Reviewerly/exHarmony huggingface dataset:
Incorporating Full Text and Bibliographic Features to Improve Scholarly Journal Recommendation

Selecting an appropriate venue to communicate one's research is the very first step in scholarly communication. Many papers are simply rejected from the editor's desk on not being submitted to the right journal. Existing journal recommender systems extract keywords only from the title and abstract sections of candidate articles and produce journal recommendations based on their weighted-match with a domain-specific vocabulary. Here in this work, we investigate a simple yet effective approach by incorporating additional information from bibliography and body section of academic manuscripts and show their potency to yield a better recommendation. On a closed set of ten different journals, our content-based recommender achieves significant improvement over the usual baselines (at least ~ 10%). Our preliminary approach is simple yet promising and if suitably applied could efficiently recommend journals from a larger pool as well.
Beyond overall effects: A Bayesian approach to finding constraints in meta-analysis.

Most meta-analyses focus on the behavior of meta-analytic means. In many cases, however, this mean is difficult to defend as a construct because the underlying distribution of studies reflects many factors, including how we as researchers choose to design studies. We present an alternative goal for meta-analysis. The analyst may ask about relations that are stable across all the studies. In a typical meta-analysis, there is a hypothesized direction (e.g., that violent video games increase, rather than decrease, aggressive behavior). We ask whether all studies in a meta-analysis have true effects in the hypothesized direction. If so, this is an example of a stable relation across all the studies. We propose 4 models: (a) all studies are truly null; (b) all studies share a single true nonzero effect; (c) studies differ, but all true effects are in the same direction; and (d) some study effects are truly positive, whereas others are truly negative. We develop Bayes factor model comparison for these models and apply them to 4 extant meta-analyses to show their usefulness. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Multi-output parameter-insensitive kernel twin SVR model

Multi-output regression aims at mapping a multivariate input feature space to a multivariate output space. Currently, it is effective to extend the traditional support vector regression (SVR) mechanism to solve the multi-output case. However, some methods adopting a combination of single-output SVR models exhibit the severe drawback of not considering the possible correlations between outputs, and other multi-output SVRs show high computational complexity and are typically sensitive to parameters due to the influence of noise. To handle these problems, in this study, we determine the multi-output regression function through a pair of nonparallel up- and down-bound functions solved by two smaller-sized quadratic programming problems, which results in a fast learning speed. This method is named multi-output twin support vector regression (M-TSVR). Moreover, when the noise is heteroscedastic, based on our M-TSVR, we introduce a pair of multi-input/output nonparallel parameter insensitive up- and down-bound functions to evaluate a regression model named multi-output parameter-insensitive twin support vector regression (M-PITSVR). To handle the nonlinear case, we derive the kernelized extensions of M-TSVR and M-PITSVR. Finally, a series of comparative experiments with several other multi-output-based methods are performed on twelve multi-output datasets. The experimental results indicate that the proposed multi-output regressors yield fast learning speed as well as a better and more stable prediction performance.
Intelligent Handover Management in 5G Mobile Networks based on Recurrent Neural Networks

In modern mobile networks, we are seeing a huge leap in the uses of traffic by subscribers, so it is becoming increasingly difficult to ensure the proper operation of the network. In this paper, we propose to use an intelligent approach to network management, namely handover management. The main idea is to use neural networks, which, based on knowledge of user mobility, can predict ways of moving a group of subscribers between cells, which provide the maximum effectiveness of the implementation of the handover. From the results, we see that the neural network can predict traffic with an accuracy of more than 90%, and if you use the proper equipment and explore more durable traffic statistics, then this value can be significantly increased.
Comparing the dynamics of periodically forced lasers and neurons

Abstract Neuromorphic photonics is a new paradigm for ultra-fast neuro-inspired optical computing that can revolutionize information processing and artificial intelligence systems. To implement practical photonic neural networks is crucial to identify low-cost energy-efficient laser systems that can mimic neuronal activity. Here we study experimentally the spiking dynamics of a semiconductor laser with optical feedback under periodic modulation of the pump current, and compare with the dynamics of a neuron that is simulated with the stochastic FitzHugh–Nagumo model, with an applied periodic signal whose waveform is the same as that used to modulate the laser current. Sinusoidal and pulse-down waveforms are tested. We find that the laser response and the neuronal response to the periodic forcing, quantified in terms of the variation of the spike rate with the amplitude and with the frequency of the forcing signal, is qualitatively similar. We also compare the laser and neuron dynamics using symbolic time series analysis. The characterization of the statistical properties of the relative timing of the spikes in terms of ordinal patterns unveils similarities, and also some differences. Our results indicate that semiconductor lasers with optical feedback can be used as low-cost, energy-efficient photonic neurons, the building blocks of all-optical signal processing systems; however, the length of the external cavity prevents optical feedback on the chip.
Hybrid Generative Adversarial Networks for Deep MR to CT Synthesis Using Unpaired Data

Many different methods have been proposed for generation of synthetic CT from MR images. Most of these methods depend on pairwise aligned MR and CT training images of the same patient, which are difficult to obtain. 2D cycle-consistent Generative Adversarial Networks (2D-cGAN) have been explored before for generating synthetic CTs from MR images but the results are not satisfied due to spatial inconsistency. There exists attempt to develop 3D cycle GAN (3D-cGAN) for image translation but its training requires large number of data which may not be always available. In this paper, we introduce two novel mechanisms to address above mentioned problems. First, we introduce a hybrid GAN (hGAN) consisting of a 3D generator network and a 2D discriminator network for deep MR to CT synthesis using unpaired data. We use 3D fully convolutional networks to form the generator, which can better model the 3D spatial information and thus could solve the discontinuity problem across slices. Second, we take the results generated from the 2D-cGAN as weak labels, which will be used together with an adversarial training strategy to encourage the generator’s 3D output to look like a stack of real CT slices as much as possible. Experimental results demonstrated that our approach achieved better results than the state-of-the-art when limited number of unpaired data are available.
Supervised Dimensionality Reduction Methods via Recursive Regression

In this article, the recursive problems of both orthogonal linear discriminant analysis (OLDA) and orthogonal least squares regression (OLSR) are investigated. Different from other works, the associated recursive problems are addressed via a novel recursive regression method, which achieves the dimensionality reduction in the orthogonal complement space heuristically. As for the OLDA, an efficient method is developed to obtain the associated optimal subspace, which is closely related to the orthonormal basis of the optimal solution to the ridge regression. As for the OLSR, the scalable subspace is introduced to build up an original OLSR with optimal scaling (OS). Through further relaxing the proposed problem into a convex parameterized orthogonal quadratic problem, an effective approach is derived, such that not only the optimal subspace can be achieved but also the OS could be obtained automatically. Accordingly, two supervised dimensionality reduction methods are proposed via obtaining the heuristic solutions to the recursive problems of the OLDA and the OLSR.
Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia

Traditional exploration techniques usually rely on extensive field work supported by geophysical ground surveying. However, this approach can be limited by several factors such as field accessibility, financial cost, area size, climate, and public disapproval. We recommend the use of multiscale hyperspectral remote sensing to mitigate the disadvantages of traditional exploration techniques. The proposed workflow analyzes a possible target at different levels of spatial detail. This method is particularly beneficial in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally noninvasive surveying. After a satellite regional reconnaissance, a target is characterized in more detail by plane-based hyperspectral mapping. Subsequently, Remotely Piloted Aircraft System (RPAS)-mounted hyperspectral sensors are deployed on selected regions of interest to provide a higher level of spatial detail. All hyperspectral data are corrected for radiometric and geometric distortions. End-member modeling and classification techniques are used for rapid and accurate lithological mapping. Validation is performed via field spectroscopy and portable XRF as well as laboratory geochemical and spectral analyses. The resulting spectral data products quickly provide relevant information on outcropping lithologies for the field teams. We show that the multiscale approach allows defining the promising areas that are further refined using RPAS-based hyperspectral imaging. We further argue that the addition of RPAS-based hyperspectral data can improve the detail of field mapping in mineral exploration, by bridging the resolution gap between airplane- and ground-based data. RPAS-based measurements can supplement and direct geological observation rapidly in the field and therefore allow better integration with in situ ground investigations. We demonstrate the efficiency of the proposed approach at the Lofdal Carbonatite Complex in Namibia, which has been previously subjected to rare earth elements exploration. The deposit is located in a remote environment and characterized by difficult terrain which limits ground surveys.
Software/Hardware Codesign of the Post Quantum Cryptography Algorithm NTRUEncrypt Using High-Level Synthesis and Register-Transfer Level Design Methodologies

When quantum computers become scalable and reliable, they are likely to break all public-key cryptography standards, such as RSA and Elliptic Curve Cryptography. The projected threat of quantum computers has led the U.S. National Institute of Standards and Technology (NIST) to an effort aimed at replacing existing public-key cryptography standards with new quantum-resistant alternatives. In December 2017, 69 candidates were accepted by NIST to Round 1 of the NIST Post-Quantum Cryptography (PQC) standardization process. NTRUEncrypt is one of the most well-known PQC algorithms that has withstood cryptanalysis. The speed of NTRUEncrypt in software, especially on embedded software platforms, is limited by the long execution time of its primary operation, polynomial multiplication. In this paper, we investigate speeding up NTRUEncrypt using software/hardware codesign on a Xilinx Zynq UltraScale+ multiprocessor system-on-chip (MPSoC). Polynomial multiplication is implemented in the Programmable Logic (PL) of Zynq using two approaches: traditional Register-Transfer Level (RTL) and High-Level Synthesis (HLS). The remaining operations of NTRUEncrypt are executed in software on the Processing System (PS) of Zynq, using the bare-metal mode. The speed-up of our software/hardware codesigns vs. purely software implementations is determined experimentally and analyzed in the paper. The results are reported for the RTL-based and HLS-based hardware accelerators, and compared to the best available software implementation, included in the NIST submission package. The speed-ups for encryption were 2.4 and 3.9, depending on the selected parameter set. For decryption, the corresponding speed-ups were 4.0 and 6.8. In addition, for the polynomial multiplication operation itself, the speed up was in excess of 75. Our code for the NTRUEncrypt polynomial multiplier accelerator is being made open-source for further evaluation on multiple software/hardware platforms.
Prediction of flood areas using the logistic regression method (case study of the provinces Banten, DKI Jakarta, and West Java)

Abstract Disasters that occur in Indonesia continue to increase from year to year. The National Disaster Management Agency (BNPB) noted that throughout 2017 there had been 2,862 disasters. Of these, almost 99 percent are hydrometeorological disasters, namely disasters that are affected by weather and surface flow. The details of the disaster include floods (979), tornadoes (886), landslides (848), forest and land fires (96), drought (19), earthquakes (20), tidal waves and abrasions (11), dam volcanic eruptions (3). The western part of Java Island includes three provinces namely West Java Province, DKI Jakarta Province, and Banten Province. Of the three provinces, they are no stranger to hearing floods, especially in the capital city of Jakarta and West Java. Flood problems until now have not been resolved completely even the problem of floods is the tendency to increase both in terms of intensity, frequency, and distribution due to climate change. Based on the above conditions, it is necessary to do a study that can provide information about the main causal factors and predict areas that are likely to experience floods. In achieving these objectives, this study uses a mathematical model of logistic regression analysis and application of Geographic Information Systems (GIS) conducted based on the variables that cause flooding, namely rainfall, topography, slope, flow accumulation, land use, and distance to the nearest river. The modelling results obtained an accuracy rate for predicting flood disasters in the study area using logistic regression which was between 85.05% - 94.39%.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products

In the last decade, it has been shown that many hard AI tasks, especially in NLP, can be naturally modeled as extreme classification problems leading to improved precision. However, such models are prohibitively expensive to train due to the memory bottleneck in the last layer. For example, a reasonable softmax layer for the dataset of interest in this paper can easily reach well beyond 100 billion parameters (> 400 GB memory). To alleviate this problem, we present Merged-Average Classifiers via Hashing (MACH), a generic $K$-classification algorithm where memory provably scales at $O(\log K)$ without any assumption on the relation between classes. MACH is subtly a count-min sketch structure in disguise, which uses universal hashing to reduce classification with a large number of classes to few embarrassingly parallel and independent classification tasks with a small (constant) number of classes. MACH naturally provides a technique for zero communication model parallelism. We experiment with 6 datasets; some multiclass and some multilabel, and show consistent improvement in precision and recall metrics compared to respective baselines. In particular, we train an end-to -end deep classifier on a private product search dataset sampled from Amazon Search Engine with 70 million queries and 49.46 million documents. MACH outperforms, by a significant margin, the state-of-the-art extreme classification models deployed on commercial search engines: Parabel and dense embedding models. Our largest model has 6.4 billion parameters and trains in less than 35 hrs on a single p3.16x machine. Our training times are 7-10x faster, and our memory footprints are 2-4x smaller than the best baselines. This training time is also significantly lower than the one reported by Google’s mixture of experts (MoE) language model on a comparable model size and hardware.
Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms

Multilayer feed-forward Artificial Neural Networks (ANNs) are universal function approximators capable of modeling measurable functions to any desired degree of accuracy. In practice, designing practical, efficient neural network architectures requires significant effort and expertise. Further, designing efficient neural network architectures that fit optimally on hardware for the benefit of acceleration adds yet another degree of complexity. In this paper, we use Evolutionary Cell Aided Design (ECAD), a framework capable of searching the design spaces for ANN structures and reconfigurable hardware to find solutions based on a set of constraints and fitness functions. Providing a modular and scalable 2D systolic array based machine learning accelerator design built for an Arria 10 GX 1150 FPGA device using OpenCL enables results to be tested and deployed in real hardware. Along with the hardware, a software model of the architecture was developed to speed up the evolutionary process. We present results from the ECAD framework showing the effect various optimizations including accuracy, images per second, effective giga-operations per second, and latency have on both ANN and hardware configurations. Through this work we show that unique solutions can exist for each optimization resulting in the best performance. This work lays the foundation for finding machine learning based solutions for a wide range of applications having different system constraints.
Imitation-Projected Programmatic Reinforcement Learning

We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for such policies remains a challenge. Our approach to this challenge - a meta-algorithm called PROPEL - is based on three insights. First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a form of mirror descent that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space. Second, we view the unconstrained policy space as mixing neural and programmatic representations, which enables employing state-of-the-art deep policy gradient approaches. Third, we cast the projection step as program synthesis via imitation learning, and exploit contemporary combinatorial methods for this task. We present theoretical convergence results for PROPEL and empirically evaluate the approach in three continuous control domains. The experiments show that PROPEL can significantly outperform state-of-the-art approaches for learning programmatic policies.
A Wearable System for In-Home and Long-Term Assessment of Fetal Movement

Objectives: This paper presents a novel wearable system for in-home and long-term fetal movement monitoring on a reliable and easily accessible basis. Material and methods: The system mainly consists of four accelerometers for fetal movement signal acquisition, a microcontroller for signal processing and an Android-based device interacting with the microcontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetal movement in an intelligible way. Results: The proposed system can deliver reliable results with a specificity of 0.99 and a sensitivity of 0.77 for fetal movement time series signal classification. Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medical professionals and hospital resources, and has potential applications in the field of e-Health home care. Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) help establish an initial database for future medical analysis of sensor-recorded fetal behaviors.
Extended Variational Inference for Propagating Uncertainty in Convolutional Neural Networks

Model confidence or uncertainty is critical in autonomous systems as they directly tie to the safety and trustworthiness of the system. The quantification of uncertainty in the output decisions of deep neural networks (DNNs) is a challenging problem. The Bayesian framework enables the estimation of the predictive uncertainty by introducing probability distributions over the (unknown) network weights; however, the propagation of these high-dimensional distributions through multiple layers and non-linear transformations is mathematically intractable. In this work, we propose an extended variational inference (eVI) framework for convolutional neural network (CNN) based on tensor Normal distributions (TNDs) defined over convolutional kernels. Our proposed eVI framework propagates the first two moments (mean and covariance) of these TNDs through all layers of the CNN. We employ first-order Taylor series linearization to approximate the mean and covariances passing through the non-linear activations. The uncertainty in the output decision is given by the propagated covariance of the predictive distribution. Furthermore, we show, through extensive simulations on the MNIST and CIFAR-10 datasets, that the CNN becomes more robust to Gaussian noise and adversarial attacks.
Computing Optimal Hypertree Decompositions

We propose a new algorithmic method for computing the hypertree width of hypergraphs, and we evaluate its performance empirically. At the core of our approach lies a novel ordering based characterization of hypertree width which lends to an efficient encoding to SAT modulo Theory (SMT). We tested our algorithm on an extensive benchmark set consisting of real-world instances from various sources. Our approach outperforms state-of-the-art algorithms for hypertree width. We achieve a further speedup by a new technique that first solves a relaxation of the problem and subsequently uses the solution to guide the algorithm for solving the problem itself.
Unsupervised Model Selection for Variational Disentangled Representation Learning

Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and practical applications, it is important to enable hyperparameter tuning and model selection of existing unsupervised approaches without requiring access to ground truth attribute labels, which are not available for most datasets. This paper addresses this problem by introducing a simple yet robust and reliable method for unsupervised disentangled model selection. We show that our approach performs comparably to the existing supervised alternatives across 5400 models from six state of the art unsupervised disentangled representation learning model classes. Furthermore, we show that the ranking produced by our approach correlates well with the final task performance on two different domains.