Reviewerly/exHarmony huggingface dataset:
Altmetrics of the Open Access Institutional Repositories: a webometrics approach

Self-archiving in Institutional Repositories (IRs) is playing a central role in the success of the Open Access initiatives. Deposited documents are more visible and probably they get more downloads and citations, but making them freely available in a local repository is not enough. Social tools, both public and academic targeting, networking or silo oriented, should be taken into account for reaching larger audiences and increase not only the scholarly but also the social impact. The paper explores the presence of IRs contents in 28 social tools (Academia, Bibsonomy, CiteUlike, CrossRef, Datadryad, Facebook, Figshare, Google+, GitHub, Instagram, LinkedIn, Pinterest, Reddit, RenRen, ResearchGate, Scribd, SlideShare, Tumblr, Twitter, Vimeo, VKontakte, Weibo, Wikipedia All Languages, Wikipedia English, Wikia, Wikimedia, YouTube and Zenodo) using a webometric approach. We collected the link mentions of 2185 IRs in the cited tools during July 2017 from Google selected data centers. The results show that most of the IRs have no strong presence in the most specializes tools and even for the most popular services the figures are not high enough too. A candidate explanation for the low number of altmetric mentions is the lack of strategy in promotion of IRs contents and certain bad practices mostly regarding URL naming.
Person re-identification based on re-ranking with expanded k-reciprocal nearest neighbors

In this paper, a robust re-ranking method based on expanded k-reciprocal neighbors is proposed. Our method assumes that if a gallery image is the probe image of the expanded k-reciprocal nearest neighbors, these images are more likely to be of the same person. Specifically, given a probe image, we replace the probe with its expanded reciprocal nearest neighbor and the final distance is computed by the mean value of the corresponding neighbor set. The proposed method is unsupervised, automatic and applicable to other person re-identification problems. Moreover, our method can perform well even with a simple direct rank list where the Euclidean distance was used to compute the distances between the images. Experiments on many public datasets demonstrate the effectiveness and robustness of our re-ranking method. The proposed method achieves 4.9% improvement in Rank-1 on the CUHK03 dataset and a significant improvement of 18.6% in mAP on the Duke dataset.
Attacks on the confidentiality, integrity and availability of data and computer systems in the criminal case law of the Czech Republic

Uncovering attacks on data and computer systems and those responsible for them is one of the contemporary problems that the authorities involved in criminal proceedings have to deal with. Where this sort of cybercrime is concerned we can expect not only high levels of latency but also a low clear-up rate for crimes on file. This paper demonstrates this using the example of the Czech Republic, by providing an analysis of all cases of this type of cybercrime dealt with by courts between 2008 and 2016. It focuses in particular on proceedings concerned with illegal access and data and system interference. The cybercrimes that are brought to court in the Czech Republic is not representative of the cybercrime committed in the Czech Republic either in terms of its type or its extent. The cases dealt with by the courts are primarily connected with personal relationships and the Internet. The most frequent type of case thus concerns the misuse of passwords for accessing social networking sites or e-mail inboxes. We were also surprised to find that the courts had begun to apply facts relevant to cybercrime cases in cases involving the manual manipulation of gambling machines. The Czech Republic has evidently not yet found a way of demonstrating criminals’ guilt in more complex cybercrime cases.
Scene Classification by Coupling Convolutional Neural Networks With Wasserstein Distance

The traditional convolutional neural networks (CNNs) coupled with cross-entropy loss ignore interclass relationship, and hence output unreasonable predictions from a holistic perspective. We address this issue by integrating CNNs with Wasserstein distance (WD): first, we find that the classical WD problem has an analytical solution in the case of multiclass classification; second, by leveraging multiple pretrained CNNs to extract multiscale convolutional features and encoding the features via the improved Fisher kernel, we propose a novel method for computing the ground distance matrix, which characterizes the affinities between classes and is also a key component of the WD problem; third, we use the analytical solution to construct new losses for CNNs. Our proposed model is applied to scene classification and leads to a higher performance than other methods.
A Quantum Query Complexity Trichotomy for Regular Languages

We present a trichotomy theorem for the quantum query complexity of regular languages. Every regular language has quantum query complexity Θ(1), Θ̃(√ n), or Θ(n). The extreme uniformity of regular languages prevents them from taking any other asymptotic complexity. This is in contrast to even the context-free languages, which we show can have query complexity Θ(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sup> ) for all computable c [1/2,1]. Our result implies an equivalent trichotomy for the approximate degree of regular languages, and a dichotomy-either Θ(1) or Θ(n)-for sensitivity, block sensitivity, certificate complexity, deterministic query complexity, and randomized query complexity. The heart of the classification theorem is an explicit quantum algorithm which decides membership in any star-free language in Õ(√n) time. This well-studied family of the regular languages admits many interesting characterizations, for instance, as those languages expressible as sentences in first-order logic over the natural numbers with the less-than relation. Therefore, not only do the star-free languages capture functions such as OR, they can also express functions such as "there exist a pair of 2's such that everything between them is a 0." Thus, we view the algorithm for star-free languages as a nontrivial generalization of Grover's algorithm which extends the quantum quadratic speedup to a much wider range of string-processing algorithms than was previously known. We show a variety of applications-new quantum algorithms for dynamic constant-depth Boolean formulas, balanced parentheses nested constantly many levels deep, binary addition, a restricted word break problem, and path-discovery in narrow grids-all obtained as immediate consequences of our classification theorem.
Graphics for Uncertainty

Summary Graphical methods such as colour shading and animation, which are widely available, can be very effective in communicating uncertainty. In particular, the idea of a ‘density strip’ provides a conceptually simple representation of a distribution and this is explored in a variety of settings, including a comparison of means, regression and models for contingency tables. Animation is also a very useful device for exploring uncertainty and this is explored particularly in the context of flexible models, expressed in curves and surfaces whose structure is of particular interest. Animation can further provide a helpful mechanism for exploring data in several dimensions. This is explored in the simple but very important setting of spatiotemporal data.
Bayberry image segmentation based on manifold ranking salient object detection method

A bayberry image segmentation method based on salient object detection is proposed in this study. First, an image enhancement algorithm, homomorphic filtering, was used for light compensation. Second, superpixels were used as the basic unit in the subsequent significant calculation with the SLIC (simple linear iterative clustering) algorithm and were constructed as nodes in an Undirected Graphical Model G (V,E). Third, a two-stage scheme of saliency detection was proposed. In the first stage, the nodes on each boundary were separately ranked as query nodes. Then, four saliency maps were obtained using a MR (manifold ranking) method. After combining the four saliency maps, the saliency map of the first stage was acquired. In the second stage, binary segmentation was performed, based on the saliency map of the first stage to obtain the target query. Finally, binary segmentation was performed based on the saliency map with the Otsu's method to achieve the segmentation of bayberry images. The average values of Precision, Recall and F-measure in the proposed algorithm are 0.93, 0.83 and 0.90, respectively. Experimental results show that the proposed segmentation algorithm performs more effectively than Otsu's and K-means segmentation algorithms.
A failure remember-driven self-adaptive differential evolution with top-bottom strategy

Differential evolution (DE) has attracted more and more attention. However, the neighborhood and direction information has not been fully utilized in exploration and exploitation stages. A failure remember-driven self-adaptive differential evolution algorithm, ATBDE, is proposed in this paper, which uses “Top-Bottom” strategy with optional archive and a parameter self-adapting strategy driven by “Failure Remember” operation. “Top-Bottom” strategy utilizes historical heuristic information obtained from the successful and failed individuals, respectively, to guide individuals toward the potential more promising regions in an optional archive manner. This strategy is also theoretically analyzed for the best implementation. The failure remember-driven parameter adaption strategy shares the positive search experience from the successful individuals and abandons the negative search experience for those successive failing individuals. Comprehensive experiments show that ATBDE is better than, or at least comparable to, other DE algorithms in terms of convergence performance and accuracy.
Object tracking under large motion: Combining coarse-to-fine search with superpixels

We propose an object tracking method under large motion in image sequences. Dense sampling and particle filtering have been widely applied to cope with this problem; however, the former is computationally expensive, and the latter is sensitive to local minima. By introducing a novel search method based on coarse-to-fine strategy and image superpixels, we try to solve both drawbacks. In the coarse step, we first extract superpixels associated with a target object on the entire search region by using a simple generative appearance model. In the fine step, we perform a sampling and similarity measurement process within the selected superpixels to find the most accurate location of the target object, also suggest a way to use both a discriminative appearance model and a sophisticated generative appearance model simultaneously. Extensive experiments on popular benchmark dataset demonstrate that the proposed method outperforms other competitive approaches, and also show better results in challenging scenarios such as occlusion, deformation, out-of-view, and in-plane/out-of-plane rotation.
A unified framework for M-estimation based robust Kalman smoothing

We consider the robust smoothing problem for a state-space model with outliers in measurements. A unified framework for robust smoothing based on M-estimation is developed, in which the robust smoothing problem is formulated by replacing the quadratic loss for measurement fitting in the conventional Kalman smoother by a robust cost function from robust statistics. The majorization-minimization method is employed to iteratively solve the formulated robust smoothing problem. In each iteration, a surrogate function is constructed for the robust cost, which enables the states update procedure to be implemented in a similar way as that in a conventional Kalman smoother with a reweighted measurement covariance. Numerical experiments show that the proposed robust approach outperforms the traditional Kalman smoother and several robust filtering methods.
Superadditivity in Trade-Off Capacities of Quantum Channels

In this paper, we investigate the additivity phenomenon in the quantum dynamic capacity region of a quantum channel for trading the resources of classical communication, quantum communication, and entanglement. Understanding such an additivity property is important if we want to optimally use a quantum channel for general communication purposes. However, in a lot of cases, the channel one will be using only has an additive single or double resource capacity region, and it is largely unknown if this could lead to a strictly superadditive double or triple resource capacity region, respectively. For example, if a channel has additive classical and quantum capacities, can the classical-quantum capacity region be strictly superadditive? In this paper, we answer such questions affirmatively. We give proof-of-principle requirements for these channels to exist. In most cases, we can provide an explicit construction of these quantum channels. The existence of these superadditive phenomena is surprising in contrast to the result that the additivity of both classical-entanglement and classical-quantum capacity regions imply the additivity of the triple resource capacity region for a given channel.
Experimental demonstration of the DPTS QKD protocol over a 170 km fiber link

Quantum key distribution (QKD) is a promising technology aiming at solving the security problem arising from the advent of quantum computers. While the main theoretical aspects are well developed today, limited performances, in terms of achievable link distance and secret key rate, are preventing the deployment of this technology on a large scale. More recent QKD protocols, which use multiple degrees of freedom for the encoding of the quantum states, allow an enhancement of the system performances. Here, we present the experimental demonstration of the differential phase-time shifting protocol (DPTS) up to 170 km of fiber link. We compare its performance with the well-known coherent one-way (COW) and the differential phase shifting (DPS) protocols, demonstrating a higher secret key rate up to 100 km. Moreover, we propagate a classical signal in the same fiber, proving the compatibility of quantum and classical light.
Detecting anomalies in sequential data augmented with new features

This paper presents a new weighted local outlier factor method for anomaly detection, which is underpinned with three novel components: (1) a piecewise linear representation defined on the basis of the important points that consist of extreme points and additional points; (2) a set of new features which are used to identify anomalies given the new piecewise linear representation; (3) a weighting schema, assigning different weights to different features by accounting for the discriminant power of the features. The underlying idea of the proposed method is to characterize a time series with a set of four features and then discover abnormal changes by taking account of the closeness of any data points augmented with the new features. The comparative experiments demonstrate that the proposed piecewise representation method has performed well in sequential time series data, and the weighted local outlier factor method has achieved better accuracy and RankPower in detecting anomalies from the same data sets in comparison with the conventional local outlier factor, normalized local outlier factor and HOT symbolic aggregate approximation methods.
Automated Documentation of Android Apps

Developers do not always have the knowledge needed to understand source code and must refer to different resources (e.g., teammates, documentation, the web). This non-trivial process, called program comprehension, is very time-consuming. While many approaches support the comprehension of a given code at hand, they are mostly focused on defining extractive summaries from the code (i.e., on selecting from a given piece of code the most important statements/comments to comprehend it). However, if the information needed to comprehend the code is not there, their usefulness is limited. We present ADANA, an approach to automatically inject comments describing a given piece of Android code. ADANA reuses the descriptions of similar and well-documented code snippets retrieved from various online resources. Our evaluation has shown that ADANA is able to aid the program comprehension process.
On Quantum Related-Key Attacks on Iterated Even-Mansour Ciphers

It has been said that security of symmetric key schemes is not so much affected by quantum computers, compared to public key schemes. However, recent works revealed that, in some specific situations, symmetric key schemes are also broken in polynomial time by adversaries with quantum computers. These works contain a quantum distinguishing attack on 3-round Feistel ciphers and a quantum key recovery attack on the Even-Mansour cipher by Kuwakado and Morii, in addition to the quantum forgery attack on CBC-MAC which is proposed independently by Kaplan et al., and by Santoli and Schaffner. Iterated Even-Mansour cipher is a simple but important block cipher, which can be regarded as an idealization of AES. Whether there exists an efficient quantum algorithm that can break iterated Even-Mansour cipher with independent subkeys is an important problem from the viewpoint of analyzing post-quantum security of block ciphers. Actually there is an efficient quantum attack on iterated Even-Mansour cipher by Kaplan et al., but their attack can only be applied in the case that all subkeys are the same. This paper shows that there is a polynomial time quantum algorithm that recovers partial keys of the iterated Even-Mansour cipher with independent subkeys, in a related-key setting. The related-key condition is somewhat strong, but our algorithm can recover subkeys with two related oracles. In addition, we also show that our algorithm can recover all keys of the i-round iterated Even-Mansour cipher, if we are allowed to access i related quantum oracles. To realize quantum related-key attacks, we extend Simon's quantum algorithm so that we can recover the hidden period of a function that is periodic only up to constant. Our technique is to take differential of the target function to make a double periodic function, and then apply Simon's algorithm.
rmSWSpec: Real-Time Monitoring of Service Workflow Specification Language for Specification Patterns

Service-oriented computing (SOC) lays the foundation for modern enterprise systems where services are incorporated as building blocks in application workflows. However, SOC is still at development stage with many unsolved issues; particularly, composing services for a workflow pose a significant challenge when some factors including distribution and dynamic characteristics of services are in consideration. The number of vendors providing similar services is increasing, and this requires effective methodologies for service workflow compositions. Service workflow specification (SWSpec) language was proposed to specify requirements of service attributes and verify them in a workflow during service selection processes automatically. One requirement for SWSpec is that all services must be represented for service selections adequately, while existing SWSpec exhibits its limitation in addressing significant specification patterns found in service-based applications (SBAs). This paper proposes a strengthened SWSpec, called real-time monitoring SWSpec that incorporates new modalities to represent missing specification patterns of events occurring in SBAs.
An analysis of QoS ranking prediction framework techniques

In the real world, building the high quality cloud computing framework is the challenge for the researcher in the present scenario where on demand service is required. The services which are performing the non-functional activities are referred to as Quality-of Service (QoS). The experience of real-world usage services are generally required to obtain the QoS. Many organizations offer various cloud services, such as Amazon, HP and IBM, to the customers. No technique is available to measure the real-world usage and estimate the ranking of the cloud services. From the customer side, it is a very tough job to choose the right cloud service provider (SP), which fulfills all the requirements of the customers. To avoid the confusion to select the right CSP, this paper proposes QoS ranking prediction methods such as Cloud Rank1, Cloud Rank2 and Cloud Rank3. Various experiments are done on the real-world QoS data by using EC2 services of Amazon and providing healthy results and solutions.
Source identification of 3D printed objects based on inherent equipment distortion

The widespread use of 3D printers introduces tremendous challenges for the regulation of illegal products. In the current situation, since it is impossible to completely prohibit users from using 3D printers to manufacture illegal products, source identification of 3D printed products is a possible alternative for regulators to trace the offenders. In this paper, a source identification scheme for 3D printed objects based on inherent equipment distortion is proposed. By investigating the 3D printing process, an equipment distortion model is constructed, and then the inherent equipment distortion is analyzed. Furthermore, in order to exhibit the inherent equipment distortion, a uniform mark is designed and the inherent equipment distortion is extracted. With the features of the inherent equipment distortion of the 3D printers, SVM classifier is employed for the source identification of the 3D printed objects. Experimental results and analysis show that it can obtain an average identification accuracy of 91.1% with the 3D printed objects from 9 printers, and the analysis also indicates that it can achieve satisfactory robustness and reliability.
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization

The aim of computer-aided musical orchestration (CAMO) is to find a combination of musical instrument sounds that perceptually approximates a reference sound when played together. The complexity of timbre perception and the combinatorial explosion of all possible musical instrument sound combinations make it very challenging to find even one orchestration for a reference sound. However, finding only one orchestration is seldom enough given the creative nature of the compositional process. Compositional applications of computer-aided musical orchestration can greatly benefit from multiple orchestrations with diversity. In this work, we use an artificial immune system (AIS) called opt-aiNet to search for combinations of musical instrument sounds that minimize the distance to a reference sound encoded in a fitness function. Opt-aiNet was developed to maximize diversity in the solution set of multi-modal optimization problems, which results in multiple alternative orchestrations for the same reference sound that are different among themselves. We compared the diversity and the similarity of the orchestrations proposed by opt-aiNet (CAMO-AIS) against a standard genetic algorithm (CAMO-GA) and Orchids, which is considered the state of the art for CAMO, for 13 reference sounds. In general, CAMO-AIS outperformed CAMO-GA and Orchids for several measures of objective diversity. We performed a listening test to evaluate and compare the perceptual similarity of the orchestrations by CAMO-AIS and Orchids. CAMO-AIS generated orchestrations that were perceived to be as similar to the reference sounds as those returned by Orchids. Therefore, CAMO-AIS has higher diversity of orchestrations than Orchids without loss of perceptual similarity.