Reviewerly/exHarmony huggingface dataset:
CoHA-NTT: A Configurable Hardware Accelerator for NTT-based Polynomial Multiplication

In this paper, we introduce a configurable hardware architecture that can be used to generate unified and parametric NTT-based polynomial multipliers that support a wide range of parameters of lattice-based cryptographic schemes proposed for post-quantum cryptography. Both NTT and inverse NTT operations can be performed using the unified butterfly unit of our architecture, which constitutes the core building block in NTT operations. The multitude of this unit plays an essential role in achieving the performance goals of a specific application area or platform. To this end, the architecture takes the size of butterfly units as input and generates an efficient NTT-based polynomial multiplier hardware to achieve the desired throughput and area requirements. More specifically, the proposed hardware architecture provides run-time configurability for the scheme parameters and compile-time configurability for throughput and area requirements. This work presents the first architecture with both run-time and compile-time configurability for NTT-based polynomial multiplication operations to the best of our knowledge. The implementation results indicate that the advanced configurability has a negligible impact on the time and area of the proposed architecture and that its performance is on par with the state-of-the-art implementations in the literature, if not better. The proposed architecture comprises various sub-blocks such as modular multiplier and butterfly units, each of which can be of interest on its own for accelerating lattice-based cryptography. Thus, we provide the design rationale of each sub-block and compare it with those in the literature, including our earlier works in terms of configurability and performance.
A new method to build the adaptive k-nearest neighbors similarity graph matrix for spectral clustering

In spectral clustering (SC), the clustering result highly depends on the similarity graph matrix. The k-nearest neighbors graph is a popular method to build the similarity graph matrix with a sparse structure for better graph cutting. However, many current methods require that the parameter k is specified by the user, and specifying an appropriate k for an unknown data set is often a difficult task. In this paper, we propose a new method for building the adaptive k-nearest neighbors similarity graph (AKNNG). The AKNNG specifies different k values for different data points to obtain a better graph structure. Specifically, it sets a maximum number of the nearest neighbors kmax and assigns a different k value (k⩽kmax) for each data point. The k value is adjusted automatically by cutting some weak connections from each data point according to the m powers transform of the similarity graph. The experimental results on Spiral, Multi-clusters, Yale and Coil20 datasets have shown that when setting kmax=20, the new method has improved the clustering accuracies of these four datasets over 4%, 6%, 4%, 5%, respectively, in comparison with those by the existing methods. The new method can also reduce the sensitiveness of the number of nearest neighbors, and build the similarity graph with less time.
A Drug Recommendation Model Based on Message Propagation and DDI Gating Mechanism

Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models.
Discriminative Style Learning for Cross-Domain Image Captioning

The cross-domain image captioning, which is trained on a source domain and generalized to other domains, usually faces the large domain shift problem. Although prior work has attempted to leverage both paired source and unpaired target data to minimize this shift, the performance is still unsatisfactory. One main reason lies in the large discrepancy in language expression between two domains, where diverse language styles are adopted to describe an image from different views, resulting in different semantic descriptions for an image. To tackle this problem, this paper proposes a Style-based Cross-domain Image Captioner (SCIC) which incorporates the discriminative style information into the encoder-decoder framework, and interprets an image as a special sentence according to external style instructions. Technically, we design a novel "Instruction-based LSTM", which adds the instruct gate to collect a style instruction, and then outputs a specified format according to that instruction. Two objectives are designed to train I-LSTM: 1) generating correct image descriptions and 2) generating correct styles, thus the model is expected to accurately capture the semantic meanings of an image by the special caption as well as understand the syntactic structure of the caption. We use MS-COCO as the source domain, and Oxford-102, CUB-200, Flickr30k as the target domains. Experimental results demonstrate that our model consistently outperforms the previous methods, and the style information incorporating with I-LSTM significantly improves the performance, with 5% CIDEr improvements at least on all datasets.
Transverse Mode-Encoded Quantum Gate on a Silicon Photonic Chip

As an important degree of freedom (d.o.f.) in photonic integrated circuits, the orthogonal transverse mode provides a promising and flexible way to increase communication capability, for both classical and quantum information processing. To construct large-scale on-chip multimode multi-d.o.f.s quantum systems, a transverse mode-encoded controlled-NOT (CNOT) gate is necessary. Here, with the help of our new transverse mode-dependent directional coupler and attenuator, we demonstrate the first multimode implementation of a 2-qubit quantum gate. The ability of the gate is demonstrated by entangling two separated transverse mode qubits with an average fidelity of 0.89±0.02 and the achievement of 10 standard deviations of violations in the quantum nonlocality verification. In addition, a fidelity of 0.82±0.01 is obtained from quantum process tomography used to completely characterize the CNOT gate. Our work paves the way for universal transverse mode-encoded quantum operations and large-scale multimode multi-d.o.f.s quantum systems.
QoS-Aware Cloud Resource Prediction for Computing Services

Computing services are increasingly located in computing clouds, which allows for on-demand scalability but may also increase operating costs. It is believed that cloud expenses constitute a significant budget item in companies of all sizes. There is a considerable body of work dedicated to reducing the costs of cloud computing, which is mainly focused on optimizing the use of cloud resources. Such optimization, however, tends to result in the deterioration of computing service responsiveness and, as a result, quality of service parameters, especially when applied to real-world, noisy data which include anomalies. This article presents a novel approach which involves a six-stage optimization process incorporating load prediction supported by machine learning, the discovery of computing service characteristics and long-term planning of resource usage alongside anomaly detection and continuous monitoring with a self-adapting ability. The solution proposed works autonomously, builds knowledge about the optimized system and its load patterns, calculates cost-optimal resource provisioning plans and adapts to rapid environmental changes. Our evaluation using Microsoft’s Azure cloud environment demonstrates savings ranging from 31% to 89% depending on the test scenario; cost reductions for other cloud computing providers were estimated as well.
POI Recommendation System using Hypergraph Embedding and Logical Matrix Factorization

Aiming at the problems of inaccurate recommendation and single consideration in the traditional Points of Interest (POI) recommendation model, a POI Recommendation System using Hypergraph Embedding and Logical Matrix Factorization (HE-LMF) has been proposed. The user's check-in points of interest and time information are sampled by hypergraph embedding technology, and users with similar points of interest to the target user are found, and their points of interest are recommended to the target user. At the same time, through the geographic recommendation model based on logical matrix decomposition, the regions with many user check-in times and the correlation of each region are considered. The results of the two models are weighted, and top-k is selected to recommend to the user. Finally, experiments are carried out on the two datasets of gowalla and foursquare, and compared with the three models USG, PFMMGM and LRT. The experimental results show that the HE-LMF algorithm can effectively improve the accuracy and recall rate of POI recommendation.
Ranking Distance Calibration for Cross-Domain Few-Shot Learning

Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are in different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more information in the target domain rather than to overly elaborate training strategies on the source domain as in many existing methods. Hence, we start from a generic representation pre-trained by a cross-entropy loss and a conventional distance-based classifier, along with an image retrieval view, to employ a re-ranking process to calibrate a target distance matrix by discovering the k-reciprocal neighbours within the task. Assuming the pre-trained representation is biased towards the source, we construct a non-linear subspace to minimise task-irrelevant features therewithin while keep more transferrable discriminative information by a hyperbolic tangent transformation. The calibrated distance in this target-aware non-linear sub-space is complementary to that in the pre-trained representation. To impose such distance calibration information onto the pre-trained representation, a Kullback-Leibler divergence loss is employed to gradually guide the model towards the calibrated distance-based distribution. Extensive evaluations on eight target domains show that this target ranking calibration process can improve conventional distance-based classifiers in few-shot learning.
Simultaneous Depth Estimation and Surgical Tool Segmentation in Laparoscopic Images

Surgical instrument segmentation and depth estimation are crucial steps to improve autonomy in robotic surgery. Most recent works treat these problems separately, making the deployment challenging. In this paper, we propose a unified framework for depth estimation and surgical tool segmentation in laparoscopic images. The network has an encoder-decoder architecture and comprises two branches for simultaneously performing depth estimation and segmentation. To train the network end to end, we propose a new multi-task loss function that effectively learns to estimate depth in an unsupervised manner, while requiring only semi-ground truth for surgical tool segmentation. We conducted extensive experiments on different datasets to validate these findings. The results showed that the end-to-end network successfully improved the state-of-the-art for both tasks while reducing the complexity during their deployment.
Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning

Few-shot learning is a fundamental and challenging problem since it requires recognizing novel categories from only a few examples. The objects for recognition have multiple variants and can locate anywhere in images. Directly comparing query images with example images can not handle content misalignment. The representation and metric for comparison are critical but challenging to learn due to the scarcity and wide variation of the samples in few-shot learning. In this paper, we present a novel semantic alignment model to compare relations, which is robust to content misalignment. We propose to add two key ingredients to existing few-shot learning frameworks for better feature and metric learning ability. First, we introduce a semantic alignment loss to align the relation statistics of the features from samples that belong to the same category. And second, local and global mutual information maximization is introduced, allowing for representations that contain locally-consistent and intra-class shared information across structural locations in an image. Furthermore, we introduce a principled approach to weigh multiple loss functions by considering the homoscedastic uncertainty of each stream. We conduct extensive experiments on several few-shot learning datasets. Experimental results show that the proposed method is capable of comparing relations with semantic alignment strategies, and achieves state-of-the-art performance.
Semantic Property Graph for Scalable Knowledge Graph Analytics

Graphs are a natural and fundamental representation to describe entities, relationships, activities, and evolution of complex systems. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used graph-based data models to encode information. Both the models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of the modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model to perform scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPG lacks the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We introduce Semantic Property Graph (SPG) as a logical projection of reified RDF into the LPG model. SPG continues to use RDF ontology to define the type hierarchy of the projected graph and validate it against a given ontology. We present the projection rules and a cloud-scale framework to convert reified RDF into SPG. We also present examples to demonstrate lossless SPG conversion for the subgraph matching application.
Noise free defect detection in ceramic tableware using a portable digital holographic camera

This paper describes detection of cracks in ceramic tableware using a portable digital holographic camera working on the principle of double-exposure digital holographic interferometry. Digital image processing techniques are applied on amplitude and phase information obtained from the numerically reconstructed wavefronts of the test object to locate position of the defect. Speckles generated due to illumination of the rough surface of the ceramic cup with the coherent light source deteriorate quality of the interferometric phase. Several iterations of the averaging filter with optimized kernel size are applied on sine and cosine components of the interferometric phase to minimize the speckle noise. The effect of kernel size of the matrix used in the averaging filter on quality of the interferometric phase is analyzed by evaluating the signal-to-noise ratio. The experimentally obtained size of crack on a ceramic cup (0.08 mm) is validated by a mechanical profiler with an error of 6.6%. This study may help in improving the quality criteria of tableware items.
Predicting Subjective Discomfort Associated With Lens Distortion in VR Headsets During Vestibulo-Ocular Response to VR Scenes

With advances in Virtual Reality (VR) technology, user expectation for a near-perfect experience is also increasing. The push for a wider field-of-view can increase the challenges of correcting lens distortion. Past studies on imperfect VR experiences have focused on motion sickness provoked by vection-inducing VR stimuli and discomfort due to mismatches in accommodation and binocular convergence. Disorientation and discomfort due to unintended optical flow induced by lens distortion, referred to as dynamic distortion (DD), has, to date, received little attention. This study examines and models the effects of DD during head rotations with various fixed gazes stabilized by vestibulo-ocular reflex (VOR). Increases in DD levels comparable to lens parameters from poorly designed commercial VR lenses significantly increase discomfort scores of viewers in relation to disorientation, dizziness, and eye strain. Cross-validated results indicate that the model is able to predict significant differences in subjective scores resulting from different commercial VR lenses and these predictions correlated with empirical data. The present work provides new insights to understand symptoms of discomfort in VR during user interactions with static world-locked / space-stabilized scenes and contributes to the design of discomfort-free VR headset lenses.
On the combined effect of class imbalance and concept complexity in deep learning

Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions. When these effects were uncovered in the early 2000s, understandably, the classifiers on which they were demonstrated belonged to the classical rather than Deep Learning categories of approaches. As Deep Learning is gaining ground over classical machine learning and is beginning to be used in critical applied settings, it is important to assess systematically how well they respond to the kind of challenges their classical counterparts have struggled with in the past two decades. The purpose of this paper is to study the behavior of deep learning systems in settings that have previously been deemed challenging to classical machine learning systems to find out whether the depth of the systems is an asset in such settings. The results in both artificial and real-world image datasets show that these settings remain mostly challenging for Deep Learning systems. Deeper architectures help with structural concept complexity but not with data scarcity and class overlap.
Touch and Beyond: Comparing Physical and Virtual Reality Visualizations

We compare physical and virtual reality (VR) versions of simple data visualizations and explore how the addition of virtual annotation and filtering tools affects how viewers solve basic data analysis tasks. We report on two studies, inspired by previous examinations of data physicalizations. The first study examines differences in how viewers interact with physical hand-scale, virtual hand-scale, and virtual table-scale visualizations and the impact that the different forms had on viewer's problem solving behavior. A second study examines how interactive annotation and filtering tools might support new modes of use that transcend the limitations of physical representations. Our results highlight challenges associated with virtual reality representations and hint at the potential of interactive annotation and filtering tools in VR visualizations.
Construction of Verifier Combinations Based on Off-the-Shelf Verifiers

Abstract Software verifiers have different strengths and weaknesses, depending on properties of the verification task. It is well-known that combinations of verifiers via portfolio and selection approaches can help to combine the strengths. In this paper, we investigate (a) how to easily compose such combinations from existing , ‘off-the-shelf’ verification tools without changing them and (b) how much performance improvement easy combinations can yield, regarding the effectiveness (number of solved problems) and efficiency (consumed resources). First, we contribute a method to systematically and conveniently construct verifier combinations from existing tools, using the composition framework CoVeriTeam . We consider sequential portfolios, parallel portfolios, and algorithm selections. Second, we perform a large experiment on 8 883 verification tasks to show that combinations can improve the verification results without additional computational resources. All combinations are constructed from off-the-shelf verifiers, that is, we use them as published. The result of our work suggests that users of verification tools can achieve a significant improvement at a negligible cost (only configure our composition scripts).
Outlier detection algorithm based on k-nearest neighbors-local outlier factor

The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the limitations of the existing outlier detection methods, we propose a novel outlier detection algorithm which is named as kNN-LOF. First, the k-nearest neighbors algorithm is applied to divide different areas for outlier attributes, which is more suitable for outlier detection in different density distributions. Secondly, a hierarchical adjacency order is proposed to hierarchize the neighborhood range according to the link distance. The average sequence distance is calculated from the data objects in the hierarchy, and the reachable distance of an object is redefined to introduce a new local outlier factor. Experimental results show that the proposed algorithm has good performance in improving the accuracy of outlier detection.