Reviewerly/exHarmony huggingface dataset:
Prediction of Purchase Intention on the E-Commerce Clickstream Data

Every day millions of customers do shopping through e-commerce web sites around the world. Huge amounts of click stream data are generated from customers' use of e-commerce websites. Learning the behavior of users from click data and estimating the intention to buy has become an important need. Within the scope of this research, a methodology is proposed on the estimation of the purchase intention before finalizing the sessions of the customers. The proposed methodology was tested by the ACM RecSys Symposium on e-commerce data set published in 2015 and it was found that successful results could be obtained.
Compressing Weight-updates for Image Artifacts Removal Neural Networks

In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible. At encoder side, we fine-tune a pre-trained artifact removal network on target data by using a compression objective applied on the weight-update. In particular, the compression objective encourages weight-updates which are sparse and closer to quantized values. This way, the final weight-update can be compressed more efficiently by pruning and quantization, and can be included into the encoded bitstream together with the image bitstream of a traditional codec. We show that this approach achieves reconstruction quality which is on-par or slightly superior to a traditional codec, at comparable bitrates. To our knowledge, this is the first attempt to combine image compression and neural network's weight update compression.
Wide dynamic range quadrature interferometer with high-gain approach and sliding mode control

The present work concerns with the modeling, development and application of a novel control strategy based on sliding mode control, for two beam quadrature interferometers, with the high-gain approach. In this case, by reading the control signal the demodulation process does not require phase unwrapping algorithms, i.e., the output signal presents a straight-line relationship with the interferometer total phase shift. This system was implemented in a digital platform to control a bulk Michelson interferometer whose performance was experimentally determined, showing its capability on achieving real-time measurement and presenting wider dynamic range and bandwidth (52.5 rad in low frequencies and 5.8 rad up to 500 Hz) when compared with the literature. Moreover, this performance can be improved even further by simply increasing the feedback gain.
Framework Model in Anylogic for Smart City Ring Road Management

A city ring road with the parameters of a highway forms a part of the infrastructure of a modern city. The introduction of automated highway management to the city ring road moves it towards a smart city. This paper presents the framework model which was created with the aim of developing an agent-based control within the highway management in the future. The model was created in the multi-method simulation tool Anylogic since it allows the testing of both traditional rule-based algorithms and agent-based control. It is universal enough, thus, it can be used to compare different approaches to highway management. In this paper, a literature review focusing on the Multi-Agent Systems and their application in traffic and transportation is performed. Based on the above mentioned review, a system architecture for agent-based traffic control is proposed and the framework model is described.
Learning Transferable Graph Exploration

This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a distribution of environments. At test time, presented with an unseen environment from the same distribution, the policy aims to generalize the exploration strategy to visit the maximum number of unique states in a limited number of steps. We particularly focus on environments with graph-structured state-spaces that are encountered in many important real-world applications like software testing and map building. We formulate this task as a reinforcement learning problem where the `exploration' agent is rewarded for transitioning to previously unseen environment states and employ a graph-structured memory to encode the agent's past trajectory. Experimental results demonstrate that our approach is extremely effective for exploration of spatial maps; and when applied on the challenging problems of coverage-guided software-testing of domain-specific programs and real-world mobile applications, it outperforms methods that have been hand-engineered by human experts.
USAAR-DFKI – The Transference Architecture for English–German Automatic Post-Editing

In this paper we present an English–German Automatic Post-Editing (APE) system called transference, submitted to the APE Task organized at WMT 2019. Our transference model is based on a multi-encoder transformer architecture. Unlike previous approaches, it (i) uses a transformer encoder block for src, (ii) followed by a transformer decoder block, but without masking, for self-attention on mt, which effectively acts as second encoder combining src –> mt, and (iii) feeds this representation into a final decoder block generating pe. Our model improves over the raw black-box neural machine translation system by 0.9 and 1.0 absolute BLEU points on the WMT 2019 APE development and test set. Our submission ranked 3rd, however compared to the two top systems, performance differences are not statistically significant.
Interval timing in deep reinforcement learning agents

The measurement of time is central to intelligent behavior. We know that both animals and artificial agents can successfully use temporal dependencies to select actions. In artificial agents, little work has directly addressed (1) which architectural components are necessary for successful development of this ability, (2) how this timing ability comes to be represented in the units and actions of the agent, and (3) whether the resulting behavior of the system converges on solutions similar to those of biology. Here we studied interval timing abilities in deep reinforcement learning agents trained end-to-end on an interval reproduction paradigm inspired by experimental literature on mechanisms of timing. We characterize the strategies developed by recurrent and feedforward agents, which both succeed at temporal reproduction using distinct mechanisms, some of which bear specific and intriguing similarities to biological systems. These findings advance our understanding of how agents come to represent time, and they highlight the value of experimentally inspired approaches to characterizing agent abilities.
Evaluating Topic Quality with Posterior Variability

Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over topic model parameters.We derive a novel measure of LDA topic quality using the variability of the posterior distributions.Compared to several existing baselines for automatic topic evaluation, the proposed metric achieves state-of-the-art correlations with human judgments of topic quality in experiments on three corpora. 1 We additionally demonstrate that topic quality estimation can be further improved using a supervised estimator that combines multiple metrics.
POS Tagging for Improving Code-Switching Identification in Arabic

When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language.This paper explores the possibility of utilizing this pattern in improving code-switching identification between Modern Standard Arabic (MSA) and Egyptian Arabic (EA).We try to answer the question of how strong is the POS signal in word-level code-switching identification.We build a deep learning model enriched with linguistic features (including POS tags) that outperforms the state-of-the-art results by 1.9% on the development set and 1.0% on the test set.We also show that in intrasentential code-switching, the selection of lexical items is constrained by POS categories, where function words tend to come more often from the dialectal language while the majority of content words come from the standard language.
Crowd Abnormal Event Detection Based on Sparse Coding

Crowd feature perception is an essential step for us to understand the crowd behavior. However, as the individuals present not only the sociality but also the randomness, there remain great challenges to extract the sociality of the individual directly. In this paper, we propose a crowd feature perception algorithm based on a sparse linear model (SLM). It builds the statistical characterization of the sociality by assuming a priori distribution of the SLM. First, we calculate the optical flow to extract the motion information of the crowd. Second, we input the video motion features to the sparse coding and generate the SLM. The super-Gaussian prior distributions in SLMs build the statistical characterization of the sociality. In addition, we combine the infinite Hidden Markov Model (iHMM) statistic model to determine whether the detected event is an abnormal event. We validate our method on UMN dataset and simulate dataset for abnormal detection, and the experiments show that this algorithm generates promising result compared with other state-of-art methods.
Stereo Depth Mapping via Axis-Aligned Warping

Viewing various stereo images under different viewing conditions has escalated the need for efficient and effective depth mapping techniques for adjusting the depths and sizes of objects to match user preference. Existing methods mainly alter the depth of an object through non-uniform region warping, which, however, often cause severe depth or shape distortions, due to improper warping such as local rotations. In this paper, we propose a new object depth mapping scheme based on axis-aligned warping. The proposed axis-aligned-warping based optimization model can simultaneously adjust the depths and sizes of selected objects to their target values without introducing severe shape distortions. Experimental results demonstrate that our method achieves high flexibility and effectiveness in adjusting the size and depth of object compared with existing methods.
Performance Analysis of NSL-KDD Dataset Using Classification Algorithms with Different Feature Selection Algorithms and Supervised Filter Discretization

Naive Bayes and Bayes Net are critical classification methods in data mining classification and have build up being important software tools for the classification, the description and generalization of information. All classification algorithms are open sources, which are implemented on Java (C4.5 algorithms) for WEKA software tool. In this paper, we exhibit the strategy for increasing the performance of Naive Bayes and Bayes Net algorithms with supervised filter discretization after we applied feature selection techniques. We have used the supervised filter discretization on two classification algorithms and compared the result with and without discretization. The outcomes acquired from the experiment showed significant improvement over the existing classification algorithms.
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries

This paper explores the task of interactive image retrieval using natural language queries, where a user progressively provides input queries to refine a set of retrieval results. Moreover, our work explores this problem in the context of complex image scenes containing multiple objects. We propose Drill-down, an effective framework for encoding multiple queries with an efficient compact state representation that significantly extends current methods for single-round image retrieval. We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes. Furthermore, we find that existing image datasets with textual captions can provide a surprisingly effective form of weak supervision for this task. We compare our method with existing sequential encoding and embedding networks, demonstrating superior performance on two proposed benchmarks: automatic image retrieval on a simulated scenario that uses region captions as queries, and interactive image retrieval using real queries from human evaluators.
Architecture-Aware Network Pruning for Vision Quality Applications

Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field. However, CNN incurs high computational complexity, especially for vision quality applications because of large image resolution. In this paper, we propose an iterative architecture-aware pruning algorithm with adaptive magnitude threshold while cooperating with quality-metric measurement simultaneously. We show the performance improvement applied on vision quality applications and provide comprehensive analysis with flexible pruning configuration. With the proposed method, the Multiply-Accumulate (MAC) of state-of-the-art low-light imaging (SID) and super-resolution (EDSR) are reduced by 58% and 37% without quality drop, respectively. The memory bandwidth (BW) requirements of convolutional layer can be also reduced by 20% to 40%.
Personalized Case-Based Explanation of Matrix Factorization Recommendations

Matrix factorization is an advanced recommendation strategy based on characterizing both items and users on a vector of latent factors inferred from rating patterns. These vectors represent, somehow, a characterization of the user preferences in a lower dimensionality space. Although matrix factorization is more accurate that other recommendation strategies, the main problem associated with this approach is that the discovered factors are opaque and difficult to explain to the final user. In this paper we propose a personalized case-based explanation strategy that uses the latent factors to find similar explanatory cases already rated by the user.
Rethinking Kernel Methods for Node Representation Learning on Graphs

Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which is a related problem to graph representation learning, is still ill-posed and the state-of-the-art methods are heavily based on heuristics. Here, we present a novel theoretical kernel-based framework for node classification that can bridge the gap between these two representation learning problems on graphs. Our approach is motivated by graph kernel methodology but extended to learn the node representations capturing the structural information in a graph. We theoretically show that our formulation is as powerful as any positive semidefinite kernels. To efficiently learn the kernel, we propose a novel mechanism for node feature aggregation and a data-driven similarity metric employed during the training phase. More importantly, our framework is flexible and complementary to other graph-based deep learning models, e.g., Graph Convolutional Networks (GCNs). We empirically evaluate our approach on a number of standard node classification benchmarks, and demonstrate that our model sets the new state of the art.
Training Compact Neural Networks via Auxiliary Overparameterization.

It is observed that overparameterization (i.e., designing neural networkswhose number of parameters is larger than statistically needed to fit thetraining data) can improve both optimization and generalization while compactnetworks are more difficult to be optimized. However, overparameterizationleads to slower test-time inference speed and more power consumption. To tacklethis problem, we propose a novel auxiliary module to simulate the effect ofoverparameterization. During training, we expand the compact network with theauxiliary module to formulate a wider network to assist optimization whileduring inference only the original compact network is kept. Moreover, wepropose to automatically search the hierarchical auxiliary structure to avoidadding supervisions heuristically. In experiments, we explore severalchallenging resource constraint tasks including light-weight classification,semantic segmentation and multi-task learning with hard parameter sharing. Weempirically find that the proposed auxiliary module can maintain the complexityof the compact network while significantly improving the performance.
The Rising Threat of Launchpad Attacks

Human failure is the weakest link in many, if not most, security systems. As a result, criminals are increasingly relying on social engineering as a way to circumvent security controls. To improve their yield, criminals constantly experiment with methods aimed at making their attacks harder to detectmboth to security systems and to the end users behind them. Naturally, an attack that successfully evades detection by both human and machine has the potential to make criminals very wealthy. Therefore, once discovered and successfully tested, such attacks exhibit dramatic growth and are commonly copied and tweaked by other criminals spotting an opportunity when they see it. What we term the launchpad attack is the newest example of such an attack. This attack is also commonly referred to as a business email compromise (BEC) that uses account takeover. Although it was virtually unheard of just a few years ago, 44% of organizations have now experienced this type of attack according to a recent industry report.1
Atmospheric cloud algorithms for day/night whole sky imagers

This paper discusses new atmospheric cloud algorithms that we have developed to detect the presence and location of clouds in images of the sky acquired by day/night whole sky imagers, and to categorize the clouds by opacity. The day/night whole sky imagers that we developed are ground-based sensors that acquire digital imagery of the full sky down to the horizon in several visible spectral bands and a near-infrared spectral band. The instruments acquire accurate data under daylight, moonlight, and starlight conditions. This paper discusses the previously unpublished day and night cloud algorithms. The day cloud algorithm identifies opaque clouds using a red/blue ratio and identifies thin clouds by comparing current red/blue ratios with the ratios anticipated for clear skies. The clear sky ratios include an image-to-image adaptive algorithm feature to adjust for haze amount. The night cloud algorithms are based on determinations of earth-to-space beam transmittances and on comparisons of current radiances with radiance distributions for clear skies and for opaque clouds. They also include an adaptive algorithm feature to adjust for changes in haze amount. In addition, these new night algorithms report the Earth-to-space beam transmittance in selected directions. The algorithms provide a pixel-by-pixel determination of the cloud results with excellent accuracy. This paper discusses the new day and night cloud algorithms and an assessment of their accuracy, as well as a short discussion of the pros and cons of using these wave bands versus other wave bands.
A Hybrid PSO-ANFIS Approach for Horizontal Solar Radiation Prediction in Nigeria

For efficient and reliable hydrogen production via solar photovoltaic system, it is important to obtain accurate solar radiation data. Though there are equipment specifically designed for solar radiation prediction but are very expensive and have high maintenance cost that most countries like Nigeria are unable to purchase. In this study, the accuracy of a hybrid PSO-ANFIS method is examined to predict horizontal solar radiation in Nigeria. The prediction is done based on the available meteorological data obtained from NIMET Nigeria. The meteorological data used for this study are monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours, which serves as inputs to the developed model. The model accuracy is evaluated using two statistical indicators Root Mean Square Error (RMSE) and Coefficient of determination (R²). The accuracy of the proposed model is validated using ANFIS, GA-ANFIS models and other literatures. Based on the statistical parameters used for the model evaluation, the results obtained proves PSO-ANFIS as a good model for predicting solar radiation with the values of RMSE=0.68318, R²=0.9065 at the training stage and RMSE=1.3838, R²=0.8058 at the testing stage. This proves the potentiality of PSO-ANFIS technique for accurate solar radiation prediction
High-Accuracy Correction of a Microlens Array for Plenoptic Imaging Sensors

Microlens array (MLA) errors in plenoptic cameras can cause the confusion or mismatching of 4D spatio-angular information in the image space, significantly affecting the accuracy and efficiency of target reconstruction. In this paper, we present a high-accuracy correction method for light fields distorted by MLA errors. Subpixel feature points are extracted from the microlens subimages of a raw image to obtain correction matrices and perform registration of the corresponding subimages at a subpixel level. The proposed method is applied for correcting MLA errors of two different categories in light-field images, namely form errors and orientation errors. Experimental results show that the proposed method can rectify the geometric and intensity distortions of raw images accurately and improve the quality of light-field refocusing. Qualitative and quantitative comparisons between images before and after correction verify the performance of our method in terms of accuracy, stability, and adaptability.
HEURISTICS FOR THE ARITHMETIC OF ELLIPTIC CURVES

This is an introduction to a probabilistic model for the arithmetic of elliptic curves, a model developed in a series of articles of the author with Bhargava, Kane, Lenstra, Park, Rains, Voight, and Wood. We discuss the theoretical evidence for the model, and we make predictions about elliptic curves based on corresponding theorems proved about the model. In particular, the model suggests that all but finitely many elliptic curves over $\mathbb{Q}$ have rank $\le 21$, which would imply that the rank is uniformly bounded.
Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Cross-lingual morphological inflection with explicit alignment

This paper describes two related systems for cross-lingual morphological inflection for SIGMORPHON 2019 Shared Task participation.Both sets of results submitted to the shared task for evaluation are obtained using a simple approach of predicting transducer actions based on initial alignments on the training set, where cross-lingual transfer is limited to only using the high-resource language data as additional training set.The performance of the system does not reach the performance of the top two systems in the competition.However, we show that results can be improved with further tuning.We also present further analyses showing that the cross-lingual gain is rather modest.