Reviewerly/exHarmony huggingface dataset:
Adaptive nonnegative matrix factorization and measure comparisons for recommender systems

The Nonnegative Matrix Factorization (NMF) of the rating matrix has shown to be an effective method to tackle the recommendation problem. In this paper we propose new methods based on the NMF of the rating matrix and we compare them with some classical algorithms such as the SVD and the regularized and unregularized non-negative matrix factorization approach. In particular a new algorithm is obtained changing adaptively the function to be minimized at each step, realizing a sort of dynamic prior strategy. Another algorithm is obtained modifying the function to be minimized in the NMF formulation by enforcing the reconstruction of the unknown ratings toward a prior term. We then combine different methods obtaining two mixed strategies which turn out to be very effective in the reconstruction of missing observations. We perform a thoughtful comparison of different methods on the basis of several evaluation measures. We consider in particular rating, classification and ranking measures showing that the algorithm obtaining the best score for a given measure is in general the best also when different measures are considered, lowering the interest in designing specific evaluation measures. The algorithms have been tested on different datasets, in particular the 1M, and 10M MovieLens datasets containing ratings on movies, the Jester dataset with ranting on jokes and Amazon Fine Foods dataset with ratings on foods. The comparison of the different algorithms, shows the good performance of methods employing both an explicit and an implicit regularization scheme. Moreover we can get a boost by mixed strategies combining a fast method with a more accurate one.
Context Aware VM Placement Optimization Technique for Heterogeneous IaaS Cloud

Ever increasing demand for cloud adoption is prompting researchers and engineers around the world to make cloud computing more efficient and beneficial for cloud service providers and users. Cloud computing brings profits for all when the cloud infrastructure is used efficiently, and its services are made affordable to businesses of all scales. Managing cloud data center incurs a significant cost, which includes investing in IT infrastructure at the beginning and data center management costs for power, repair, space, and so on at later stages. The power costs are contributing to a significant share in overall data center management costs, and saving in power consumption can help reduce management costs for data center owners. This paper proposes an efficient context-aware adaptive heuristic-based solution for the virtual machine (VM) placement optimization in the heterogeneous cloud data centers. The proposed VM placement technique takes into the account of physical machine characteristics and load (peak and non-peak) conditions in the heterogeneous data centers to save power and also improve performance efficiency for data center owners. The experiments conducted with real cloud workloads and also synthetic workloads against a well-known adaptive heuristic-based technique indicate significant performance improvements and energy saving with our proposed solution.
Experimental Comparison of Stochastic Optimizers in Deep Learning

The stochastic optimization problem in deep learning involves finding optimal values of loss function and neural network parameters using a meta-heuristic search algorithm. The fact that these values cannot be reasonably obtained by using a deterministic optimization technique underscores the need for an iterative method that randomly picks data segments, arbitrarily determines initial values of optimization (network) parameters and steadily computes series of error functions until a tolerable error is attained. The typical stochastic optimization algorithm for training deep neural networks as a non-convex optimization problem is gradient descent. It has existing extensions like Stochastic Gradient Descent, Adagrad, Adadelta, RMSProp and Adam. In terms of accuracy, convergence rate and training time, each of these stochastic optimizers represents an improvement. However, there is room for further improvement. This paper presents outcomes of series of experiments conducted with a view to providing empirical evidences of successes made so far. We used Python deep learning libaries (Tensorflow and Keras API) for our experiments. Each algorithm is executed, results collated, and a case made for further research in deep learning to improve training time and convergence rate of deep neural network, as well as accuracy of outcomes. This is in response to the growing demands for deep learning in mission-critical and highly sophisticated decision making processes across industry verticals.
Realization of a causal-modeled delayed-choice experiment using single photons

Wave-particle duality constitutes one of the most intriguing features in quantum physics. A well-known gedanken experiment that provides evidence for this is Wheeler's delayed-choice experiment based on a Mach-Zehnder interferometer [J. A. Wheeler, in Mathematical Foundations of Quantum Theory, edited by A. R. Marlow (Academic, New York, 1978), pp. 9--48; in Quantum Theory and Measurement, edited by J. A. Wheeler and W. H. Zurek (Princeton University Press, Princeton, 1984), pp. 182--213]. Many different versions of delayed-choice experiments have been conducted with both classical and quantum detecting devices. Recently, it was proposed that the delayed-choice experiment could be devised from the perspective of a device-independent prepare-and-measure scenario [R. Chaves et al., Causal Modeling the Delayed-Choice Experiment, Phys. Rev. Lett. 120, 190401 (2018)]. In our work, we experimentally realize this modified version with a deterministic single-photon source and examine the wave-particle objective in a causal-modeled scheme without the assistance of entanglement, which is achieved by violating the dimension-witness inequalities. Our experiment also provides an intriguing perspective and exhibits the benefits of studying quantum theory from the casual model point of view.
An Efficient Hardware-Oriented Single-Pass Approach for Connected Component Analysis

Connected Component Analysis (CCA) plays an important role in several image analysis and pattern recognition algorithms. Being one of the most time-consuming tasks in such applications, specific hardware accelerator for the CCA are highly desirable. As its main characteristic, the design of such an accelerator must be able to complete a run-time process of the input image frame without suspending the input streaming data-flow, by using a reasonable amount of hardware resources. This paper presents a new approach that allows virtually any feature of interest to be extracted in a single-pass from the input image frames. The proposed method has been validated by a proper system hardware implemented in a complete heterogeneous design, within a Xilinx Zynq-7000 Field Programmable Gate Array (FPGA) System on Chip (SoC) device. For processing 640 × 480 input image resolution, only 760 LUTs and 787 FFs were required. Moreover, a frame-rate of ~325 fps and a throughput of 95.37 Mp/s were achieved. When compared to several recent competitors, the proposed design exhibits the most favorable performance-resources trade-off.
Multi-modal Discriminative Model for Vision-and-Language Navigation

Haoshuo Huang, Vihan Jain, Harsh Mehta, Jason Baldridge, Eugene Ie. Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP). 2019.
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models

Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. In this work, we focus on the vast amounts of unstructured natural language data stored in clinical notes and propose to automatically generate synthetic clinical notes that are more amenable to sharing using generative models trained on real de-identified records. To evaluate the merit of such notes, we measure both their privacy preservation properties as well as utility in training clinical NLP models. Experiments using neural language models yield notes whose utility is close to that of the real ones in some clinical NLP tasks, yet leave ample room for future improvements.
The Level Weighted Structural Similarity Loss: A Step Away from MSE

The Mean Square Error (MSE) has shown its strength when applied in deep generative models such as Auto-Encoders to model reconstruction loss. However, in image domain especially, the limitation of MSE is obvious: it assumes pixel independence and ignores spatial relationships of samples. This contradicts most architectures of Auto-Encoders which use convolutional layers to extract spatial dependent features. We base on the structural similarity metric (SSIM) and propose a novel level weighted structural similarity (LWSSIM) loss for convolutional Auto-Encoders. Experiments on common datasets on various Auto-Encoder variants show that our loss is able to outperform the MSE loss and the Vanilla SSIM loss. We also provide reasons why our model is able to succeed in cases where the standard SSIM loss fails.
A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.
A Framework of Reversible Color-to-Grayscale Conversion With Watermarking Feature

Reversible color-to-grayscale conversion (RCGC) is a method that embeds the chromatic information of a full color image into its grayscale version such that the original color image can be reconstructed in the future when necessary. In practical applications, it is required to provide a means to authenticate an information-embedded image such that its integrity can be guaranteed. However, none of the current RCGC algorithms take this factor into account. In this paper, to address this issue, we develop an information-embedding framework based on a vector quantization-based (VQ-based) RCGC algorithm recently proposed by us. Under this framework, we propose a RCGC algorithm that can embed both chromatic information and fragile watermark simultaneously into a grayscale image with the same technique to reduce the complexity and improve the efficiency. Like other VQ-based RCGC algorithms, the performance of the proposed RCGC algorithm highly relies on the palette it uses. We also propose a palette generation algorithm in this paper to support the information embedding process such that the visual quality of the color-embedded grayscale images and the reconstructed color images can be significantly improved.
Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER

Contextualized embeddings, which capture appropriate word meaning depending on context, have recently been proposed. We evaluate two methods for precomputing such embeddings, BERT and Flair, on four Czech text processing tasks: part-of-speech (POS) tagging, lemmatization, dependency parsing and named entity recognition (NER). The first three tasks, POS tagging, lemmatization and dependency parsing, are evaluated on two corpora: the Prague Dependency Treebank 3.5 and the Universal Dependencies 2.3. The named entity recognition (NER) is evaluated on the Czech Named Entity Corpus 1.1 and 2.0. We report state-of-the-art results for the above mentioned tasks and corpora.
Document Hashing with Mixture-Prior Generative Models

Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes.Generative hashing is often used to generate hashing codes in an unsupervised way.However, existing generative hashing methods only considered the use of simple priors, like Gaussian and Bernoulli priors, which limits these methods to further improve their performance.In this paper, two mixture-prior generative models are proposed, under the objective to produce high-quality hashing codes for documents.Specifically, a Gaussian mixture prior is first imposed onto the variational auto-encoder (VAE), followed by a separate step to cast the continuous latent representation of VAE into binary code.To avoid the performance loss caused by the separate casting, a model using a Bernoulli mixture prior is further developed, in which an end-toend training is admitted by resorting to the straight-through (ST) discrete gradient estimator.Experimental results on several benchmark datasets demonstrate that the proposed methods, especially the one using Bernoulli mixture priors, consistently outperform existing ones by a substantial margin.
Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-Level Embedding Features

This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation. The system is composed of a bidirectional recurrent neural network component acting as a sentence encoder to accumulate the context correlations, followed by a prediction network that maps the polyphonic character embeddings along with the conditions to corresponding pronunciations. We obtain the word-level condition from a pre-trained word-to-vector lookup table. One goal of polyphone disambiguation is to address the homograph problem existing in the front-end processing of Mandarin Chinese text-to-speech system. Our system achieves an accuracy of 94.69\% on a publicly available polyphonic character dataset. To further validate our choices on the conditional feature, we investigate polyphone disambiguation systems with multi-level conditions respectively. The experimental results show that both the sentence-level and the word-level conditional embedding features are able to attain good performance for Mandarin Chinese polyphone disambiguation.
Scalable Blockchain Protocol Based on Proof of Stake and Sharding

Blockchain – a distributed and public database of transactions – has become a platform for decentralized applications. Despite its increasing popularity, blockchain technology faces a scalability problem: the throughput does not scale with the increasing network size. Thus, in this paper, we propose a scalable blockchain protocol to solve the scalability problem. The proposed method was designed based on a proof of stake (PoS) consensus protocol and a sharding protocol. Instead of transactions being processed by the whole network, the sharding protocol is employed to divide unconfirmed transactions into transaction shards and to divide the network into network shards. The network shards process the transaction shards in parallel to produce middle blocks. Middle blocks are then combined into a final BLOCK in a timestamp recorded on the blockchain. Experiments were performed in a simulation network consisting of 100 Amazon EC2 instances. The latency of the proposed method was approximately 27 s and the maximum throughput achieved was 36 transactions per second for a network containing 100 nodes. The results of the experiments indicate that the throughput of the proposed protocol increases with the network size. This confirms the scalability of the proposed protocol.
Revisiting Quantum Feedback Control: Disentangling the Feedback‐Induced Phase from the Corresponding Amplitude

Abstract Coherent time‐delayed feedback allows the control of a quantum system and its partial stabilization against noise and decoherence. The crucial and externally accessible parameters in such control setups are the round‐trip‐induced delay time τ and the frequencies ω of the involved optical transitions which are typically controllable via global parameters like temperature, bias, or strain. They influence the dynamics via the amplitude and the phase of the feedback signal. These quantities are, however, not independent. Here, the aim is to control the feedback phase via a microwave pump field. Using the example of a Λ‐type three‐level system, it is shown that the Rabi frequency of the pump field induces phase shifts on demand and therefore increases the applicability of coherent quantum feedback control protocols.
On Reproducing Semi-dense Depth Map Reconstruction using Deep Convolutional Neural Networks with Perceptual Loss

In our recent papers, we proposed a new family of residual convolutional neural networks trained for semi-dense and sparse depth reconstruction without use of RGB channel. The proposed models can be used in low-resolution depth sensors or SLAM methods estimating partial depth with certain distributions. We proposed using perceptual loss for training depth reconstruction in order to better preserve edge structure and reduce over-smoothness of models trained on MSE loss alone. This paper contains reproducibility companion guide on training, running and evaluating suggested methods, while also presenting links on further studies in view of reviewers comments and related problems of depth reconstruction.
Straight to the Point: Reinforcement Learning for User Guidance in Ultrasound

Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders. In this setting it is essential to be able to provide means to obtain diagnostic data to potentially inexperienced users who did not receive an extensive medical training. Interpretation and acquisition of ultrasound images is not trivial. First, the user needs to find a suitable sound window which can be used to get a clear image, and then he needs to correctly interpret it to perform a diagnosis. Although many recent approaches focus on developing smart ultrasound devices that add interpretation capabilities to existing systems, our goal in this paper is to present a reinforcement learning (RL) strategy which is capable to guide novice users to the correct sonic window and enable them to obtain clinically relevant pictures of the anatomy of interest. We apply our approach to cardiac images acquired from the parasternal long axis (PLAx) view of the left ventricle of the heart.
Asymmetric Entanglement-Assisted Quantum Error-Correcting Codes and BCH Codes

The concept of asymmetric entanglement-assisted quantum error-correcting code (asymmetric EAQECC) is introduced in this article. Codes of this type take advantage of the asymmetry in quantum errors since phase-shift errors are more probable than qudit-flip errors. Moreover, they use pre-shared entanglement between encoder and decoder to simplify the theory of quantum error correction and increase the communication capacity. Thus, asymmetric EAQECCs can be constructed from any pair of classical linear codes over an arbitrary field. Their parameters are described and a Gilbert-Varshamov bound is presented. Explicit parameters of asymmetric EAQECCs from BCH codes are computed and examples exceeding the introduced Gilbert-Varshamov bound are shown.
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning

Text-based interactive recommendation provides richer user preferences and has demonstrated advantages over traditional interactive recommender systems. However, recommendations can easily violate preferences of users from their past natural-language feedback, since the recommender needs to explore new items for further improvement. To alleviate this issue, we propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference, which is incorporated into the standard RL objective of maximizing expected cumulative future rewards. Our proposed framework is general and is further extended to the task of constrained text generation. Empirical results show that the proposed method yields consistent improvement relative to standard RL methods.
Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34,469 valid investment events collected from www.itjuzi.com and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies' domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors' domain preference.
Fast Steganalysis Method for VoIP Streams

in this letter, we present a novel and extremely fast steganalysis method for voice over ip (voip) streams, driven by the need for a quick and accurate detection of possible steganography in VoIP streams. We firstly analyzed the correlations in carriers. To better exploit the correlations in code-words, we mapped vector quantization code-words into a semantic space. In order to achieve high detection efficiency, only one hidden layer was utilized to extract the correlations between these code-words. Finally, based on the extracted correlation features, we used the softmax classifier to categorize the input stream carriers. To boost the performance of this proposed model, we incorporate a simple knowledge distillation framework into the training process. Experimental results show that the proposed method achieves state-of-the-art performance both in detection accuracy and efficiency. In particular, the processing time of this method on average is only about 0.05% when sample length is as short as 0.1 s, attaching strong practical value to online serving of steganography monitor.