Reviewerly/exHarmony huggingface dataset:
What can violations of good practices tell about the relationship between GoF patterns and run-time quality attributes?

GoF patterns have been extensively studied with respect to the benefit they provide as problem-solving, communication and quality improvement mechanisms. The latter has been mostly investigated through empirical studies, but some aspects of quality (esp. run-time ones) are still under-investigated. In this paper, we study if the presence of patterns enforces the conformance to good coding practices. To achieve this goal, we explore the relationship between the presence of GoF design patterns and violations of good practices related to source code correctness, performance and security, via static analysis. Specifically, we exploit static analysis so as to investigate whether the number of violations of good coding practices identified on classes is related to: (a) their participation in pattern occurrences, (b) the pattern category, (c) the pattern in which they participate, and (d) their role within the pattern occurrence. To answer these questions, we performed a case study on approximately 13,000 classes retrieved from five open-source projects. The obtained results suggest that classes not participating in patterns are more probable to violate good coding practices for correctness, performance and security. In a more fine-grained level of analysis, by focusing on specific patterns, we observed that patterns with more complex structure (e.g., Decorator) and pattern roles that are more change-prone (e.g., Subclasses) are more likely to be associated with a higher number of violations (up to 50 times more violations). This finding implies that investing in a well-thought architecture based on best practices, such as patterns, is often accompanied with cleaner code with fewer violations.
Integrating Concurrency Control in n-Tier Application Scaling Management in the Cloud

Scaling complex distributed systems such as e-commerce is an importance practice to simultaneously achieve high performance and high resource efficiency in the cloud. Most previous research focuses on hardware resource scaling to handle runtime workload variation. Through extensive experiments using a representative n-tier web application benchmark (RUBBoS), we demonstrate that scaling an n-tier system by adding or removing VMs without appropriately re-allocating soft resources (e.g., server threads and connections) may lead to significant performance degradation resulting from implicit change of request processing concurrency in the system, causing either over- or under-utilization of the critical hardware resource in the system. We build a concurrency-aware model that determines a near optimal soft resource allocation of each tier by combining some operational queuing laws and the fine-grained online measurement data of the system. We then develop a dynamic concurrency management (DCM) framework that integrates the concurrency-aware model to intelligently reallocate soft resources in the system during the system scaling process. We compare DCM with Amazon EC2-AutoScale, the state-of-the-art hardware only scaling management solution using six real-world bursty workload traces. The experimental results show that DCM achieves significantly shorter tail latency and higher throughput compared to Amazon EC2-AutoScale under all the workload traces.
Loss Asymmetries in Quantum Traveling-Wave Parametric Amplifiers

Traveling-wave parametric amplifiers (TWPAs) are widely used in quantum information science for amplification and measurement at the quantum limit, but their development is held back by a lack of understanding of their susceptibility to internal loss. The authors use both lumped-element and distributed-loss models to describe the output of a lossy TWPA, and identify a surprising, strong dependence on the symmetry of loss between signal and idler modes. This insight will have immediate impact on the design of TWPAs as both quantum-limited amplifiers and sources of squeezed radiation.
When Human cognitive modeling meets PINs: User-independent inter-keystroke timing attacks

This paper proposes the first user-independent inter-keystroke timing attacks on PINs. Our attack method is based on an inter-keystroke timing dictionary built from a human cognitive model whose parameters can be determined by a small amount of training data on any users (not necessarily the target victims). Our attacks can thus be potentially launched on a large scale in real-world settings. We investigate inter-keystroke timing attacks in different online attack settings and evaluate their performance on PINs at different strength levels. Our experimental results show that the proposed attack performs significantly better than random guessing attacks. We further demonstrate that our attacks pose a serious threat to real-world applications and propose various ways to mitigate the threat.
Identifying the most informative features using a structurally interacting elastic net

Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information loss. To address this problem, we propose a new graph-based structurally interacting elastic net method for feature selection. Specifically, we commence by constructing feature graphs that can incorporate pairwise relationship between samples. With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation. This measure is used to obtain a structural interaction matrix where the elements represent the proposed information theoretic measure between feature pairs. We then formulate a new optimization model through the combination of the structural interaction matrix and an elastic net regression model for the feature subset selection problem. This allows us to (a) preserve the information of the original vectorial space, (b) remedy the information loss of the original feature space caused by using graph representation, and (c) promote a sparse solution and also encourage correlated features to be selected. Because the proposed optimization problem is non-convex, we develop an efficient alternating direction multiplier method (ADMM) to locate the optimal solutions. Extensive experiments on various datasets demonstrate the effectiveness of the proposed method.
Blind color-image deblurring based on color image gradients

In this paper, a blind color-image deblurring method based on color image gradients is proposed. The color-image deblurring model is built by introducing color image gradients in the rgb normalized space. The deblurring process is based on alternating iterations of image estimations and blurring kernel estimations. The clear image is obtained by the alternating direction method of multipliers (ADMM), and the blurring kernel is calculated by the half-quadratic penalty method. Experimental results show that the proposed method effectively estimates the blurring kernels. The fidelity of the deblurred images is better, for both motion blurred images and defocused images, than what is achieved with other state-of-the-art blind image deblurring methods. Furthermore, the quality metrics for the proposed method also verify the value of our method compared with other methods.
Deep Saliency With Channel-Wise Hierarchical Feature Responses for Traffic Sign Detection

Traffic sign detection is challenging in cases of a complex background, occlusions, distortions, and so on. To overcome the above-mentioned challenges, this paper pays close attention to channel-wise feature responses to propose an end-to-end deep learning-based saliency traffic sign detection method. Our model contains three main components: channel-wise coarse feature extraction (CCFE), channel-wise hierarchical feature refinement (CHFR), and hierarchical feature map fusion (HFMF). In addition, it is based on the squeeze-and-excitation-residual network to explicitly model the inter dependences between the channels of its convolution features at a slight computational cost. We first apply CCFE to produce coarse feature maps with much information loss. To make full use of spatial information and fine details, CHFR is executed to refine hierarchical features. After that, HFMF is used to fuse hierarchical feature maps to generate the final traffic sign saliency map. Compared with other five traffic sign detection methods, the experimental results demonstrate the efficiency (a real-time speed) and superior performance of the proposed method according to comprehensive evaluations over three benchmark data sets.
Linear Equivalence of Block Ciphers with Partial Non-Linear Layers: Application to LowMC

$$\textsc {LowMC}$$ is a block cipher family designed in 2015 by Albrecht et al. It is optimized for practical instantiations of multi-party computation, fully homomorphic encryption, and zero-knowledge proofs. $$\textsc {LowMC}$$ is used in the $$\textsc {Picnic}$$ signature scheme, submitted to NIST's post-quantum standardization project and is a substantial building block in other novel post-quantum cryptosystems. Many $$\textsc {LowMC}$$ instances use a relatively recent design strategy (initiated by Gérard et al. at CHES 2013) of applying the non-linear layer to only a part of the state in each round, where the shortage of non-linear operations is partially compensated by heavy linear algebra. Since the high linear algebra complexity has been a bottleneck in several applications, one of the open questions raised by the designers was to reduce it, without introducing additional non-linear operations (or compromising security). In this paper, we consider $$\textsc {LowMC}$$ instances with block size n, partial non-linear layers of size $$s \le n$$ and r encryption rounds. We redesign LowMC's linear components in a way that preserves its specification, yet improves LowMC's performance in essentially every aspect. Most of our optimizations are applicable to all SP-networks with partial non-linear layers and shed new light on this relatively new design methodology. Our main result shows that when $$s < n$$ , each $$\textsc {LowMC}$$ instance belongs to a large class of equivalent instances that differ in their linear layers. We then select a representative instance from this class for which encryption (and decryption) can be implemented much more efficiently than for an arbitrary instance. This yields a new encryption algorithm that is equivalent to the standard one, but reduces the evaluation time and storage of the linear layers from $$r \cdot n^2$$ bits to about $$r \cdot n^2 - (r-1)(n-s)^2$$ . Additionally, we reduce the size of LowMC's round keys and constants and optimize its key schedule and instance generation algorithms. All of these optimizations give substantial improvements for small s and a reasonable choice of r. Finally, we formalize the notion of linear equivalence of block ciphers and prove the optimality of some of our results. Comprehensive benchmarking of our optimizations in various $$\textsc {LowMC}$$ applications (such as $$\textsc {Picnic}$$ ) reveals improvements by factors that typically range between 2x and 40x in runtime and memory consumption.
Multisensor Composite Kernels Based on Extreme Learning Machines

In this letter, we first propose multisensor composite kernel (MCK) extreme learning machines to fuse hyperspectral and light detection and ranging (LiDAR) features effectively. Then, based on the MCK, we develop a fully automatic fusion framework. In the proposed framework, spatial and elevation features of hyperspectral and LiDAR data are first extracted using extinction profiles. Then, hyperspectral Stein's unbiased risk estimator is utilized to extract the subspace (informative features) of spectral, spatial, and elevation features. The obtained results indicate that the proposed approach can successfully integrate and classify hyperspectral and LiDAR images to provide accurate classification results classification accuracies in an automatic manner.
Treasure Collection on Foggy Islands: Building Secure Network Archives for Internet of Things

Fog computing has emerged as a promising paradigm in overcoming the growing challenges (e.g., low latency, location awareness, and geographic distribution) arising from many real-world Internet of Things (IoT) applications, by extending the cloud to the network edge. With the widespread deployment of fog-assisted IoT applications, unprecedentedly huge volumes of network traffic from massive IoT devices would continuously arrive at the fog nodes. Archiving the network traffic can be highly beneficial to fog computing, which forms the basis of forensic, monitoring, troubleshooting, and many other critical tasks. Such high value, however, constantly renders traffic archives the first-order target to experienced attackers. This mandates the traffic archives to be built in a trustworthy way and stayed encrypted at rest. Security aside, it is yet highly desirable to retain the utility of the encrypted traffic archives, in particular by making them privately queryable. In this paper, we take the first research attempt and explore a new design point to delicately bridge trusted hardware and searchable encryption for building trustworthy, encrypted, yet queryable network traffic archives for fog-assisted IoT applications. We take a systematic approach to address several key challenges, which are unsolvable by synthesizing out-of-box techniques, from ground up. Extensive evaluations show that our system can achieve stable archiving throughput of 350 Mb/s with one core, and saturate a 1 Gb/s link with four cores; for a real trace, it outperforms a baseline system without any of our designs by over 110× .
Web-Based Learning and Development of University's Electronic Informational Educational Environment

Web-based learning has been developed by the majority of academic institutions and organizations worldwide due to its obvious benefits for both educators and learners. Meanwhile, many of the existing developmental approaches in this sphere lack one crucial consideration necessary for implementing web-based learning at academic institutions. In this article, the authors identify two processes: development of distance learning and digitalization of training that are represented now at almost every academic university. The article singles out the stages of their development and shows that the processes lead to the same result - a new quality of education. The authors focus on figuring out which path is more effective for achieving the highest level of development depending on university's characteristics. Accumulation of detailed information in the Electronic informational educational environment about current learning outcomes creates opportunities for emerging trends, such as Learning Analytics, Personalized Learning and Adaptive Learning. In data analysis using BigData and Machine Learning technologies.
On the minimum distance of negacyclic codes with two zeros

We investigate the minimum distance of q -ary negacyclic codes of length q m − 1 2 generated by a product of two distinct minimal polynomials. A necessary and sufficient condition on the minimum distance of such negacyclic codes is given. Several classes of optimal quinary negacyclic codes with parameters [ ( 5 m − 1 ) / 2 , ( 5 m − 1 ) / 2 − 2 m , 4 ] are constructed. The dual codes of a subclass of the quinary negacyclic codes are studied. A comparison with cyclic codes is also presented.
Digit-Serial Versatile Multiplier Based on a Novel Block Recombination of the Modified Overlap-Free Karatsuba Algorithm

Overlap-Free Karatsuba Algorithm (OFKA) combined with block recombination approach (OFKABR) can improve the complexity of the original OFKA to obtain efficient implementation of polynomial basis multiplication over finite field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ). In this paper, we have further proposed a modified OFKABR (MOFKABR) strategy to reduce the space and time complexities of the best-known method. The proposed strategy is also extended to obtain a low-complexity versatile multiplier, which is designed to support the generalized multiplication on a wider range of field size for m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ≤m≤m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sub> . Using the proposed MOFKABR approach, the proposed digit-serial versatile multiplier can achieve subquadratic space complexity when compared with the existing digit-serial versatile multipliers.
Approximate Fisher Information Matrix to Characterize the Training of Deep Neural Networks

In this paper, we introduce a novel methodology for characterizing the performance of deep learning networks (ResNets and DenseNet) with respect to training convergence and generalization as a function of mini-batch size and learning rate for image classification. This methodology is based on novel measurements derived from the eigenvalues of the approximate Fisher information matrix, which can be efficiently computed even for high capacity deep models. Our proposed measurements can help practitioners to monitor and control the training process (by actively tuning the mini-batch size and learning rate) to allow for good training convergence and generalization. Furthermore, the proposed measurements also allow us to show that it is possible to optimize the training process with a new dynamic sampling training approach that continuously and automatically change the mini-batch size and learning rate during the training process. Finally, we show that the proposed dynamic sampling training approach has a faster training time and a competitive classification accuracy compared to the current state of the art.
Content-based medical image retrieval by spatial matching of visual words

Content-Based Image Retrieval (CBIR) systems have recently emerged as one of the most promising and best image retrieval paradigms. To pacify the semantic gap associated with CBIR systems, the Bag of Visual Words (BoVW) techniques are now increasingly used. However, existing BoVW techniques fail to capture the location information of visual words effectively. This paper proposes an unsupervised Content-Based Medical Image Retrieval (CBMIR) framework based on the spatial matching of the visual words. The proposed method efficiently computes the spatial similarity of visual words using a novel similarity measure called the Skip Similarity Index. Experiments on three large medical datasets reveal promising results. The location-based correlation of visual words assists in more accurate and efficient retrieval of anatomically diverse and multimodal medical images than the state-of-the-art CBMIR systems.
Query inseparability for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:mi mathvariant="script">ALC</mml:mi></mml:math> ontologies

We investigate the problem whether two ALC ontologies are indistinguishable (or inseparable) by means of queries in a given signature, which is fundamental for ontology engineering tasks such as ontology versioning, modularisation, update, and forgetting. We consider both knowledge base (KB) and TBox inseparability. For KBs, we give model-theoretic criteria in terms of (finite partial) homomorphisms and products and prove that this problem is undecidable for conjunctive queries (CQs), but 2ExpTime-complete for unions of CQs (UCQs). The same results hold if (U)CQs are replaced by rooted (U)CQs, where every variable is connected to an answer variable. We also show that inseparability by CQs is still undecidable if one KB is given in the lightweight DL EL and if no restrictions are imposed on the signature of the CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query over any ABox in a given signature and show that, for CQs, this problem is undecidable, too. We then develop model-theoretic criteria for HornALC TBoxes and show using tree automata that, in contrast, inseparability becomes decidable and 2ExpTime-complete, even ExpTime-complete when restricted to (unions of) rooted CQs.
Enhancing image visuality by multi-exposure fusion

Image visuality enhancement aims at increasing visual quality of a given image to convey more useful information. The key for visuality enhancement is to comprehensively exploit the details of the image scene. However, one (or several) observed image only provides partial information of the scene. To address this problem, we present a novel multi-exposure fusion based visuality enhancement method in this study. Firstly, we propose a simulated exposure model to synthesize the results of the observed image under different exposure conditions. Then, white balancing and image gradient are separately performed on those simulated exposure results. By doing these, scene details in different exposure conditions as well as image feature spaces (i.e., color and gradient spaces) can be well exploited. To appropriately take advantage of those resultant details, we adopt the Laplacian pyramid based linear fusion framework to integrate them into the enhanced image in a multi-scale way. Different from conventional fusion methods, we develop a more powerful weight map for fusion, which is able to simultaneously highlight pixels with good exposedness, contrast, saturation as well as Gamut. Experimental results demonstrate that the proposed method can effectively enhance the visuality of the observed image. Compared with existing methods, the proposed method well highlights the fine details as well as avoiding halo artifacts.
Wavelet energy feature based source camera identification for ear biometric images

In this paper a source camera identification algorithm for ear biometric images has been proposed based on tunable filter bank as a feature extractor. Maintaining the frequency selectivity property, distinct features are extracted by this filter bank, based on a half-band polynomial of 14th order. With the help of four ear databases, it is demonstrated that tunable filter bank based features correctly identify the sources of ear images with an average accuracy of 99.25% when there are limited number of camera sources available. It is also shown that accuracy would fall when significantly large number of cameras are introduced to acquire ear images. Depending on the experimental results, it can be well concluded that tunable filter bank based feature, apart from its recognition performance, is also a promising candidate to support forensic validation of camera source.
High-speed 3D shape measurement by one pitch phase analysis method using brightness values in small square area of single-shot image

The authors have previously proposed one pitch phase analysis (OPPA) method in which the phase of a grating at a pixel point is analyzed using brightness values of one pitch of a projected grating of a single-shot image. Conventional phase-shifting method which uses several images during one-cycle shifting, cannot analyze moving objects. The OPPA method is useful to measure a 3D shape of moving objects in motion capture and vibration. However, since the method uses the brightness values in the oblong rectangular area of N × 1 pixels of the recording camera, the results are anisotropic and a wide area near the edge of an object has error. In this paper, a new phase analysis method using N brightness values in a small square area is proposed. The area with large error at the edge is reduced and the anisotropy is improved. The theory and the applications for motion capture are shown.