF having almost arbitrary output and feedback functions where the only requirement is that the secret key incorporation is biased. We introduce an upper bound for the time complexity of the first algorithm. Our extensive experiments validate our algorithms and assumptions made thereof. 2) We study Sprout to show the effectiveness of our algorithms in a practical instance. A straightforward application of our generic algorithm yields one of the most successful attacks on Sprout.
Convex non-convex segmentation of scalar fields over arbitrary triangulated surfaces

An extension of the Mumford–Shah model for image segmentation is introduced to segment real-valued functions having values on a complete, connected, 2-manifold embedded in R3. The proposed approach consists of three stages: first, a multi-phase piecewise smooth partition function is computed, then its values are clustered and, finally, the curve tracking is computed on the segmented boundaries. The first stage, which constitutes the key novelty behind our proposal, relies on a Convex Non-Convex variational model where an ad-hoc non-convex regularization term coupled with a space-variant regularization parameter allows to effectively deal with both the boundaries and the inner parts of the segments. The cost functional is minimized by means of an efficient numerical scheme based on the Alternating Directions Methods of Multipliers. Experimental results are presented which demonstrate the effectiveness of the proposed three-stage segmentation approach.
A High-Throughput VLSI Architecture for Real-Time Full-HD Gradient Guided Image Filter

Guided image filtering has been applied widely in recent years as a solution to the ever-increasing demand of high-performance filtering, especially for real-time image/video processing. The lately proposed gradient domain guided image filter (GDGIF) is one of the typical works focusing on improving the quality of the filtering result of the original guided image filter (GIF), dealing with the halo-artifacts problem for edge-preserving smoothing. However, due to the involvement of global pixel values in the computation, high computation complexity, and additional complex operation, there is no existing VLSI design for the gradient guided filter. This paper presents a high-throughput VLSI architecture for real-time full-HD (1920×1080) GDGIF. Four techniques are used in the newly proposed design to reduce the computation complexity and increase the processing throughput. First, a pre-processing stage is introduced to address the problem of global parameters required in the GDGIF. Second, multi-scale down-samplings are adopted for reducing circuit size and processing time. Then, a parallel structure operated at a higher frequency is used at the output stage to restore the full image size and to achieve high throughput at the same time. At last, approximated computation is introduced to eliminate speed bottleneck by simplifying the complex exponentiation operation. Based on the STM 65-nm CMOS technology, the implementation results show that the proposed architecture is able to support full-HD image filtering at a throughput above 75 frame/s, with a design area of 586314 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a power consumption of 18.5 mW.
A quality assessment approach for evolving knowledge bases

Knowledge bases are nowadays essential components for any task that requires automation with some degrees of intelligence. Assessing the quality of a knowledge base is a complex task as it often means measuring the quality of structured information, ontologies and vocabularies, and queryable endpoi nts. Popular knowledge bases such as DBpedia, YAGO2, and Wikidata have chosen the RDF data model to represent their data due to its capabilities for semantically rich knowledge representation. Despite its advantages, there are challenges in using RDF data model, for example, data quality assessment and validation. In this paper, we present a novel knowledge base quality assessment approach that relies on evolution analysis. The proposed approach uses data profiling on consecutive knowledge base releases to compute quality measures that allow detecting quality issues. Our quality characteristics are based on the evolution analysis and we used high-level change detection for measurement functions. In particular, we propose four quality characteristics: Persistency, Historical Persistency, Consistency, and Completeness. Persistency and historical persistency measure the degree of changes and lifespan of any entity type. Consistency and completeness identify properties with incomplete information and contradictory facts. The approach has been assessed both quantitatively and qualitatively on a series of releases from two knowledge bases, eleven releases of DBpedia and eight releases of 3cixty. The capability of Persistency and Consistency characteristics to detect quality issues varies significantly between the two case studies. Persistency gives observational results for evolving knowledge bases. It is highly effective in case of knowledge bases with periodic updates such as the 3cixty one. The Completeness characteristic is extremely effective and was able to achieve 95% precision in error detection for both use cases. The measures are based on simple statistical operations that make the solution both flexible and scalable.
A Judgment-Based Risk Assessment Framework for Consumer Loans

Credit to personal consumption is an important activity of the financial system and crucial to the socioeconomic development of a country. It is important, therefore, that the methods and techniques used to evaluate consumer credit risk be as efficient and informative as possible, in order to strengthen decisions to approve or reject credit and promote sustainable economic growth. This study aims to create a multiple criteria expert system which integrates cognitive maps and the measuring attractiveness by a categorical-based evaluation technique (MACBETH) to create a complementary framework for consumer credit risk assessment. The results show that this integrated approach allows the evaluation process of consumer credit risk to be more informed and transparent, providing value for the evaluation processes of this type of credit application as a result of the privileged contact established with a panel of credit analysts. Advantages, limitations, and managerial implications are also discussed.
New Multiplicative Inverse Architectures Using Gaussian Normal Basis

The multiplicative inverse over binary fields is one of main arithmetic operations used in cryptography. This paper presents two new inversion architectures. First, an improved architecture for classic inversion scheme using single multiplier is presented. The new improved inverter achieves lower latency through loading input registers during last multiplication cycle, at the expense of higher propagation delay. After this, a novel inversion architecture which uses half the latency to process the classic-based addition chains (or improved ones) is presented. The latter architecture, named Classical-Interleaved, is constructed based on a novel fully-serial-in square-multiply processor (FSISM). The FSISM, squares one operand, and multiply it to the second one, concurrently while the two inputs are absorbed serially digit-by-digit. The new classical inverter and the new classical-interleaved inverter reduce the latency compared to other schemes. In addition, the proposed classical and interleaved inverters outperform the original Itoh-Tsujii algorithm (ITA) and Ternary Itoh-Tsujii / optimal 3-chain algorithms in terms of its higher throughput and improved hardware efficiency for a number of digit sizes. The efficiency of the proposed field inverters are demonstrated by comparisons based on application specific integrated circuits (ASIC) implementations results using the standard 65 nm CMOS technology libraries.
Firefly photinus search algorithm

Firefly Algorithm (FA) is one of the new natural inspired optimization algorithms. It is inspired by the flashing behavior of the fireflies. Firefly algorithm, has some drawbacks such as getting trapped into several local optima, FA parameters are set fixed without change during iterations time. Besides that, it does not memorize or remember the history of any situation for each iteration. In this paper, we propos a firefly photinus algorithm (FPA) based on the initialize mate list to solve problems of trapped into several local optima and remember history of situation to forbidden fireflies movements in mate list (history) during the search process, and propose new absorption parameter σ to change the parameters during iterations time which lead to balance between exploration and exploitation, and it controls the dominance area of a lighter firefly during time iterations by reduction or increase σ coefficient whether. The experimental results tested on thirteen benchmark functions are selected to evaluate performance of the FPA and to compare it with the standards of the FA and Some FA variants algorithm, it show that FPA algorithm can outperform FA and FA variants algorithm in most of the experiments.
A Furcated Visual Collision Avoidance System for an Autonomous Microrobot

This paper proposes a secondary reactive collision avoidance system for microclass of robots based on a novel approach known as the furcated luminance-difference processing (FLDP) inspired by the lobula giant movement detector, a wide-field visual neuron located in the lobula layer of a locust nervous system. This paper addresses some of the major collision avoidance challenges: obstacle proximity and direction estimation, and operation in GPS-denied environment with irregular lighting. Additionally, it has proven effective in detecting edges independent of background color, size, and contour. The FLDP executes a series of image enhancement and edge detection algorithms to estimate collision threat-level which further determines whether the robot's field of view must be dissected where each section's response is compared against the others to generate a simple collision-free maneuver. Ultimately, the computation load and the performance of the model are assessed against an eclectic set of offline as well as real-time real-world collision scenarios validating the proposed model's asserted capability to avoid obstacles at more than 670 mm prior to collision, moving at 1.2 ms <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> with a successful avoidance rate of 90% processing at 120 Hz on a simple single-core microcontroller, sufficient to conclude the system's feasibility for real-time real-world applications that possess fail-safe collision avoidance system.
FPAP: A Folded Architecture for Energy-Quality Scalable Convolutional Neural Networks

Emerging convolutional neural networks (CNNs) tend to be designed with varied per-layer data widths and sparse representations. However, these two features, which bring many redundant computations, have not been exploited simultaneously in existing hardware architectures for CNNs. This paper proposes an energy-quality scalable architecture, namely folded precision-adjustable processor (FPAP), to eliminate all computational redundancies by using folding techniques. On one hand, FPAP decomposes the dominant multiply-accumulate (MAC) operations into multiple adds and folds them into single arithmetic unit. Only effective adds (or part of them) are then calculated serially. Thus, FPAP can adapt to different per-layer data widths and enable precision-adjustable approximate computing. Particularly, FPAP adaptively selects either activation or weight to be decomposed in every single MAC to minimize the total number of adds and clock cycles. On the other hand, a 1-D convolution is undertaken by a multi-tap transposed finite impulse response (FIR) filter, which is folded into one tap to skip MACs with zero weights or activations. Besides, a judicious delay element remapping scheme and a novel genetic algorithm-based kernel reallocation scheme, are developed to reduce the power consumption in a folded FIR filter and mitigate the load imbalance issue caused by irregular sparsity, respectively. With all these optimizations, FPAP is able to reach comparable or even faster processing speed over the corresponding unfolded design in sparse CNNs while consuming smaller area. Experimental results on real CNN models demonstrate that FPAP can scale its energy efficiency from 4.28 to 23.63 TOP/s/W, and area efficiency from 37.79 to 164.15GOP/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively, under the TSMC 28-nm HPC CMOS technology.
Identity-Preserving Public Integrity Checking with Dynamic Groups for Cloud Storage

Despite a variety of security threats, cloud storage is on the increase especially when a group of users need to store and share data. Identity-privacy and user dynamic operation are of growing concern in public integrity checking (PIC) scheme. In this paper, we develop an identity-preserving public integrity checking scheme with dynamic groups (IPIC-DG) for cloud storage. Firstly, our IPIC-DG scheme can realize the whole anonymity. On the one hand, no one except the group manager can discover the real identity of users. On the other hand, even the manager, who issues user's secret key, is not capable of forging signatures on behalf of others. Secondly, we propose an anonymous public integrity verification protocol which not only supports integrity checking without retrieving whole data from the cloud, but also protects the signer's identity during the whole process. We utilize group signature to construct a homomorphic authenticator on each file block to guarantee the anonymous remote data integrity checking. Thirdly, our scheme supports a way of dynamic user operation that greatly improves the efficiency and feasibility of user revocation. At last, we formally prove our IPIC-DG scheme is IND-CCA security. Experimental results show that our work performs well in practical application.
A fuzzy logic expert system to predict module fault proneness using unlabeled data

Several techniques have been proposed to predict the fault proneness of software modules in the absence of fault data. However, the application of these techniques requires an expert assistant and is based on fixed thresholds and rules, which potentially prevents obtaining optimal prediction results. In this study, the development of a fuzzy logic expert system for predicting the fault proneness of software modules is demonstrated in the absence of fault data. The problem of strong dependability with the prediction model for expert assistance as well as deciding on the module fault proneness based on fixed thresholds and fixed rules have been solved in this study. In fact, involvement of experts is more relaxed or provides more support now. Two methods have been proposed and implemented using the fuzzy logic system. In the first method, the Takagi and Sugeno-based fuzzy logic system is developed manually. In the second method, the rule-base and data-base of the fuzzy logic system are adjusted using a genetic algorithm. The second method can determine the optimal values of the thresholds while recommending the most appropriate rules to guide the testing of activities by prioritizing the module’s defects to improve the quality of software testing with a limited budget and limited time. Two datasets from NASA and the Turkish white-goods manufacturer that develops embedded controller software are used for evaluation. The results based on the second method show improvement in the false negative rate, f-measure, and overall error rate. To obtain optimal prediction results, developers and practitioners are recommended to apply the proposed fuzzy logic expert system for predicting the fault proneness of software modules in the absence of fault data.
A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images

Background: Various kind of medical imaging modalities are available for providing noninvasive view and for analyzing any pathological symptoms of human beings. Different noise may appear in those modalities at the time of acquisition, transmission, scanning, or at the time of storing. The removal of noises from the digital medical images without losing any inherent features is always considered a challenging task because a successful diagnosis relies on them. Numerous techniques have been proposed to fulfill this objective, and each having their own benefits and limitations. Discussion: In this comprehensive review article, more than 65 research articles are investigated to illustrate the applications of Artificial Neural Networks (ANN) in the field of biomedical image denoising. In particular, the zest of this article is to highlight the hybridized filtering model using nature-inspired algorithms and artificial neural networks for suppression of noise. Various other techniques, such as fixed filter, linear adaptive filters and gradient descent learning based neural network filter are also included. Conclusion: This article envisages how to train ANN using derivative free nature-inspired algorithms, and its performance in various medical images modalities and noise conditions.
Semantically Modeling of Object and Context for Categorization

Object-centric-based categorization methods have been proven more effective than hard partitions of images (e.g., spatial pyramid matching). However, how to determine the locations of objects is still an open problem. Besides, modeling of context areas is often mixed with the background. Moreover, the semantic information is often ignored by these methods that only use visual representations for classification. In this paper, we propose an object categorization method by semantically modeling the object and context information (SOC). We first select a number of candidate regions with high confidence scores and semantically represent these regions by measuring correlations of each region with prelearned classifiers (e.g., local feature-based classifiers and deep convolutional-neural-network-based classifiers). These regions are clustered for object selections. The other selected areas are then viewed as context areas. We treat other areas beyond the object and context areas within one image as the background. The visually and semantically represented objects and contexts are then used along with the background area for object representations and categorizations. Experimental results on several public data sets well demonstrate the effectiveness of the proposed object categorization method by semantically modeling the object and context information.
Parallel Transport Unfolding: A Connection-Based Manifold Learning Approach

Manifold learning offers nonlinear dimensionality reduction of high-dimensional datasets. In this paper, we bring geometry processing to bear on manifold learning by introducing a new approach based on metric connection for generating a quasi-isometric, low-dimensional mapping from a sparse and irregular sampling of an arbitrary manifold embedded in a high-dimensional space. Geodesic distances of discrete paths over the input pointset are evaluated through parallel transport unfolding (PTU) to offer robustness to poor sampling and arbitrary topology. Our new geometric procedure exhibits the same strong resilience to noise as one of the staples of manifold learning, the Isomap algorithm, as it also exploits all pairwise geodesic distances to compute a low-dimensional embedding. While Isomap is limited to geodesically convex sampled domains, PTU does not suffer from this crippling limitation, resulting in an improved robustness to irregularity and voids in the sampling. Moreover, it involves only simple linear algebra, significantly improves the accuracy of all pairwise geodesic distance approximations, and has the same computational complexity as Isomap. Finally, we show that our connection-based distance estimation can be used for faster variants of Isomap, such as L-Isomap.
A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise.