Reviewerly/exHarmony huggingface dataset:
Constrained Domain Adaptation for Segmentation

We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions. Such knowledge may take the form of simple anatomical information, e.g., structure size or shape, estimated from source samples or known a priori. Our method imposes domain-invariant inequality constraints on a network output of unlabeled target samples. It implicitly matches prediction statistics between target and source domains with permitted uncertainty of prior knowledge. We address our constrained problem with a differentiable penalty, fully suited for conventional gradient descent approaches, removing the need for computationally expensive Lagrangian optimization with dual projections. Unlike current two-step adversarial training, our formulation is based on a single loss in a single network, which simplifies adaptation by avoiding extra adversarial steps, while improving convergence and quality of training. The comparison of our approach with state-of-the-art adversarial methods reveals substantially better performance on the challenging task of adapting spine segmentation across different MRI modalities. Our results also show a robustness to imprecision of size priors, approaching the accuracy of a fully supervised model trained directly in a target domain. Our method can be readily used for various constraints and segmentation problems.
Analysis of Spatial and Temporal Information Variation for 10-Bit and 8-Bit Video Sequences

Spatial Information (SI) and Temporal Information (TI) have been used widely as an approximate estimation of video complexity. Recently, SI (and TI) have found use in many other applications such as Quality of Experience modeling, Bandwidth and Rate-distortion modeling, etc., for both traditional and non-traditional (gaming, dynamic vision sensors, etc.) videos. It is often assumed that SI and TI only depend on video content, while instead factors such as resolution, bit depth, compression have an impact on the values of SI and TI for a specific video content. A systematic study on SI and TI for videos, investigating the effect of different video encoding and processing steps on SI and TI values has been missing so far. Also, SI and TI calculation has been limited to 8-bit videos, while there has been increasing popularity and usage of 10-bit videos. Towards this end, we present in this paper a comprehensive evaluation of the variation of SI and TI for different 8-bit and 10-bit videos. Results and insights into the variation of SI and TI values for different encoding settings, choice of encoders, temporal pooling methods, resolution, etc. are presented in this study.
Improved Self-adaptive Search Equation-based Artificial Bee Colony Algorithm with competitive local search strategy

The search equations and the local search procedures used in Artificial Bee Colony (ABC) algorithm are two key components that affect the performance of the algorithm. However, there is no search equation or local search that provides good results for all problem types. In this article, an ABC algorithm called “Self-adaptive Search Equation-based Artificial Bee Colony” (SSEABC) is proposed which can determine the appropriate local search procedure and the search equation internally during execution. SSEABC integrates three strategies into the canonical ABC algorithm. The first strategy is a self-adaptive strategy that determines the appropriate search equations for a particular problem by eliminating improper ones from a pool consisting of randomly generated search equations. The second strategy is a competitive local search selection. It decides the most effective local search procedure by comparing the performances of SSEABC, Mtsls1 and IPOP-CMA-ES. The third strategy is an incremental population size strategy, which is based on adding new food sources located around the best-so-far food source position after a predefined number of iterations. This helps to increase convergence speed. The SSEABC algorithm is tested on benchmark functions proposed in the CEC'14 abd CEC'17 competition on single objective bound constrained real-parameter numerical optimization. SSEABC is compared with several ABC variants, competitor algorithms of CEC'14 and CEC'17, and several state-of-the-art algorithms. Finally, we applied SSEABC to the infinite impulse response (IIR) system identification problem as an engineering application. The results showed the superiority of the SSEABC algorithm.
Boosting Search Performance Using Query Variations

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. However, to date fusion has not been regarded as being cost-effective in cases where strict per-query efficiency guarantees are required, such as in web search. In this work we propose a novel solution to rank fusion by splitting the computation into two parts -- one phase that is carried out offline to generate pre-computed centroid answers for queries with broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. We explore efficiency improvements to classic fusion algorithms whose costs can be amortized as a pre-processing step, and can then be combined with re-ranking approaches to dramatically improve effectiveness in multi-stage retrieval systems with little efficiency overhead at query time. Experimental results using the ClueWeb12B collection and the UQV100 query variations demonstrate that centroid-based approaches allow improved retrieval effectiveness at little or no loss in query throughput or latency, and with reasonable pre-processing requirements. We additionally show that queries that do not match any of the pre-computed clusters can be accurately identified and efficiently processed in our proposed ranking pipeline.
A roadmap for heritage language research

Our keynote, “Understanding heritage languages” (Polinsky &amp; Scontras, 2019), and the commentaries to it make the following main points.
An Integrated Privacy Preserving Attribute-Based Access Control Framework Supporting Secure Deduplication

Recent advances in information technologies have facilitated applications to generate, collect or process large amounts of sensitive personal data. Emerging cloud storage services provide a better paradigm to support the needs of such applications. Such cloud based solutions introduce additional security and privacy challenges when dealing with outsourced data including that of supporting fine-grained access control over such data stored in the cloud. In this paper, we propose an integrated, privacy-preserving user-centric attribute based access control framework to ensure the security and privacy of users' data outsourced and stored by a cloud service provider (CSP). The core component of the proposed framework is a novel privacy-preserving, revocable ciphertext policy attribute-based encryption (PR-CP-ABE) scheme. To support advanced access control features like write access on encrypted data and privacy-preserving access policy updates, we propose extended Path-ORAM access protocol that can also prevent privacy disclosure of access patterns. We also propose an integrated secure deduplication approach to improve the storage efficiency of CSPs while protecting data privacy. Finally, we evaluate the proposed framework and compare it with other existing solutions with regards to the security and performance issues.
Real-time Vision-based Depth Reconstruction with NVidia Jetson

Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution. Reconstructing depth from single image is particularly valuable to mobile robotics as it can be embedded to the modern vision-based simultaneous localization and mapping (vSLAM) methods providing them with the metric information needed to construct accurate maps in real scale. Typically, depth reconstruction is done nowadays via fully-convolutional neural networks (FCNNs). In this work we experiment with several FCNN architectures and introduce a few enhancements aimed at increasing both the effectiveness and the efficiency of the inference. We experimentally determine the solution that provides the best performance/accuracy tradeoff and is able to run on NVidia Jetson with the framerates exceeding 16FPS for 320 × 240 input. We also evaluate the suggested models by conducting monocular vSLAM of unknown indoor environment on NVidia Jetson TX2 in real-time. Open-source implementation of the models and the inference node for Robot Operating System (ROS) are available at https://github.com/CnnDepth/tx2_fcnn_node.
RDF Explorer: A Visual SPARQL Query Builder

Despite the growing popularity of knowledge graphs for managing diverse data at large scale, users who wish to pose expressive queries against such graphs are often expected to know (i) how to formulate queries in a language such as SPARQL, and (ii) how entities of interest are described in the graph. In this paper we propose a language that relaxes these expectations; the language's operators are based on an interactive graph-based exploration that allows non-expert users to simultaneously navigate and query knowledge graphs; we compare the expressivity of this language with SPARQL. We then discuss an implementation of this language that we call RDF Explorer and discuss various desirable properties it has, such as avoiding interactions that lead to empty results. Through a user study over the Wikidata knowledge-graph, we show that users successfully complete more tasks with RDF Explorer than with the existing Wikidata Query Helper, while a usability questionnaire demonstrates that users generally prefer our tool and self-report lower levels of frustration and mental effort.
Evaluating the performance of face sketch generation using generative adversarial networks

One of the most significant and widely used methods for identifying a culprit in the field of forensic science is generating a sketch of the suspect from descriptions given by an eyewitness to the crime. However, there is a high level of uncertainty in recognizing an individual solely from a sketch. Recognition of sketches based on photos of an individual is non-trivial, as there are differences between the domain features of a sketch and a photo. To streamline this process, this article presents a methodology for generating a colored photo from a sketch, which can then be used for identification using a variety of classification techniques. Implementation of the proposed method involves a trained Convolution Neural Network for sketch generation paired with a conditional Generative Adversarial Network's pix2pix model for color photo generation. Experimental results of the work are validated using standard datasets, and the proposed model achieved a minimum average rate of 65% similarity index value on all employed datasets with a training efficiency of more than 98% in every epoch level.
Large Scale Shadow Annotation and Detection Using Lazy Annotation and Stacked CNNs

Recent shadow detection algorithms have shown initial success on small datasets of images from specific domains. However, shadow detection on broader image domains is still challenging due to the lack of annotated training data, caused by the intense manual labor required for annotating shadow data. In this paper we propose “lazy annotation”, an efficient annotation method where an annotator only needs to mark the important shadow areas and some non-shadow areas. This yields data with noisy labels that are not yet useful for training a shadow detector. We address the problem of label noise by jointly learning a shadow region classifier and recovering the labels in the training set. We consider the training labels as unknowns and formulate label recovery as the minimization of the sum of squared leave-one-out errors of a Least Squares SVM, which can be efficiently optimized. Experimental results show that a classifier trained with recovered labels achieves comparable performance to a classifier trained on the properly annotated data. These results motivated us to collect a new dataset that is 20 times larger than existing datasets and contains a large variety of scenes and image types. Naturally, such a large dataset is appropriate for training deep learning methods. Thus, we propose a stacked Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information. This means that the detected shadow patches are refined based on image semantics. Our proposed pipeline, trained on recovered labels, performs at state-of-the art level. Furthermore, the proposed model performs exceptionally well on a cross dataset task, proving the generalization power of the proposed architecture and dataset.
A survey on different human-machine interactions used for controlling an electric wheelchair

Nowadays, enhanced mobility is a major challenge in the public healthcare sector, especially for the elderly and disabled persons living alone. Therefore, the demand for assistance systems as smart wheelchairs, especially for patients who are not able to walk normally because of some injury, has considerably increased within the healthcare industry. The power of modern computer systems has created new opportunities for human-machine interaction researchers to expand the capabilities of conventional wheelchairs by introducing specialized interfaces to enable the user to emit a command perceptible and understandable by a machine. In this paper, we present a survey on different human-machine interactions used for controlling an electric wheelchair and we demonstrate the effectiveness of our interface based on human facial expressions.
On Optimality of CSS Codes for Transversal <i>T</i>

In order to perform universal fault-tolerant quantum computation, one needs to implement a logical non-Clifford gate. Consequently, it is important to understand codes that implement such gates transversally. In this paper, we adopt an algebraic approach to characterize all stabilizer codes for which transversal $T$ and $T^{-1}$ gates preserve the codespace. Our Heisenberg perspective reduces this to a finite geometry problem that translates to the design of certain classical codes. We prove three corollaries: (a) For any non-degenerate $[[ n,k,d ]]$ stabilizer code supporting a physical transversal $T$, there exists an $[[ n,k,d ]]$ CSS code with the same property; (b) Triorthogonal codes are the most general CSS codes that realize logical transversal $T$ via physical transversal $T$; (c) Triorthogonality is necessary for physical transversal $T$ on a CSS code to realize the logical identity. The main tool we use is a recent efficient characterization of certain diagonal gates in the Clifford hierarchy (arXiv:1902.04022). We refer to these gates as Quadratic Form Diagonal (QFD) gates. Our framework generalizes all existing code constructions that realize logical gates via transversal $T$. We provide several examples and briefly discuss connections to decreasing monomial codes, pin codes, generalized triorthogonality and quasitransversality. We partially extend these results towards characterizing all stabilizer codes that support transversal $\pi/2^{\ell}$ $Z$-rotations. In particular, using Ax's theorem on residue weights of polynomials, we provide an alternate characterization of logical gates induced by transversal $\pi/2^{\ell}$ $Z$-rotations on a family of quantum Reed-Muller codes. We also briefly discuss a general approach to analyze QFD gates that might lead to a characterization of all stabilizer codes that support any given physical transversal $1$- or $2$-local diagonal gate.
Intelligent optimization algorithms for the problem of mining numerical association rules

There are many effective approaches that have been proposed for association rules mining (ARM) on binary or discrete-valued data. However, in many real-world applications, the data usually consist of numerical values and the standard algorithms cannot work or give promising results on these datasets. In numerical ARM (NARM), it is a difficult problem to determine which attributes will be included in the rules to be discovered and which ones will be on the left of the rule and which ones on the right. It is also difficult to automatically adjusting of most relevant ranges for numerical attributes. Directly discovering the rules without generating the frequent itemsets as used in the literature as the first step of ARM accelerates the whole process without determining the metrics needed for this step. In classical ARM algorithms, generally one or two metrics are considered. However, mined rules are needed to be comprehensible, surprising, interesting, accurate, confidential, and etc. in many real-world applications. Adjusting all of these processes without the need for the metrics to be pre-determined for each dataset seems another problem. For these purposes, evolutionary intelligent optimization algorithms seem potential solution method for this complex problem. In this paper, the performance analysis of seven evolutionary algorithms and fuzzy evolutionary algorithms; namely Alatasetal, Alcalaetal, EARMGA, GAR, GENAR, Genetic Fuzzy Apriori, and Genetic Fuzzy AprioriDC for NARM problem has been performed within eleven real datasets for the first time. The obtained results have also been compared with the classical Apriori algorithm to show the efficiencies of the intelligent algorithms on NARM problem. Performances of eight algorithms in terms of support, confidence, number of mined rules, number of covered records, and time metrics have been comparatively performed with eleven real-world datasets. One of the best-mined rules obtained by each algorithm has been given and analyzed with respect to confidence, support, and lift metrics.
The fair value of a token: How do markets price cryptocurrencies?

With the rise of cryptocurrency tokens as a new asset class, the question of the fair evaluation of a cryptocurrency token has become a question of increasing importance. We estimate the pricing kernel with which users price factors affecting their token holdings. We investigate how traditional risk factors such as market risk are evaluated, as well as how blockchain specific risk factors are priced in. In order to do so, we introduce an asset pricing model and modify its properties to make it applicable to cryptocurrency markets. We group the risk factors into market related and Bitcoin- and Ethereum blockchain specific risk factors. We find that blockchain specific risk factors are priced in. There is evidence that risk factors have moved from Bitcoin to Ethereum specific risk factors with an increasing importance of market factors, providing evidence for a decoupling of on-chain and off-chain trading activity.
Blockchain-Enabled Charging Right Trading Among EV Charging Stations

Increasing penetration of electric vehicles (EVs) gives rise to the challenges in the secure operation of power systems. The EV charging loads should be distributed among charging stations in a fair and incentive-compatible manner while ensuring that power transmission and transformation facilities are not overloaded. This paper first proposes a charging right (or charging power ration) trading mechanism and model based on blockchain. Considering all kinds of random factors of charging station loads, we use Monte Carlo modeling to determine the charging demand of charging stations in the future. Based on the charging demand of charging stations, a charging station needs to submit the charging demand for a future period. The blockchain first distributes initial charging right in a just manner and ensures the security of facilities. Given that the charging urgency and elasticity differences vary by charging stations, all charging stations then proceed with double auction and peer-to-peer (P2P) transaction of charging right. Bids and offers are cleared via double auctions if bids are higher than offers. The remaining bids and offers are cleared via the P2P market. Then, this paper designs the charging right allocation and trading platform and smart contract based on the Ethernet blockchain to ensure the safety of the distribution network (DN) and the transparency and efficiency of charging right trading. Simulation results based on the Ethereum private blockchain show the fairness and efficiency of the proposed mechanism and the effectiveness of the method and the mechanism.
Multi-source transfer learning of time series in cyclical manufacturing

Abstract This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions. Graphic abstract
Unrolling Ternary Neural Networks

The computational complexity of neural networks for large-scale or real-time applications necessitates hardware acceleration. Most approaches assume that the network architecture and parameters are unknown at design time, permitting usage in a large number of applications. This article demonstrates, for the case where the neural network architecture and ternary weight values are known a priori , that extremely high throughput implementations of neural network inference can be made by customising the datapath and routing to remove unnecessary computations and data movement. This approach is ideally suited to FPGA implementations as a specialized implementation of a trained network improves efficiency while still retaining generality with the reconfigurability of an FPGA. A VGG-style network with ternary weights and fixed point activations is implemented for the CIFAR10 dataset on Amazon’s AWS F1 instance. This article demonstrates how to remove 90% of the operations in convolutional layers by exploiting sparsity and compile-time optimizations. The implementation in hardware achieves 90.9 ± 0.1% accuracy and 122k frames per second, with a latency of only 29µs, which is the fastest CNN inference implementation reported so far on an FPGA.
Deep Spherical Quantization for Image Search

Hashing methods, which encode high-dimensional images with compact discrete codes, have been widely applied to enhance large-scale image retrieval. In this paper, we put forward Deep Spherical Quantization (DSQ), a novel method to make deep convolutional neural networks generate supervised and compact binary codes for efficient image search. Our approach simultaneously learns a mapping that transforms the input images into a low-dimensional discriminative space, and quantizes the transformed data points using multi-codebook quantization. To eliminate the negative effect of norm variance on codebook learning, we force the network to L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> normalize the extracted features and then quantize the resulting vectors using a new supervised quantization technique specifically designed for points lying on a unit hypersphere. Furthermore, we introduce an easy-to-implement extension of our quantization technique that enforces sparsity on the codebooks. Extensive experiments demonstrate that DSQ and its sparse variant can generate semantically separable compact binary codes outperforming many state-of-the-art image retrieval methods on three benchmarks.