Reviewerly/exHarmony huggingface dataset:
The minimum Manhattan distance and minimum jump of permutations

Let π be a permutation of {1,2,…,n}. If we identify a permutation with its graph, namely the set of n dots at positions (i,π(i)), it is natural to consider the minimum L1 (Manhattan) distance, d(π), between any pair of dots. The paper computes the expected value (and higher moments) of d(π) when n→∞ and π is chosen uniformly, and settles a conjecture of Bevan, Homberger and Tenner (motivated by permutation patterns), showing that when d is fixed and n→∞, the probability that d(π)≥d+2 tends to e−d2−d. The minimum jump mj(π) of π, defined by mj(π)=min1≤i≤n−1⁡|π(i+1)−π(i)|, is another natural measure in this context. The paper computes the asymptotic moments of mj(π), and the asymptotic probability that mj(π)≥d+1 for any constant d.
Security Policy Opt-in Decisions in Bring-Your-Own-Device (BYOD) – A Persuasion and Cognitive Elaboration Perspective

Bring-Your-Own-Device (BYOD) has gained increased popularity in organizations but may engender information security concerns. To address these concerns, employees are expected to opt-in and comply with organizational BYOD security policy. This study investigates the factors that affect employees’ opt-in decisions with BYOD security policy. Drawing on the theoretical lenses of persuasion and cognitive elaboration, we propose that employees’ cognitive elaborations of BYOD security policy could be affected by the valence of justification of the BYOD security policy, the stringency of BYOD security measures, and the sequence of the introduction of BYOD security policy in relation to employees’ use of personal devices to perform organizational tasks and such cognitive elaborations would in turn affect opt-in decisions. We conducted an experimental survey to test our propositions. The results indicate that positive BYOD security policy justification framing and post-task security policy exposure would lead to more positive cognitive elaboration, decision to opt-in, and compliance with the BYOD security policy. This research has significant implications for security management with respect to the design and implementation of BYOD security policy within an organization according to the nature of security policy and the task requirements.
Discriminative clustering using regularized subspace learning

Clustering is among the most important unsupervised learning tasks with several applications in a wide range of domains. Discriminative Clustering (DC) techniques combine the unsupervised nature of clustering with the high discriminative ability of supervised subspace learning methods by simultaneously performing clustering and learning a representation that encourages the separability of the clusters. However, in contrast with classical supervised learning tasks, where the labels are usually correct, cluster assignments are inherently noisy leading to suboptimal results when used as ground truth information with highly discriminative methods. To this end, a novel similarity-based subspace learning method, that allows for learning regularized clustering-oriented representations, avoiding the pitfalls of highly discriminative methods, such as Linear Discriminant Analysis (LDA), is proposed in this paper. The ability of the proposed method to improve the quality of the obtained clustering solutions is demonstrated using extensive experiments on four datasets.
A Noncoherent Space-Time Code from Quantum Error Correction

In this work, we develop a space-time block code for noncoherent communication using techniques from the field of quantum error correction. We decompose the multiple-input multiple-output (MIMO) channel into operators from quantum mechanics, and design a non-coherent space time code using the quantum stabilizer formalism. We derive an optimal decoder, and analyze the former through a quantum mechanical lens. We compare our approach to a comparable coherent approach and a noncoherent differential approach, achieving comparable or better performance.
Residual Pyramid Fcn for Robust Follicle Segmentation

In this paper, we propose a pyramid network structure to improve the FCN-based segmentation solutions and apply it to label thyroid follicles in histology images. Our design is based on the notion that a hierarchical updating scheme, if properly implemented, can help FCNs capture the major objects, as well as structure details in an image. To this end, we devise a residual module to be mounted on consecutive network layers, through which pixel labels would be propagated from the coarsest layer towards the finest layer in a bottom-up fashion. We add five residual units along the decoding path of a modified U-Net to make our segmentation network, Res-Seg-Net. Experiments demonstrate that the multi-resolution set-up in our model is effective in producing segmentations with improved accuracy and robustness.
Phase Noise Robustness of a Coherent Spatially Parallel Optical Reservoir

Reservoir computing is a machine learning algorithm particularly adapted to process time-dependent signals. It can be easily implemented experimentally with good performance. However experimental implementations are subject to noise, which degrades performance. We develop strategies to mitigate the effects of noise. The specific system on which we illustrate our approaches-currently under development in our laboratory-is a coherent linear Fabry-Perot resonator in which neurons are encoded as a grid of spots on the input mirror plane. In this system, changes in the length of the resonator are the major source of noise and can be modeled as phase noise. This can in principle be partially solved by active stabilisation, but it is interesting to find other strategies to counter the effect of phase noise. We show that a completely unknown phase can be tolerated with only a small degradation of performance by using appropriate training and a readout layer architecture in which the output weights depend on the noisy parameter (the phase). Furthermore, the phase can be estimated by the reservoir itself, leading to an architecture in which the reservoir has two outputs, one of which (the phase estimation) is used to control the other. Our approach should find applications in many experimental implementations of reservoir computing.
An Optimal Long-Term Aerial Infrared Object Tracking Algorithm With Re-Detection

In the field of automatic target recognition and tracking, long-term tracking for aerial infrared target has been recently seen with great interest. Although deep trackers and correlation filtering trackers offer competitive results on performance, the problems of deformation, abrupt motion, heavy occlusion, and out of view still remain unsolved. In addition, since this paper focus on infrared images, it is also important to consider that infrared images have a significant drawbacks, such as low resolution, low contrast, and lack of textures. In this paper, we adopt correlation filtering trackers and deep learning detection method to achieve accurate tracking results. Our tracking system composed of three parts: the DTB correlation filtering tracker (DTB-CF), a better regression model to discriminate the target from the background with adjustable Gaussian window functions; the UTA correlation filtering tracker (UTA-CF), an optimum regression model to update the target appearance with simultaneously optimal in position, scale, and integration of multi-feature fusion; and the YOLOv3 re-detector, which ensures re-location of the correct position of the target when the tracking fails. In addition, we introduce the ratio between average peak-to-correlation energy (APCE) of the current frame and average APCE of former frames as a criterion to update the UTA-CF tracker to maintain the target model stability. And we combine the nearest neighbor maximum value method with APCE as criterion together to initialize the YOLOv3 re-detector. We evaluate our algorithm on real aerial infrared target thermal image sequences in terms of precision plot, success plot, and speed. The experimental results show that our method has a significant improvement than the state-of-the-art methods for long-term tracking both in accuracy and robustness for aerial infrared object tracking.
Cross-Domain Face Sketch Synthesis

Synthesizing sketches from facial photos is of great significance to digital entertainment. Along with higher demands on sketch quality in a complex environment, however, it has been an urgent issue on how to synthesize realistic sketches with the limited training data. The existing face sketch methods pay less attention to the insufficient problem of the training data, leading to the synthesized sketches with some noise or without some identity-specific information in real-world applications. Target on providing sufficient photo-sketch pairs to meet the demand of users in digital entertainment, we present a cross-domain face sketch synthesis framework in this paper. In the photo-sketch mixed domain, we leverage the generative adversarial network to construct a cross-domain mapping function and generate identity-preserving face sketches as the hidden training data. Combined it with the insufficient original training data, we provide sufficient training data to recover the underlying structures and learn the cross-domain transfer of the high-level qualitative knowledge from the photo domain to the sketch domain by the latent low-rank representation. The qualitative and quantitative evaluations on the public facial photo-sketch database demonstrate that the proposed cross-domain face sketch synthesis method can solve the insufficient problem of the training data successfully. And it outperforms other state-of-the-art works and generates more vivid and cleaner facial sketches.
Generalized Subspace Subcodes With Application in Cryptology

Most codes with an algebraic decoding algorithm are derived from Reed-Solomon codes. They are obtained by taking equivalent codes, for example, generalized Reed-Solomon codes, or by using the so-called subfield subcode method, which leads to alternant codes over the underlying prime field, or over some intermediate subfield. The main advantage of these constructions is to preserve both the minimum distance and the decoding algorithm of the underlying Reed-Solomon code. In this paper, we explore in detail the subspace subcodes construction. This kind of codes was already studied in the particular case of cyclic Reed-Solomon codes. We extend this approach to any linear code over the extension of a finite field. We are interested in additive codes who are deeply connected to subfield subcodes. We characterize the duals of subspace subcodes. We introduce the notion of generalized subspace subcodes. We apply our results to generalized Reed-Solomon codes which leads to codes with interesting parameters, especially over a large alphabet. To conclude this paper, we discuss the security of the use of generalized subspace subcodes of Reed-Solomon codes in a cryptographic context.
Multi-view multitask learning for knowledge base relation detection

Relation detection is a key component of knowledge base question answering (KBQA). Existing methods mainly focus on learning semantic relevance between the question and the candidate relation, which is challenging due to the lexical variation (i.e. lexical gaps) especially for relations with few annotated samples. In this paper, we propose to estimate the semantic relevance from both the traditional question–relation view and a novel question–question view, which leverages the similarities among questions corresponding to the same relation. The question–question view can facilitate the learning of few-shot relations, and it supports accessing the annotated samples during inference, thus allowing new annotations to take effect on–the–fly. Moreover, a multi-task learning framework is devised to jointly optimize the models of different views. Experimental results on WebQSP and a Chinese KB relation detection dataset demonstrate the effectiveness and generalization ability of the proposal.
Reconstructed similarity for faster GANs-based word translation to mitigate hubness

In machine word translation, Nearest Neighbor (NN) retrieval is able to search the best-k translation candidates as linguistic labels of a source query from a unified multilingual semantic feature space. However, NN is polluted by hubs in the high-dimensional feature space. Many proposed approaches remove hubs in the list of translation candidates to relieve this problem. But those approaches to eliminating hubs are flawed because they also have corresponding translations. To address this issue, we propose a novel Reconstructed Similarity (RS) retrieval for the neural machine word translation model to mitigate the hubness problem regardless of whether it is a hub. Different from previous work, RS reduces the impact of hubness pollution in dense and high-dimensional space and allows the hubs to have the same probability as the target candidates without being inappropriately excluded. In addition, RS improves the quality of bilingual dictionaries by measuring the bilateral similarity of the bilingual and monolingual distance of each of the source query embeddings. Additionally, to model the unsupervised machine word translation, we introduce Generative Adversarial Nets (GANs) to map the source and target word distribution into a shared semantic space. We also construct a tiny GAN topology for neural machine word translation, which is at least 52 × faster than previous GAN-based models. To further align cross-lingual embedding distributions, we provide orthogonal Procrustes mapping, global-awareness of the transformation matrix and rescaling of the target embeddings as flexible and optional multirefinements. The results show that our model outperforms the state-of-the-art by nearly 4% in distant languages such as English to Finnish. Compared with a [email protected] of 47.00% from English to Finnish, our model obtains a [email protected] of 47.53% and achieves state-of-the-art results in a fully unsupervised form. Moreover, our model achieves competitive results in the shortest time among GAN-based models, which easily trade off between speed and accuracy.
Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge

Abstract The Decision Model and Notation (DMN) is a recent Object Management Group standard for the elicitation and representation of decision models and for managing their interconnection with business processes. DMN builds on the notion of decision tables and their combination into more complex decision requirements graphs (DRGs), which bridge between business process models and decision logic models. DRGs may rely on additional, external business knowledge models, whose functioning is not part of the standard. In this work, we consider one of the most important types of business knowledge, namely, background knowledge that conceptually accounts for the structural aspects of the domain of interest, and propose decision knowledge bases (DKBs), which semantically combine DRGs modeled in DMN, and domain knowledge captured by means of first-order logic with datatypes. We provide a logic-based semantics for such an integration, and formalize different DMN reasoning tasks for DKBs. We then consider background knowledge formulated as a description logic (DL) ontology with datatypes, and show how the main verification tasks for DMN in this enriched setting can be formalized as standard DL reasoning services and actually carried out in ExpTime. We discuss the effectiveness of our framework on a case study in maritime security.
Total Organic Carbon Prediction with Artificial Intelligence Techniques

This study used the Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM) and Artificial Neural Network (ANN) models with a feed-forward neural network structure and partial least squares (PLSR) methods to estimate total organic carbon. In order to develop models, on-line data measured at five-minute time intervals were collected through one year (2007-2008) from the online-monitoring stations which were built near the River Yeşilırmak in Amasya in North-Eastern Turkey. These stations were the first practice in Turkey. Twelve parameters as luminescent dissolved oxygen (LDO), pH, conductivity, nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total organic carbon (TOC), chloride, orthophosphate, temperature, turbidity, suspended solid and flow rate were measured at the on-line monitoring stations. To predict the total organic carbon, four input variables, pH, conductivity, dissolved oxygen and temperature were selected. Moreover, the data were also collected at the central office in Ankara via a General Packet Radio Service (GPRS) channel. The validity of models was tested by using statistical methods in MATLAB including correlation coefficients (R), mean absolute percentage error (MAPE%) and root mean square error (RMSE). The best result was obtained in the presence of KELM with a radial basis function (RBF) kernel. Rtest = 0.984, MAPEtest = 3.01, RMSEtest = 0.9676. Additionally, Rtrain = 0.995, MAPEtrain = 1.58 and RMSEtrain = 0.532. Among the other two algorithms ANN provided better results than ELM and PLSR.
Are you eligible? Predicting adulthood from face images via Class Specific Mean Autoencoder

Predicting if a person is an adult or a minor has several applications such as inspecting underage driving, preventing purchase of alcohol and tobacco by minors, and granting restricted access. The challenging nature of this problem arises due to the complex and unique physiological changes that are observed with age progression. This paper presents a novel deep learning based formulation, termed as Class Specific Mean Autoencoder, to learn the intra-class similarity and extract class-specific features. We propose that the feature of a particular class if brought similar/closer to the mean feature of that class can help in learning class-specific representations. The proposed formulation is applied for the task of adulthood classification which predicts whether the given face image is of an adult or not. Experiments are performed on two large databases and the results show that the proposed algorithm yields higher classification accuracy compared to existing algorithms and a Commercial-Off-The-Shelf system.
Mining Treatment-Outcome Constructs from Sequential Software Engineering Data

Many investigations in empirical software engineering look at sequences of data resulting from development or management processes. In this paper, we propose an analytical approach called the Gandhi-Washington Method (GWM) to investigate the impact of recurring events in software projects. GWM takes an encoding of events and activities provided by a software analyst as input. It uses regular expressions to automatically condense and summarize information and infer treatments. Relating the treatments to the outcome through statistical tests, treatment-outcome constructs are automatically mined from the data. The output of GWM is a set of treatment-outcome constructs. Each treatment in the set of mined constructs is significantly different from the other treatments considering the impact on the outcome and/or is structurally different from other treatments considering the sequence of events. We describe GWM and classes of problems to which GWM can be applied. We demonstrate the applicability of this method for empirical studies on sequences of file editing, code ownership, and release cycle time.
Visual Re-ranking with Natural Language Understanding for Text Spotting

Many scene text recognition approaches are based on purely visual information and ignore the semantic relation between scene and text. In this paper, we tackle this problem from natural language processing perspective to fill the gap between language and vision. We propose a post-processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text. For this, we initially rely on an off-the-shelf deep neural network, already trained with large amount of data, which provides a series of text hypotheses per input image. These hypotheses are then re-ranked using word frequencies and semantic relatedness with objects or scenes in the image. As a result of this combination, the performance of the original network is boosted with almost no additional cost. We validate our approach on ICDAR’17 dataset.
COB method with online learning for object tracking

Object tracking is a problem about semi-supervised learning with insufficient data set. In the field of military navigation and security of public life, it is widely used to take the place of human beings. In this paper, we come up with a new algorithm based on Bayesian, CNN and PLK optical flow, which is called COB method, for object tracking problems. With the idea of track-by-detect, we cascade CNN after PLK optical flow and integrate them in a Bayesian method. Most importantly our method is proposed with an adaptive integrating method to reduce the influence of over-fitting. The integrator also introduces the competition mechanism between tracker and detector, so that the algorithm is able to update the classifier with online learning. Besides, the regularization of deep learning is used to solve the blind spots of classifier. The experimental results show that the algorithm is more robust than the previous work.
PMCVec: Distributed phrase representation for biomedical text processing

Distributed semantic representation of biomedical text can be beneficial for text classification, named entity recognition, query expansion, human comprehension, and information retrieval. Despite the success of high-quality vector space models such as Word2Vec and GloVe, they only provide unigram word representations and the semantics for multi-word phrases can only be approximated by composition. This is problematic in biomedical text processing where technical phrases for diseases, symptoms, and drugs should be represented as single entities to capture the correct meaning. In this paper, we introduce PMCVec, an unsupervised technique that generates important phrases from PubMed abstracts and learns embeddings for single words and multi-word phrases simultaneously. Evaluations performed on benchmark datasets produce significant performance gains both qualitatively and quantitatively.
A test for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e3992" altimg="si3.gif"><mml:mi>k</mml:mi></mml:math> sample Behrens–Fisher problem in high dimensional data

In this paper, the k sample Behrens–Fisher problem is investigated in high dimensional setting. We propose new test statistics and demonstrate that the proposed test statistics are expected to have higher power than some existing tests especially when sample sizes are unbalanced. We provide theoretical investigation as well as numerical studies on both size and power of the proposed tests and existing tests.