Reviewerly/exHarmony huggingface dataset:
Nonlocal realism tests and quantum state tomography in Sagnac-based type-II polarization-entanglement SPDC-source

We have experimentally created a robust, ultrabright and phase-stable polarization-entangled state close to maximally entangled Bell-state with %98-fidelity using the type-II spontaneous parametric down-conversion (SPDC) process in periodically-poled KTiOPO4 (PPKTP) collinear crystal inside a Sagnac interferometer (SI). Bell inequality measurement, Freedman's test, as the different versions of CHSH inequality, and also visibility test which all can be seen as the nonlocal realism tests, imply that our created entangled state shows a strong violation from the classical physics or any hidden-variable theory. We have obtained very reliable and very strong Bell violation as S=2.78±0.01 with high brightness VHV=%(99.969±0.003) and VDA=%(96.751±0.002) and very strong violation due to Freedman test as δF=0.01715±0.00001. Furthermore, using the tomographic reconstruction of quantum states together a maximum-likelihood-technique (MLT) as the numerical optimization, we obtain the physical non-negative definite density operator which shows the nonseparability and entanglement of our prepared state. By having the maximum likelihood density operator, we calculate some important entanglement-measures and entanglement entropies. The Sagnac configuration provides bidirectional crystal pumping yields to high-rate entanglement source which is very applicable in quantum communication, sensing and metrology as well as quantum information protocols, and has potential to be used in quantum illumination-based LIDAR and free-space quantum key distribution (QKD).
Studying visual search without an eye tracker: an assessment of artificial foveation

Eye tracking is a useful tool for studying human cognition, both in the laboratory and in real-world applications. However, there are cases in which eye tracking is not possible, such as in high-security environments where recording devices cannot be introduced. After facing this challenge in our own work, we sought to test the effectiveness of using artificial foveation as an alternative to eye tracking for studying visual search performance. Two groups of participants completed the same list comparison task, which was a computer-based task designed to mimic an inventory verification process that is commonly performed by international nuclear safeguards inspectors. We manipulated the way in which the items on the inventory list were ordered and color coded. For the eye tracking group, an eye tracker was used to assess the order in which participants viewed the items and the number of fixations per trial in each list condition. For the artificial foveation group, the items were covered with a blurry mask except when participants moused over them. We tracked the order in which participants viewed the items by moving their mouse and the number of items viewed per trial in each list condition. We observed the same overall pattern of performance for the various list display conditions, regardless of the method. However, participants were much slower to complete the task when using artificial foveation and had more variability in their accuracy. Our results indicate that the artificial foveation method can reveal the same pattern of differences across conditions as eye tracking, but it can also impact participants' task performance.
Hierarchical and Stable Multiagent Reinforcement Learning for Cooperative Navigation Control

We solve an important and challenging cooperative navigation control problem, Multiagent Navigation to Unassigned Multiple targets (MNUM) in unknown environments with minimal time and without collision. Conventional methods are based on multiagent path planning that requires building an environment map and expensive real-time path planning computations. In this article, we formulate MNUM as a stochastic game and devise a novel multiagent deep reinforcement learning (MADRL) algorithm to learn an end-to-end solution, which directly maps raw sensor data to control signals. Once learned, the policy can be deployed onto each agent, and thereby, the expensive online planning computations can be offloaded. However, to solve MNUM, traditional MADRL suffers from large policy solution space and nonstationary environment when agents make decisions independently and concurrently. Accordingly, we propose a hierarchical and stable MADRL algorithm. The hierarchical learning part introduces a two-layer policy model to reduce the solution space and uses an interlaced learning paradigm to learn two coupled policies. In the stable learning part, we propose to learn an extended action-value function that implicitly incorporates estimations of other agents' actions, based on which the environment's nonstationarity caused by other agents' changing policies can be alleviated. Extensive experiments demonstrate that our method can converge in a fast way and generate more efficient cooperative navigation policies than comparable methods.
Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Key

In this paper, we propose a novel DNN watermarking method that utilizes a learnable image transformation method with a secret key. The proposed method embeds a watermark pattern in a model by using learnable transformed images and allows us to remotely verify the ownership of the model. As a result, it is piracy-resistant, so the original watermark cannot be overwritten by a pirated watermark, and adding a new watermark decreases the model accuracy unlike most of the existing DNN watermarking methods. In addition, it does not require a special pre-defined training set or trigger set. We empirically evaluated the proposed method on the CIFAR-10 dataset. The results show that it was resilient against fine-tuning and pruning attacks while maintaining a high watermark-detection accuracy.
Node2LV: Squared Lorentzian Representations for Node Proximity

Recently, network embedding has attracted extensive research interest. Most existing network embedding models are based on Euclidean spaces. However, Euclidean embedding models cannot effectively capture complex patterns, especially latent hierarchical structures underlying in real-world graphs. Consequently, hyperbolic representation models have been developed to preserve the hierarchical information. Nevertheless, existing hyperbolic models only capture the first-order proximity between nodes. To this end, we propose a new embedding model, named Node2LV, that learns the hyperbolic representations of nodes using squared Lorentzian distances. This yields three advantages. First, our model can effectively capture hierarchical structures that come from the network topology. Second, compared with the conventional hyperbolic embedding methods that use computationally expensive Riemannian gradients, it can be optimized in a more efficient way. Lastly, different from existing hyperbolic embedding models, Node2LV captures higher-order proximities. Specifically, we represent each node with two hyperbolic embeddings, and make the embeddings of related nodes close to each other. To preserve higher-order node proximity, we use a random walk strategy to generate local neighborhood context. We conduct extensive experiments on four different types of real-world networks. Empirical results demonstrate that Node2LV significantly outperforms various graph embedding baselines.
Mobile technology supply factors and mobile money innovation: thresholds for complementary policies

This study complements the extant literature by assessing how enhancing supply factors of mobile technologies affect mobile money innovations for financial inclusion in developing countries. The mobile money innovation outcome variables are: mobile money accounts, the mobile phone used to send money and the mobile phone used to receive money. The mobile technology supply factors are: unique mobile subscription rate, mobile connectivity performance, mobile connectivity coverage and telecommunications (telecom) sector regulation. The empirical evidence is based on quadratic Tobit regressions, and the following findings are established. There are Kuznets or inverted-shaped nexuses between three of the four supply factors and mobile money innovations from which thresholds for complementary policies are provided as follows: (i) unique adults' mobile subscription rates of 128.500%, 121.500% and 77.750% for mobile money accounts, the mobile used to send money and the mobile used to receive money, respectively; (ii) the average share of the population covered by 2G, 3G and 4G mobile data networks of 61.250% and 51.833% for the mobile used to send money and the mobile used to receive money, respectively; and (iii) a telecom sector regulation index of 0.409, 0.283 and 0.283 for mobile money accounts, the mobile phone used to send money and the mobile phone used to receive money, respectively. Some complementary policies are discussed, because at the attendant thresholds, the engaged supply factors of mobile money technologies become necessary, but not sufficient conditions of mobile money innovations for financial inclusion.
Sentiment Analysis Based on Deep Learning Methods for Explainable Recommendations with Reviews

Explainable recommendation systems have gained much attention in the last few years. Most of them use textual reviews to provide users with interpretability about why services or products are liked by users or recommended for them. Sentiment analysis has potential advantages to determine the attitudes of users in online communities using websites such as Twitter, Facebook, and YouTube. However, sentiment analysis of textual reviews in explainable recommendation systems seems to be a really challenging task. In this paper, we present a deep learning-based architecture for sentiment analysis to automatically predict the sentiment of reviews, which are considered as explanations of recommendations. It consists of two instances of the prediction model, one with the Long Short-Term Memory (LSTM) method and the other with the Gated Recurrent Unit (GRU) method. We evaluate their performance on one real-world dataset from Amazon and compare them with one state-of-the-art method. The experimental results show that our methods perform better than the baseline approach.
Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Matrix Factorization (MF) has been widely applied in machine learning and data mining. Due to the large computational cost of MF, we aim to improve the efficiency of SGD-based MF computation by utilizing the massive parallel processing power of heterogeneous multiprocessors. The main challenge in parallel SGD algorithms on heterogeneous CPU-GPU systems lies in the strategy to assign tasks. We design a novel strategy to divide the matrix into a set of blocks by considering two aspects. First, we observe that the matrix should be divided nonuniformly, and relatively large blocks should be assigned to GPUs to saturate the computing power of GPUs. In addition to exploiting the characteristics of hardware, the workloads assigned to two types of hardware should be balanced. We design a cost model tailored for our problem to accurately estimate the performance of hardware on different data sizes. Extensive experiments show that our proposed algorithm achieves high efficiency with a high quality of training quality.
HPPU: An Energy-Efficient Sparse DNN Training Processor with Hybrid Weight Pruning

Enlightened by the fact that deep-neural-networks (DNNs) are typically highly over-parameterized, weight-pruning-based sparse training (ST) becomes a practical method to reduce training computation and compress models. However, the previous pruning algorithms are either with a coarse-grained pattern or a fine-grained pattern. They lead to a limited pruning ratio or a drastically irregular sparsity distribution, which is computation-intensive or logic-complex for hardware implementation. Meanwhile, the current DNN processors focus on sparse inference but cannot support emerging ST techniques. This paper proposes a co-design approach where the algorithm is adapted to suit the hardware constraints and the hardware exploit the algorithm property to accelerate sparse training. We first present a novel pruning algorithm, hybrid weight pruning, including channel-wise and line-wise pruning. It reaches a considerable pruning ratio while maintaining the hardware friendly property. Then we design a hardware architecture, Hybrid Pruning Processing Unit, HPPU, to accelerate the proposed algorithm. It develops a 2-level active data selector and a sparse convolution engine, which maximize hardware utilization when handling the hybrid sparsity patterns during training. We evaluate HPPU by synthesizing it with 28nm CMOS technology. HPPU achieves 50.1% higher pruning ratio than coarse-grained pruning and 1.53× higher energy-efficiency than fine-grained pruning. The peak energy-efficiency of HPPU is 126.04TFLOPs/W, outperforming state-of-the-art trainable processor GANPU 1.67×. When training a ResNet18 model, HPPU consumes 3.72× less energy and offers 4.69× speedup, and maintains unpruned accuracy.
Accelerating the Yinyang K-Means Algorithm Using the GPU

The k-means clustering algorithm is widely employed for unsupervised learning. The algorithm takes as input a multidimensional dataset of points and number of clusters/centroids, k, where each point is assigned to one of the clusters. For exact k-means clustering, the algorithm must compute the same result as Lloyd's algorithm, which is well-known to be computationally expensive due to the large number of distance comparisons between each point and the k centroids. Several algorithms have been proposed for k-means clustering that avoid distance calculations but produce an exact result. However, these algorithms have all been designed for execution using the CPU, and no published works have examined using the GPU to accelerate k-means while simultaneously avoiding distance calculations. This paper examines the state-of-the-art Yinyang algorithm that avoids distance calculations as executed on the GPU. Since Lloyd's algorithm is well-suited to a GPU execution, it is not clear whether the Yinyang algorithm will obtain significant performance gains on GPU hardware. In this context, this paper: (i) proposes the first GPU-accelerated Yinyang algorithm in the literature; (ii) advances several optimizations to GPU kernels; (iii) contrasts and evaluates different degrees of distance calculation pruning; and, (iv) compares the performance of our GPU-accelerated Yinyang algorithm to four reference implementations. Our GPU algorithm achieves a speedup over the multi-core CPU Yinyang algorithm of up to 8× on real-world datasets.
CodedStereo: Learned Phase Masks for Large Depth-of-field Stereo

Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) – due to the conflicting impact of aperture size on both these variables. Inspired by the extended depth of field cameras, we propose a novel end-to-end learning-based technique to overcome this limitation, by introducing a phase mask at the aperture plane of the cameras in a stereo imaging system. The phase mask creates a depth-dependent yet numerically invertible point spread function, allowing us to recover sharp image texture and stereo correspondence over a significantly extended depth of field (EDOF) than conventional stereo. The phase mask pattern, the EDOF image reconstruction, and the stereo disparity estimation are all trained together using an end-to-end learned deep neural network. We perform theoretical analysis and characterization of the proposed approach and show a 6× increase in volume that can be imaged in simulation. We also build an experimental prototype and validate the approach using real-world results acquired using this prototype system.
A BERT-based system for multi-topic labeling of Arabic content

Text classification (or categorization) is one of the most common natural language processing (NLP) tasks. It is very useful to simplify the management of a large volume of textual data by assigning each text to one or more categories. This operation is challenging when it is a multi-label classification. For Arabic text, this task becomes more challenging due to the complex morphology and structure of Arabic language. In this paper, we address this issue by proposing a classification system for the Mowjaz Multi-Topic Labelling Task. The objective of this task is to classify Arabic articles according to the 10 topics predefined in Mowjaz. The proposed system is based on AraBERT, a pre-trained BERT model for the Arabic language. The first step of this system consists in tokenizing and representing the input articles using the AraBERT model. Then, a fully connected neural network is applied on the output of the AraBERT model to classify the articles according to their topics. The experimental tests conducted on the Mowjaz dataset showed an accuracy of 0.865 for the development set and an accuracy of 0.851 for the test set.
Code Authors Hidden in File Revision Histories: An Empirical Study

Although many programmers write their names in the comments of a source file, from such comments, it is unreliable to identify code authors, since the modifications of many programmers are not recorded. Even if they are recorded in a code repository, many authors are hidden in revision histories.The true authors of source files are important in many research topics. For example, when detecting plagiarism, if the authors of two source are overlapped, it becomes more challenging to determine plagiarism than the source files that are written by individual authors. As it is difficult to determine true authors of a source file, researchers typically use source files whose authors are already known (e.g., the source files from Google Code Jam), but such files are not many and less representative. Meanwhile, although some empirical studies touch code authors, to the best of our knowledge, no prior study has analyzed the characteristics of code authors that are hidden in revision histories. As a result, many research questions along with code authors are still open. For example, how many authors does a source file can have, and what are the proportions of contributions per source file, if they are written by more than one author?To answer the timely questions, in this paper, we conducted an empirical study on code authors that are hidden in revision histories. To support our study, we implemented a tool called CODA. By comparing the latest code lines with past commits, CODA identifies the true authors of all code lines. With its support, we analyzed 12,092 source files that were written by 506 programmers. Our study answers several interesting questions concerning code authors. For example, we find that 75.4% source files are written by multiple authors, and their contributions follow the famous 80/20 principle. These findings are useful to understand authors of source files in open source communities.
Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution

Finite mixture models have been widely used to model and analyze data from a heterogeneous populations. Moreover, data of this kind can be missing or subject to some upper and/or lower detection limits because of the constraints of experimental apparatuses. Another complication arises when measures of each population depart significantly from normality, such as asymmetric behavior. For such data structures, we propose a robust model for censored and/or missing data based on finite mixtures of multivariate skew-normal distributions. This approach allows us to model data with great flexibility, accommodating multimodality and skewness, simultaneously, depending on the structure of the mixture components. We develop an analytically simple, yet efficient, EM-type algorithm for conducting maximum likelihood estimation of the parameters. The algorithm has closed-form expressions at the E-step that rely on formulas for the mean and variance of the truncated multivariate skew-normal distributions. Furthermore, a general information-based method for approximating the asymptotic covariance matrix of the estimators is also presented. Results obtained from the analysis of both simulated and real datasets are reported to demonstrate the effectiveness of the proposed method. The proposed algorithm and method are implemented in the new R package CensMFM.
Classification of Malaria-Infected Cells using Convolutional Neural Networks

Malaria is a disease which, despite being present for over a century, still claims a significant number of lives every year. The advancement of artificial intelligence have opened the door to developing innovative methods in malaria treatment. Introducing machine learning approaches to this field can be beneficial in the disease prevention, detection, and therapy. In this work, convolutional neural networks for malaria detection are developed, based on the classification of thin blood smear images of the potentially infected cells. Input data was preprocessed using the image segmentation, file organization, image size standardization, color channel adjustment, and data splitting. Further, the proposed methodology included image conversion, network architecture defining, parameter tuning and network training. Various architectures of convolutional neural networks were developed and evaluated. In addition, multiple values of different network layer parameters were assessed. This study was implemented in Clojure programming language. Proposed network architecture includes two convolutional and pooling layers followed by activation functions, batch normalization and two linear layers. This convolutional neural network provided the best results and achieved an 82.7% accuracy. Furthermore, this paper proposes another network model with lightweight configuration and a slight accuracy decrease.
A knowledge-based differential covariance matrix adaptation cooperative algorithm

In this paper, a knowledge-based differential covariance matrix adaptation cooperative algorithm (DCMAC) is proposed for continuous problems. On the basis of combining successful history-based adaptive DE variants with linear population size reduction (LSHADE) and covariance matrix adaptation evolutionary strategy (CMA-ES), DCMAC proposes a strategy based on knowledge reward and punishment to achieve the purpose of collaborative optimization. Afterward, an adaptive learning mechanism is introduced to optimize the parameters to balance the exploitation and exploration of DCMAC. This process enables the algorithm to have global search capability. Finally, the niching-based population size reduction mechanism is introduced to improve the local search ability of the DCMAC. A weighted mutation strategy with dynamic greedy p value and covariance matrix adaptation (CMA) sampled based on differential vector are presented. Meanwhile, the knowledge acquired in the previous iteration process is adopted in the algorithm to select a mutation strategy for generating the new candidate solutions in the next iteration. The niching population size reduction mechanism is introduced to maintain the diversity of the population and compared with the other classical population size reduction methods. The optimal combination of parameters in the DCMAC algorithm is testified by the design of the experiment. Furthermore, the DCMAC is testified on the CEC2017 benchmark functions. The effectiveness and efficiency of the DCMAC are demonstrated by the experimental results in solving complex continuous problems.