Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Collaborative Model Learning Scheme for E-Healthcare

With the advances of data mining and the pervasiveness of cloud computing, online medical diagnosis service has been extensively applied in e-heathcare field, and brought great conveniences to people's life. However, due to the insufficient data sharing among healthcare centers under the security and privacy concerns of medical information, the flourish of online medical diagnosis service still faces many severe challenges including diagnostic accuracy issues. In this paper, in order to address the security issues and improve the accuracy of online medical diagnosis service, we propose a new privacy-preserving collaborative model learning scheme with skyline computation, called PCML. With PCML, healthcare centers can securely learn a global diagnosis model with their local diagnosis models in the assistance of cloud, and the sensitive medical data of each healthcare center is well protected. Specifically, with a secure multi-party vector comparison algorithm (SMVC), all local diagnosis models are encrypted by their owners before being sent to the cloud, and can be directly operated without decryption. Detailed security analysis shows that PCML can resist security threats in the semi-honest model. Moreover, PCML is implemented with medical datasets from UCI machine learning repository, and extensive simulation results demonstrate that PCML is efficient and can be implemented effectively.
How Paternalistic Leadership Influences IT Security PolicyCompliance:The Mediating Role oftheSocial Bond

Leadership plays an important role in changing employees’ behavior. This paper aims to investigate the relationship between paternalistic leadership and employees’ information security policy (ISP) compliance. We adopt social bond theory as the theoretical lens to explain the effect of paternalistic leadership on ISP compliance through social bond formation. We developed a research model and tested it using data comprising 314 dyads of employees and their supervisors in organizations. The results show that all three dimensions of paternalistic leadership— benevolence, morality, and authoritarianism—positively influence employee ISP compliance. The social bond partially mediates the effects of benevolence and morality on compliance intention. Overall, this paper reveals the positive effect of paternalistic leadership in improving ISP compliance and the mediating role of the social bond in explaining the impact of paternalistic leadership on ISP compliance. In addition, the mediation effect of the social bond suggests that the non-IT related routine behavior of leaders can also affect employee ISP compliance through facilitating the formation of social bonds with and among employees.
On Explainable Flexible Fuzzy Recommender and Its Performance Evaluation Using the Akaike Information Criterion

In the paper, fuzzy recommender systems are proposed based on the novel method for nominal attribute coding. Several flexibility parameters - subjects to learning - are incorporated to their construction, allowing systems to better represent patterns encoded in data. The learning process does not affect the initial interpretable form of fuzzy recommenders rules. Using the Akaike Information Criterion allows evaluating the trade-off between a number of rules and interpretability which is crucial to provide proper explanations for users.
How to Develop a GDPR-Compliant Blockchain Solution for Cross-Organizational Workflow Management: Evidence from the German Asylum Procedure

Blockchain technology has the potential to resolve trust concerns in cross-organizational workflows and to reduce reliance on paper-based documents as trust anchors.Although these prospects are real, so is regulatory uncertainty.In particular, the reconciliation of blockchain with Europe's General Data Protection Regulation (GDPR) is proving to be a significant challenge.We tackled this challenge with the German Federal Office for Migration and Refugees.Here, we explain how we used Action Research to guide the Federal Office in creating a GDPR-compliant blockchain solution for the German asylum procedure.Moreover, we explain the architecture of the Federal Office's solution and present two design principles for developing GDPRcompliant blockchain solutions for crossorganizational workflow management.
You Only Train Once: Loss-Conditional Training of Deep Networks

In many machine learning problems, loss functions are weighted sums of several terms. A typical approach to dealing with these is to train multiple separate models with different selections of weights and then either choose the best one according to some criterion or keep multiple models if it is desirable to maintain a diverse set of solutions. This is inefficient both at training and at inference time. We propose a method that allows replacing multiple models trained on one loss function each by a single model trained on a distribution of losses. At test time a model trained this way can be conditioned to generate outputs corresponding to any loss from the training distribution of losses. We demonstrate this approach on three tasks with parametrized losses: beta-VAE, learned image compression, and fast style transfer.
Residual Energy-Based Models for Text Generation

Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models which predict one word at a time. While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process. In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level. In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation. Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa. Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines. Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.
DMRM: A Dual-Channel Multi-Hop Reasoning Model for Visual Dialog

Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image. It remains a challenging task since it requires the agent to fully understand a given question before making an appropriate response not only from the textual dialog history, but also from the visually-grounded information. While previous models typically leverage single-hop reasoning or single-channel reasoning to deal with this complex multimodal reasoning task, which is intuitively insufficient. In this paper, we thus propose a novel and more powerful Dual-channel Multi-hop Reasoning Model for Visual Dialog, named DMRM. DMRM synchronously captures information from the dialog history and the image to enrich the semantic representation of the question by exploiting dual-channel reasoning. Specifically, DMRM maintains a dual channel to obtain the question- and history-aware image features and the question- and image-aware dialog history features by a mulit-hop reasoning process in each channel. Additionally, we also design an effective multimodal attention to further enhance the decoder to generate more accurate responses. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that the proposed model is effective and outperforms compared models by a significant margin.
An Image Encryption Algorithm Based on Logistic-Fibonacci Cascade Chaos and 3D Bit Scrambling

Aiming at the problem that the existing bit scrambling encryption algorithm is not sensitive to the bit scrambling between 8 bits of one pixel, and the anti-noise and anti-selective plaintext attack ability is weak, a 3D cyclic shift bit scrambling image encryption algorithm is proposed in this paper. Discarding the scrambling mode of 8 bits of one pixel and the bits of all pixels, the pixel values are converted into binary arrays and then converted into 3D matrices in this paper. And the higher bit-planes and the lower bit-planes, which contain the plaintext information of different weights, are scrambled by cyclic shifting respectively. Therefore the sensitivity of the bit scrambling, the anti-noise attack ability of the algorithm and the randomness of intermediate ciphertext are improved. Moreover the randomness and the anti-noise ability can be adjusted by changing the number of higher bit-planes according to different encryption requirements. A new type of Logistic-Fibonacci (L-F) cascade chaos is constructed to generate random sequences, which solves the problem of blank windows in the uneven distribution of Logistic chaos. The initial value and the control parameters are increased, the sequence randomness is improved, and the fastness of low-dimensional chaos is preserved. By strongly correlating the key with SHA-256 of the plaintext, the key stream can change adaptively with the plaintext, which greatly improves the sensitivity of the plaintext and the ability of resisting the selective plaintext and ciphertext attack. The experiments show that the algorithm can encrypt all kinds of images with high efficiency, and can resist common attacks. It is a secure and reliable image encryption algorithm.
Multi-User Adaptive Video Delivery Over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach

In this paper, we investigate the adaptive video delivery for multiple users over time-varying and mutually interfering multi-cell wireless networks. The key research challenge is to jointly design the physical-layer resource allocation scheme and application-layer rate adaptation logic, such that the users' long-term fair quality of experience (QoE) can be maximized. Due to the timescale mismatch between these two layers and the asynchrony of user requests, however, it is difficult to directly model the cross-layer stochastic control problem by using a reinforcement learning framework. To address this difficulty, we propose a novel two-level decision framework where an optimization-based beamforming scheme (performed at the base stations) and a deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals) are, respectively, developed, such that a highly complex long-term multi-user QoE fairness problem is decomposed into some relatively simple problems and solved effectively. Our strategy represents a significant departure from the existing schemes with consideration of either a short-term multi-user QoE maximization or a long-term single-user point-to-point QoE maximization. Extensive simulations demonstrate that the proposed cross-layer design is effective and promising.
PermCNN: Energy-Efficient Convolutional Neural Network Hardware Architecture With Permuted Diagonal Structure

In the emerging artificial intelligence (AI) era, efficient hardware accelerator design for deep neural networks (DNNs) is very important to enable real-time energy-efficient DNN model deployment. To this end, various DNN model compression approaches and the corresponding hardware architectures have been intensively investigated. Recently, PermDNN, as a permuted diagonal structure-imposing model compression approach, was proposed with promising classification performance and hardware performance. However, the existing PermDNN hardware architecture is specifically designed for fully-connected (FC) layer-contained DNN models; while its support for convolutional (CONV) layer is missing. To fill this gap, this article proposes PermCNN, an energy-efficient hardware architecture for permuted diagonal structured convolutional neural networks (CNNs). By fully utilizing the strong structured sparsity in the trained models as well as dedicatedly leveraging the dynamic activation sparsity, PermCNN delivers very high hardware performance for inference tasks on CNN models. A design example with 28 nm CMOS technology shows that, compared the to state-of-the-art CNN accelerator, PermCNN achieves 3.74× and 3.11× improvement on area and energy efficiency, respectively, on AlexNet workload, and 17.49× and 14.22× improvement on area and energy efficiency, respectively, on VGG model. After including energy consumption incurred by DRAM access, PermCNN achieves 2.60× and 9.62× overall energy consumption improvement on AlexNet and VGG workloads, respectively.
Graph Laplacian Mixture Model

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph. This is, however, not always the case, as data is often available in mixed form, yielding the need for methods that are able to cope with mixture data and learn multiple graphs. We propose a novel generative model that represents a collection of distinct data which naturally live on different graphs. We assume the mapping of data to graphs is not known and investigate the problem of jointly clustering a set of data and learning a graph for each of the clusters. Experiments demonstrate promising performance in data clustering and multiple graph inference, and show desirable properties in terms of interpretability and coping with high dimensionality on weather and traffic data, as well as digit classification.
High-Throughput FPGA-Based Hardware Accelerators for Deflate Compression and Decompression Using High-Level Synthesis

The Deflate compression algorithm provides one of the most widely used solutions for lossless data compression.Field-programmable gate arrays (FPGAs) are commonly used to implement hardware accelerators that speed up computation-intensive applications.In this article, FPGA-based accelerators for Deflate compression and decompression are described.These accelerators were specified in C++ and synthesized using Vivado High-Level Synthesis (HLS) for a Xilinx Virtex UltraScale+ series FPGA and a system clock frequency of 250 MHz.The proposed compressor processes data at a fixed input throughput of 4.0 GB/s and achieves a geometric mean compression ratio of 1.92 on the Calgary corpus benchmark files using static Huffman encoding.While not the first compressor synthesized using high-level synthesis, our design achieves a 25% greater throughput and an 11% greater compression ratio than the only other published design that uses Vivado HLS.The proposed decompressor design achieves average input throughputs of 196.61 MB/s and 97.40 MB/s, for statically and dynamically encoded Calgary corpus files, respectively.This is the first published decompressor design that is synthesized using high-level synthesis and provides performance that is comparable to that of the best published designs, having static throughputs 11% higher and dynamic throughputs only 10% lower than the expertly-optimized design sold by Xilinx.
Automatic Segmentation Algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency

The emergence of 4D heart images makes the data volume of the images multiply.It is more urgent to require an effective and fast segmentation algorithm.Therefore, a heart image can be accurately segmented from a large amount of image data and an area of interest can be extracted The segmentation algorithm is very necessary.Based on the segmentation and recognition of medical images, this paper proposes a neural network and image saliency based on the obvious difference between the heart image and other tissues in the slice, and the high similarity between adjacent slices in the CT image sequence.Fully automatic segmentation algorithm and 3D visual reconstruction is the segmented heart image.Convolutional neural network is a special deep neural network model of artificial intelligence.Its connections between neurons are not fully connected.The weights of connections between certain neurons in the same layer are shared, and the network model is reduced.The complexity reduces the number of weights.The use of visual saliency techniques to achieve cardiac segmentation based on CT images.An image saliency detection algorithm is adopted to introduce the image segmentation algorithm based on the saliency technique.In this paper, considering the PET image as grayscale image with low resolution, an improved Itti model and an improved GrabCut image segmentation algorithm are proposed to solve the problem of the original algorithm in grayscale image.At the same time, the operation steps of the user division area are cancelled, and the automatic processing is realized, and the running time of the algorithm is improved while optimizing the image segmentation effect.The convolutional neural network is constructed to realize the positioning function of the heart in the image.The original cardiac CT image is cropped by the positioning result, and some non-target areas are removed.A stacking noise reduction self-coding network is constructed, and the network is manually segmented.Training, realize the classification and recognition of the pixels belonging to the heart tissue in the CT image of the heart, and finally realize the segmentation of the heart image based on the classification result.The results of the above segmentation algorithm are quantitatively evaluated and analyzed with the artificial segmentation results, and the segmentation results are visually reconstructed by surface rendering and volume rendering.The algorithm has better accuracy, reliability and higher.The segmentation efficiency is more simplified for user operations.
Distillation of Gaussian Einstein-Podolsky-Rosen steering with noiseless linear amplification

Abstract Einstein–Podolsky–Rosen (EPR) steering is one of the most intriguing features of quantum mechanics and an important resource for quantum communication. For practical applications, it remains a challenge to protect EPR steering from decoherence due to its intrinsic difference from entanglement. Here, we experimentally demonstrate the distillation of Gaussian EPR steering and entanglement in lossy and noisy environments using measurement-based noiseless linear amplification. Different from entanglement distillation, the extension of steerable region happens in the distillation of EPR steering, besides the enhancement of steerabilities. We demonstrate that the two-way or one-way steerable region is extended after the distillation of EPR steering when the NLA is implemented based on Bob’s or Alice’s measurement results. We also show that the NLA helps to extract the secret key from the insecure region in one-sided device-independent quantum key distribution with EPR steering. Our work paves the way for quantum communication exploiting EPR steering in practical quantum channels.
The impact of text preprocessing on the prediction of review ratings

With the increase of e-commerce platforms and online applications, businessmen are looking to have a rating and review system through which they can easily reveal the feelings of customers related to their products and services. It is undeniable from the statistics that online ratings and reviews attract new customers as well as increase sales by means of providing confidence, ratification, opinions, comparisons, merchant credibility, etc. Although considerable research has been devoted to the sentiment analysis for review classification, rather less attention has been paid to the text preprocessing which is a crucial step in opinion mining especially if convenient preprocessing strategies are found out to increase the classification accuracy. In this paper, we concentrate on the impact of simple text preprocessing decisions in order to predict fine-grained review rating stars whereas the majority of previous work focused on the binary distinction of positive vs. negative. Therefore, the aim of this research is to analyze preprocessing techniques and their influence, at the same time explain the interesting observations and results on the performance of a five-class-based review rating classifier.
Consistent Estimators for Learning to Defer to an Expert

Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however this fact is largely ignored when designing these algorithms. In this paper we explore how to learn predictors that can either predict or choose to defer the decision to a downstream expert. Given only samples of the expert's decisions, we give a procedure based on learning a classifier and a rejector and analyze it theoretically. Our approach is based on a novel reduction to cost sensitive learning where we give a consistent surrogate loss for cost sensitive learning that generalizes the cross entropy loss. We show the effectiveness of our approach on a variety of experimental tasks.
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions. This allows to make sequences of different length comparable and to rely on strong theoretical results from stochastic analysis. Signatures capture sequential structure with tensors that can scale unfavourably in sequence length and state space dimension. To deal with this, we introduce a sparse variational approach with inducing tensors. We then combine the resulting GP with LSTMs and GRUs to build larger models that leverage the strengths of each of these approaches and benchmark the resulting GPs on multivariate time series (TS) classification datasets.
On Variational Learning of Controllable Representations for Text without Supervision

The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.
The Implicit Regularization of Stochastic Gradient Flow for Least Squares

We study the implicit regularization of mini-batch stochastic gradient descent, when applied to the fundamental problem of least squares regression. We leverage a continuous-time stochastic differential equation having the same moments as stochastic gradient descent, which we call stochastic gradient flow. We give a bound on the excess risk of stochastic gradient flow at time $t$, over ridge regression with tuning parameter $\lambda = 1/t$. The bound may be computed from explicit constants (e.g., the mini-batch size, step size, number of iterations), revealing precisely how these quantities drive the excess risk. Numerical examples show the bound can be small, indicating a tight relationship between the two estimators. We give a similar result relating the coefficients of stochastic gradient flow and ridge. These results hold under no conditions on the data matrix $X$, and across the entire optimization path (not just at convergence).