Reviewerly/exHarmony huggingface dataset:
Content-aware attributed entity embedding for synonymous named entity discovery

Synonymous Named Entity Discovery (SNED) refers to the task of discovering named entities that refer to the same entity. Discovering synonymous named entity by manually designing features and similarity metrics is non-trivial and very difficult due to the diversity of the raw features (e.g. the associated attributes and text content). In this paper, we present Content-Aware Attributed Entity Embedding (CAAEE), an unsupervised SNED model to address this issue. By leveraging the associated attributes and text content information, our approach learns a projection which maps named entities to a low-dimensional feature space without any manually designed feature and supervised information. In the learned feature space, synonymous named entities are close to each other, which can reflect the similarity between named entities. We build two heterogeneous networks to jointly model named entities, their associated attributes and text content information. For each heterogeneous network, we design two objective function based on two probability distributions aimed at preserving the network structure. By jointly optimizing the objective functions, a low-dimensional representation is obtained for each named entity. The similarity between the learned low-dimensional representations is then used to discover synonymous named entities. In experiments, we compare our model with existing SNED models on two real-world named entity datasets. Experimental results show that CAAEE outperforms state-of-the-art methods with significant improvement.
The generalisability of artificial neural networks used to classify electrophoretic data produced under different conditions

Previous work has shown that artificial neural networks can be used to classify signal in an electropherogram into categories that have interpretational meaning (such as allele, baseline, pull-up or stutter). The previous work trained the neural networks on a single data type, produced under a single laboratory condition and applied it to data that was matched in these factors. In this work we investigate the ability of neural networks to be trained on data of different types (i.e. single sourced profiles or mixed DNA profiles) and from different laboratory conditions (specifically the model of electrophoresis instrument) to determine whether a set of neural networks is required for each different type of data produced or whether a single neural network can be used for a broad range of data and still achieve the same level of performance. The results of our study have implications as to how a laboratory would choose to train and apply neural networks to classify data in electropherograms produced in their laboratory.
Poly-Logarithmic Side Channel Rank Estimation via Exponential Sampling

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over d subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. We propose ESrank, the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity, which also achieves excellent practical performance. Our main idea is to use exponential sampling to drastically reduce the algorithm's complexity. Importantly, ESrank is simple to build from scratch, and requires no algorithmic tools beyond a sorting function. After rigorously bounding the accuracy, time and space complexities, we evaluated the performance of ESrank on a real SCA data corpus, and compared it to the currently-best histogram-based algorithm. We show that ESrank gives excellent rank estimation (with roughly a 1-bit margin between lower and upper bounds), with a performance that is on-par with the Histogram algorithm: a run-time of under 1 s on a standard laptop using 6.5 MB RAM.
Human action recognition based on spatio-temporal three-dimensional scattering transform descriptor and an improved VLAD feature encoding algorithm

The local spatio-temporal descriptor and feature encoding algorithm are two crucial key steps for human action recognition based on spatio-temporal interest points (STIP). Since the local descriptors for STIP are essentially a type of motion information based on the texture, the key point of local feature description is to extract invariable, robust and distinguishable local texture features and motion information in reference spatio-temporal volume. Scattering transform is an image transform method based on directional wavelet transform and scale convolution, which has local translation invariance, rotation invariance and elastic deformation stability for local texture features. A novel local descriptor for STIP based on spatio-temporal three-dimensional scattering transform is proposed in this paper, which extends the original scattering transform to spatio-temporal three-dimensional space. Compared to the traditional descriptors, such as HOG, HOF and so on, the proposed scattering transform coefficients based histogram of oriented gradients (STC-HOG) descriptor can capture more robust and distinguishable motion information of local texture for STIP. In order to incorporate the local descriptors into action video representation, the feature encoding algorithm is indispensable. For the problem that vector of locally aggregated descriptors (VLAD) loses feature distribution location information during feature encoding, a histogram of distribution vector of locally aggregated descriptors (HOD-VALD) based on Gaussian kernel is proposed. We validated the proposed algorithm for human action recognition on multiple public available datasets, such as KTH, UCF Sports, HMDB51 and so on. The evaluation experiment results indicate that the proposed descriptor and encoding method can improve the efficiency of human action recognition and the recognition accuracy.
Towards more practical software-based attestation

Software-based attestation promises to enable the integrity verification of untrusted devices without requiring any particular hardware. However, existing proposals rely on strong assumptions that hinder their deployment and might even weaken their security. One of such assumptions is that using the maximum known network round-trip time to define the attestation timeout allows all honest devices to reply in time. While this is normally true in controlled environments, it is generally false in real deployments and especially so in a scenario like the Internet of Things where numerous devices communicate over an intrinsically unreliable wireless medium. Moreover, a larger timeout demands more computations, consuming extra time and energy and restraining the untrusted device from performing its main tasks. In this paper, we review this fundamental and yet overlooked assumption and propose a novel stochastic approach that significantly improves the overall attestation performance. Our experimental evaluation with IoT devices communicating over real-world uncontrolled Wi-Fi networks demonstrates the practicality and superior performance of our approach that in comparison with the current state of the art solution reduces the total attestation time and energy consumption around seven times for honest devices and two times for malicious ones, while improving the detection rate of honest devices (8% higher TPR) without compromising security (0% FPR).
Robust &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$L_{2,1}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Norm Distance Enhanced Multi-Weight Vector Projection Support Vector Machine

The enhanced multi-weight vector projection support vector machine (EMVSVM) is an outstanding algorithm for binary classification, which is proposed recently. However, it measures the distances in an objective function by the squared <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm, which exaggerates the effects of outliers or noisy data. In order to alleviate this problem, we propose an effective novel EMVSVM, termed robust EMVSVM based on the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm distance (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -EMVSVM). The distances in the objective of our algorithm are measured by the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm. Besides, a new powerful iterative algorithm is designed to solve the formulated objective, whose convergence is ensured by theoretical proofs. Finally, the effectiveness and robustness of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -EMVSVM are verified through extensive experiments.
Practical public key encryption with selective opening security for receivers

Data security and privacy protection is one of the highest concerns in cloud computing. Selective opening security (SOA security) is a security notion focusing on a multi-user setting on the Internet. In recent years, many public-key encryption (PKE) constcutions have been proposed that meet SOA security. Most of them focus on the sender corruption setting. However, less attention has been paid on the receiver corruption setting. Inspired by the work of Heuer et al. (PKC 2015), which showed a practical SOA secure PKE scheme (in the sender corruption setting) from a key encapsulation mechanism (KEM) and a message authentication code (MAC), we propose a practical generic PKE construction achieving simulation-based SOA security under chosen-ciphertext attacks for recievers (RSIM-SO-CCA security). Our construction is also based on a KEM and a MAC. We show that if the underlying KEM is one-way secure in the presence of a plaintext-checking oracle (OW-PCA) and the underlying MAC is strong unforgeable under one-time chosen message attacks (sUF-OT-CMA), then our generic construction is RSIM-SO-CCA secure in the random oracle model.
Numerical Quadrature for Probabilistic Policy Search

Learning control policies has become an appealing alternative to the derivation of control laws based on classic control theory. Model-based approaches have proven an outstanding data efficiency, especially when combined with probabilistic models to eliminate model bias. However, a major difficulty for these methods is that multi-step-ahead predictions typically become intractable for larger planning horizons and can only poorly be approximated. In this paper, we propose the use of numerical quadrature to overcome this drawback and provide significantly more accurate multi-step-ahead predictions. As a result, our approach increases data efficiency and enhances the quality of learned policies. Furthermore, policy learning is not restricted to optimizing locally around one trajectory, as numerical quadrature provides a principled approach to extend optimization to all trajectories starting in a specified starting state region. Thus, manual effort, such as choosing informative starting points for simultaneous policy optimization, is significantly decreased. Furthermore, learning is highly robust to the choice of initial policy and, thus, interaction time with the system is minimized. Empirical evaluations on simulated benchmark problems show the efficiency of the proposed approach and support our theoretical results.
Application of clustering-based decision tree approach in SQL query error database

In the educational environment field of teaching and learning for the SQL language, there have been recent approaches that use heuristic methods to categorize and discover information about student errors. The categorization of student source code errors by the level of complexity of the problem was one of the limitations found in these approaches. Owing to this limitation, the conclusions drawn from these studies are superficial and do not explain the real reasons that led the students to make their mistakes. As an alternative, we propose a new methodology that uses cluster-based decision trees and the fundamentals of the programming language to discover the most relevant patterns of knowledge about student behaviour. Through an innovative architecture for classifying errors, the methodology allows us to explain: (a) what are the most frequently committed errors, (b) when these errors usually occur and (c) the reasons that led to the error. The methodology was applied in a case study with more than 105,200 SQL queries, which, in addition to explaining the hypotheses about what leads students to make mistakes, also gave the following results: (1) it proved the concept of reducing the breadth of analysis of the decision tree, (2) it showed that an error database could be used for the discovery of knowledge and (3) it resulted in the discovery of a set of 66 rules; 12 related to semantic errors and 54 to syntax errors. The rules identified the error, when the mistake was made and why the student made the mistake. The methodology uses an architecture based on the fundamentals of the programming language and can therefore be adapted for use in another context.
Principles of Place: Developing a Place-Based Ethic for Discussing, Debating, and Anticipating Technical Communication Concerns

Background: This paper offers a hybrid, place-based ethic drawn from multiple perspectives as a way to reinvigorate ethical thought for technical communicators. Literature review: Aldo Leopold's land ethic asks us to consider actions beyond our immediate surroundings. Martin Buber's dialogic ethics complement a land ethic and interrogate interpersonal communication. Anticipatory technology ethics recommends the integration of ethical discussions and decisions during the design phase of new technologies. Together, these three approaches inform a place-based ethic for technical communicators. Research questions: 1. How might we meaningfully merge the many ways that technical communicators from varying backgrounds approach ethics into a useful ethical model that considers human interaction, technological innovation, and physical place? 2. How might such a merged model, what we call a place-based ethic, affect technical communication design? Methods: We analyze cases including documents from radical environmental defense groups, a restyling of certain federal court rules from legalese into plainer language, the creation of mortgage documents suitable for consumers and industry professionals, and the action-research design phase of a locative mobile application about public art. Results and conclusion: The cases provide concrete examples of the components of a place-based ethic, and we conclude that designing with a place-based ethic includes actively acknowledging the value of the environment, seeking areas for dialogue among involved parties and celebrating dialogue where it occurs, seeking shared spaces, clearly stating anticipated outcomes, and usability testing for potential ethical issues.
Face alignment by Component Adaptive Mechanism

The new cascaded shape regression architecture proposed in this paper is actually an algorithm by Component Adaptive Mechanism (CAM) to cope with unconstrained face alignment. CAM divides the process of face alignment into two parts: the updating process of face box and the cascaded shape regression process by Component Adaptive Mechanism. The former process first adjusts face box by training different classifiers to estimate its transformation parameters and thereby outputs more accurate initialized shape. The latter process uses Component Adaptive Mechanism to fuse results of different domain-specific regressors to further update the shape. The major innovation of CAM is characterized by its fault-tolerated mechanism which is shown in the following two aspects. (1) A probability-based fern classifier is adopted in the partition of the optimization space into multiple domains of homogeneous descent, which not only endows the algorithm with the fault-tolerance mechanism but also augments the available training set of each domain. (2) A training strategy based on dominant set approach is used to train a stronger domain-specific regressor by dynamically adjusting the weight of objective function corresponding to different shapes and therefore regressors derived from the training are equipped with fault-tolerance ability. Conducted on such public image datasets as AFLW-full (19-pts), COFW (29-pts) and 300-W (68-pts), experiments show that the proposed CAM: (1) can deal with the problem of face detection and face alignment simultaneously; (2) is superior to existing algorithms in solving face alignment problems with extreme variations in pose, expression, illumination and partial occlusion.
Promising Accurate Prefix Boosting for Sequence-to-sequence ASR

In this paper, we present promising accurate prefix boosting (PAPB), a discriminative training technique for attention based sequence-to-sequence (seq2seq) ASR. PAPB is devised to unify the training and testing scheme effectively. The training procedure involves maximizing the score of each partial correct sequence obtained during beam search compared to other hypotheses. The training objective also includes minimization of token (character) error rate. PAPB shows its efficacy by achieving 10.8% and 3.8% WER with and without external RNNLM respectively on Wall Street Journal dataset.
The transformation of the k-Shortest Steiner trees search problem into binary dynamic problem for effective evolutionary methods application

Evolutionary methods are well-known tools used for solving hard computational problems. In this paper, we consider k-Shortest Steiner Trees (kSST) problem appearing in a diverse set of domains, e.g., multicast tree construction in communication networks in general, and optical networks in particular. The kSST is relatively new and has not been widely investigated in the literature. Thus, only a few algorithms have been proposed, each requiring significant resources amount and long execution times, partially following from the NP-hard nature of the problem. The kSST problem solution is a set of different trees, where each single tree can be easily represented using a genotype-encoding. However, encoding the tree set may be challenging, as each tree must be unique. Especially, in most applications the number of trees is large, resulting with the genotype containing high number of necessary genes. Thus, in this paper, we propose a transformation of the kSST problem into a dynamic problem, and when applied in the evolutionary method, a single individual encodes a single tree instead of a whole tree set. The results of extensive numerical experiments executed on two representative network topologies show that the proposed transformation improves the effectiveness of all considered state-of-the-art evolutionary methods.
A sparse robust model for large scale multi-class classification based on K-SVCR

K-Support Vector Classification Regression (K-SVCR) is a multi-classification method based on "1-vs-1-vs-rest" structure, which provides better forecasting results since all the data are given full consideration while the centre of attention is a two-class partition. However, K-SVCR is not only sensitive to outliers but also time consuming. In this paper, we propose a robust least-squares version of K-SVCR (K-RLSSVCR) based on squares ε-insensitive ramp loss and truncated least squares loss, which partially depress the impact of outliers on the new model via its nonconvex ε-insensitive ramp loss and truncated least squares loss. With Concave-Convex Procedure (CCP), the solution of K-RLSSVCR is reduced to solving only a system of linear equations per iteration. For training large-scale problems, we derive an equivalent K-RLSSVCR model in primal space (Primal K-RLSSVCR) by the representer theorem, which may have a sparse solution if the corresponding kernel matrix has a low rank. We design a sparse K-RLSSVCR (K-SRLSSVCR) algorithm to achieve a sparse solution of the Primal K-RLSSVCR based on approximating the kernel matrix by a low-rank matrix. Experimental results on artificial data set and benchmark data sets show that the proposed method has better or comparable performance in classification to other related algorithms but with remarkably less training time and memory consumption, especially when used to train large-scale problems.
Multi-cue fusion: Discriminative enhancing for person re-identification

Person re-identification is an emerging research field in computer vision. Our paper aims to study how to improve the discrimination of person features. We find that some peculiarities of people have not been better attention in the semantic features of deep learning. However, some features obtained by traditional methods can better express the color, and these features are an important clue for re-identification. Therefore, in this paper, we combine traditional Gaussian features with deep semantic features to enhance the discrimination of overall features. At last, we have achieved good performance on two public datasets (Market1501 and VIPeR) in three main distance method learning (DML). In addition, we applied this model to the task of vehicle re-identification. Experiments show that our method has a great improvement on the VeRi vehicle dataset. We compare the results with the current high level results, which indicates the effectiveness of our model.
Adaptive shrinkage on dual-tree complex wavelet transform for denoising real-time MR images

Performance of denoising filters which are based on the principle of wavelet thresholding greatly depends upon selection of the threshold value. An objective method is proposed in this paper for computing the optimum value of threshold in DTCWT based denoising. At optimum threshold, annoying intensity transitions of pixels in the homogeneous regions of the images, contributed by noise get completely suppressed and the true edges remain unaffected. For finding optimum value of threshold a newly derived quality metric termed as Optimum Denoising Index (ODI), which quantifies both the edge-preservation and smoothing of homogeneous regions is used. The ODI values corresponding to mean, median, Gaussian, Wiener, Bilateral, Kuwahara filters and wavelet thresholding are 0.1192 ± 0.0118, 0.2196 ± 0.0125, 0.1283 ± 0.0118, 0.2106 ± 0.0145, 0.1590 ± 0.0331, 0.2200 ± 0.0101 and 0.2516 ± 0.0094, respectively. The wavelet thresholding has better edge-preservation and denoising capacity than the said denoising schemes. The ODI is highly correlated with its existing alternatives like Peak Signal to Noise Ratio (PSNR) and Structured Similarity Index Metric (SSIM) with values 0.9165 ± 0.0536 and 0.9050 ± 0.0452 respectively. This shows ODI is a good alternative to PSNR and SSIM.