Reviewerly/exHarmony huggingface dataset:
A Hybrid Model Based on LFM and BiGRU Toward Research Paper Recommendation

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.
On the Theory of Transfer Learning: The Importance of Task Diversity

We provide new statistical guarantees for transfer learning via representation learning--when transfer is achieved by learning a feature representation shared across different tasks. This enables learning on new tasks using far less data than is required to learn them in isolation. Formally, we consider $t+1$ tasks parameterized by functions of the form $f_j \circ h$ in a general function class $\mathcal{F} \circ \mathcal{H}$, where each $f_j$ is a task-specific function in $\mathcal{F}$ and $h$ is the shared representation in $\mathcal{H}$. Letting $C(\cdot)$ denote the complexity measure of the function class, we show that for diverse training tasks (1) the sample complexity needed to learn the shared representation across the first $t$ training tasks scales as $C(\mathcal{H}) + t C(\mathcal{F})$, despite no explicit access to a signal from the feature representation and (2) with an accurate estimate of the representation, the sample complexity needed to learn a new task scales only with $C(\mathcal{F})$. Our results depend upon a new general notion of task diversity--applicable to models with general tasks, features, and losses--as well as a novel chain rule for Gaussian complexities. Finally, we exhibit the utility of our general framework in several models of importance in the literature.
Improved Schemes for Episodic Memory-based Lifelong Learning

Current deep neural networks can achieve remarkable performance on a single task. However, when the deep neural network is continually trained on a sequence of tasks, it seems to gradually forget the previous learned knowledge. This phenomenon is referred to as \textit{catastrophic forgetting} and motivates the field called lifelong learning. Recently, episodic memory based approaches such as GEM \cite{lopez2017gradient} and A-GEM \cite{chaudhry2018efficient} have shown remarkable performance. In this paper, we provide the first unified view of episodic memory based approaches from an optimization's perspective. This view leads to two improved schemes for episodic memory based lifelong learning, called MEGA-I and MEGA-II. MEGA-I and MEGA-II modulate the balance between old tasks and the new task by integrating the current gradient with the gradient computed on the episodic memory. Notably, we show that GEM and A-GEM are degenerate cases of MEGA-I and MEGA-II which consistently put the same emphasis on the current task, regardless of how the loss changes over time. Our proposed schemes address this issue by using novel loss-balancing updating rules, which drastically improve the performance over GEM and A-GEM. Extensive experimental results show that the proposed schemes significantly advance the state-of-the-art on four commonly used lifelong learning benchmarks, reducing the error by up to 18\%.
Estimating Conditional Density of Missing Values Using Deep Gaussian Mixture Model

We consider the problem of estimating the conditional probability distribution of missing values given the observed ones. We propose an approach, which combines the flexibility of deep neural networks with the simplicity of Gaussian mixture models (GMMs). Given an incomplete data point, our neural network returns the parameters of Gaussian distribution (in the form of Factor Analyzers model) representing the corresponding conditional density. We experimentally verify that our model provides better log-likelihood than conditional GMM trained in a typical way. Moreover, imputation obtained by replacing missing values using the mean vector of our model looks visually plausible.
Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.
Blockchains for constrained edge devices

Today’s networks are seeing a large influx of Internet connected devices that reside primarily on the edge of the network. Many of these devices, such as Internet of Things (IoT) devices, are resource constrained both by storage capacity and power requirements that limit a device’s Internet availability. Several interesting architectures have been proposed to address security, device management, configuration, and multi-party interaction concerns using blockchain technology. These architectures require a trusted intermediary to interact with the blockchain on behalf of the edge device. This introduces a single point of failure in trust and security. This paper proposes a novel adaptation of blockchain technology to enable these edge devices to interact and participate with a blockchain without requiring a trusted intermediary. The proposed architecture provides a flexible and extensible framework that enables multi party interactions to take place at the edge of the network. The efficacy of the proposed design is demonstrated through theoretical analysis and by an application to a network of resource constrained IoT devices.
An Ameliorated Harmony Search Algorithm With Hybrid Convergence Mechanism

The metaheuristic optimization algorithm Harmony search (HS), which imitates the process of music improvisation, is becoming widely used due to its simplicity and easy operation.However, the basic HS has shortcomings of low optimization accuracy and risk of easy falling into local optimum.To overcome these problems, this article develops an ameliorated harmony search algorithm with hybrid convergence mechanism, namely AHS-HCM.For the new method, the so-called hybrid convergence mechanism mainly includes two important schemes.The first scheme is to introduce a convergence coefficient in the harmony improvisation to further adjust the optimization performance, which can improve the final accuracy.The second scheme is to put forward a non-linear convergence domain for the global exploration, it also helps the optimization accuracy and efficiency.Besides, the operation process of new harmony variables generation is modified to enrich the search behavior and contribute to find the global optimum.Fifteen typical CEC's benchmark functions are selected for experiments, and the results clearly proved the effectiveness of the AHS-HCM.It is shown that, in most cases, the proposed AHS-HCM algorithm is superior to other HS variants as well as some similar famous population-based algorithms in terms of optimization accuracy and stability.
Learned Dual-View Reflection Removal

Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registration, and a learned synthesis model for combining aligned image pairs. Because no dataset for dual-view reflection removal exists, we render a synthetic dataset of dual-views with and without reflections for use in training. Our evaluation on an additional real-world dataset of stereo pairs shows that our algorithm outperforms existing single-image and multi-image dereflection approaches.
A Skill-Based Visual Attention Model for Cloud Gaming

Despite its recent advances and increasing industrial interest, cloud gaming’s high bandwidth usage is still one of its major challenges. In this paper, we demonstrate how incorporating visual attention into cloud gaming helps to reduce bitrate without negatively affecting the player’s quality of experience. We show that current visual attention models, which work well for normal videos, underperform in the context of cloud gaming videos. Hence, we propose our novel model, by developing a skill-based visual attention model, based on a cloud gaming dataset. First, it is demonstrated how players’ attention maps are correlated with their skill levels and how this can be exploited to improve the accuracy of visual attention modeling. Then, this fact is used to cluster attention maps, according to the player’s skill level. A simple yet effective method is introduced to predict players’ skill levels using their performance in game. Finally, the models are incorporated into the video encoder to perceptually optimize the bitrate allocation. Incorporating the player’s skill level into our model improves the accuracy of saliency maps by 14% with respect to the baseline, and 24% with respect to competing methods, in terms of Normalized Scanpath Saliency (NSS). Furthermore, we show that the maximum possible amount of video bitrate reduction depends on the player’s skill level. Experimental results show 13%, 5%, and 15% reduction in video bitrate for beginner, intermediate, and expert players, respectively.
A Perspective on the Intersection of Information Security Policies and IA Awareness, Factoring in End-User Behavior

In 2017 Executive Order 13800 was enacted for all federal entities to use the NIST Cybersecurity Framework to report on FISMA compliance.According to GAO-19-545 report sixteen agencies were identified as failing to successfully implement FISMA regulations rooted in information security policies (ISPs).This paper will introduce the link between information assurance awareness with the prescribed actions and its direct influence on information security policies.While organizations are conscious of the federal rules and regulations, most continue to fail to successfully implement and comply with the guidelines due to a sincere lack of information assurance and awareness, which ties directly into human behavior.A discussion on the intersection of information security awareness and behavior will be presented.The UTAUT theory measures and informs the researcher on factors that influence the end-user.Conclusively, recommendations will be offered on why organizations need to invest in a mechanism that measures these factors, which increases information awareness to change behavior, thus achieving better compliance with their organizational ISPs.
Parallelization Techniques for Verifying Neural Networks

Inspired by recent successes of parallel techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics to leverage parallelism and improve the scalability of neural network verification. We present a general description of the Split-and-Conquer partitioning algorithm, implemented within the Marabou framework, and discuss its parameters and heuristic choices. In particular, we explore two novel partitioning strategies, that partition the input space or the phases of the neuron activations, respectively. We introduce a branching heuristic and a direction heuristic that are based on the notion of polarity. We also introduce a highly parallelizable pre-processing algorithm for simplifying neural network verification problems. An extensive experimental evaluation shows the benefit of these techniques on both existing and new benchmarks. A preliminary experiment ultra-scaling our algorithm using a large distributed cloud - based platform also shows promising results.
BROS: A Pre-trained Language Model for Understanding Texts in Document

Understanding document from their visual snapshots is an emerging and challenging problem that requires both advanced computer vision and NLP methods. Although the recent advance in OCR enables the accurate extraction of text segments, it is still challenging to extract key information from documents due to the diversity of layouts. To compensate for the difficulties, this paper introduces a pre-trained language model, BERT Relying On Spatiality (BROS), that represents and understands the semantics of spatially distributed texts. Different from previous pre-training methods on 1D text, BROS is pre-trained on large-scale semi-structured documents with a novel area-masking strategy while efficiently including the spatial layout information of input documents. Also, to generate structured outputs in various document understanding tasks, BROS utilizes a powerful graph-based decoder that can capture the relation between text segments. BROS achieves state-of-the-art results on four benchmark tasks: FUNSD, SROIE*, CORD, and SciTSR. Our experimental settings and implementation codes will be publicly available.
Platform Works as Stack Economization: Cryptocurrency Markets and Exchanges in Perspective

What is an economic platform? I address this question by focusing on the case of cryptocurrency exchange platforms. The research draws on interviews with platform actors, fieldwork in one exchange, and computational text analysis of the terms of service of all cryptocurrency exchanges in the world. I argue that cryptocurrency exchange platforms go beyond market processes by fulfilling a variety of functions including banking, infrastructure development, gift-giving, barter, money making, payment system operation, software production, security providing, and centralized extra-blockchain accounting. I propose the concept of “stack” to describe such a process of socio-digital economization that takes place in these data money exchanges. Demonstrating that it is inadequate to describe platforms as mere digital infrastructures, devices, places or markets, I argue that cryptocurrency exchange platforms can best be understood as economization stacks that weave multiple layers and types of interaction, and facilitate an empirically observable range of variegated economic activities.
Hybrid Harmony Search Differential Evolution Algorithm

Differential evolution (DE) algorithm has some excellent attributes including strong exploration capability. However, it cannot balance the exploitation with exploration ability in the search process. To enhance the performance of the DE algorithm, this paper proposes a new algorithm named hybrid harmony differential evolution algorithm (HHSDE). The key features of HHSDE algorithm are as follows. First, a new mutation operation is developed for improving the efficiency of mutation, in which the New Harmony generation mechanics of the harmony algorithm (HS) is employed. Second, the harmony memory size is updated with the iteration. Third, a self-adaptive parameter adjustment strategy is presented to control scaling factor. Fourth, a new evaluation method is proposed to effectively assess the algorithm convergence performance. Two classical DE algorithms, HS algorithm, improvement Differential evolution algorithm(ISDE) and Hybrid Artificial Bee Colony algorithm with Differential Evolution(HABCDE) have been tested against HHSDE based on 25 benchmark functions of CEC2005 and the results reveal that the proposed algorithm is better than the other algorithms under consideration.
Linguistics for the Age of AI

A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language. With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap for creating language-endowed intelligent agents (LEIAs) that can understand,explain, and learn. They describe the language-understanding capabilities of LEIAs from the perspectives of cognitive modeling and system building, emphasizing “actionability”—which involves achieving interpretations that are sufficiently deep, precise, and confident to support reasoning about action. After detailing their microtheories for topics such as semantic analysis, basic coreference, and situational reasoning, McShane and Nirenburg turn to agent applications developed using those microtheories and evaluations of a LEIA's language understanding capabilities. McShane and Nirenburg argue that the only way to achieve human-level language understanding by machines is to place linguistics front and center, using statistics and big data as contributing resources. They lay out a long-term research program that addresses linguistics and real-world reasoning together, within a comprehensive cognitive architecture. The open access edition of this book was made possible by generous funding from Arcadia – a charitable fund of Lisbet Rausing and Peter Baldwin.
Optical Flow Estimation from a Single Motion-blurred Image

In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems. In this work, we propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner. We design our network with transformer networks to learn globally and locally varying motions from encoded features of a motion-blurred input, and decode left and right frame features without explicit frame supervision. A flow estimator network is then used to estimate optical flow from the decoded features in a coarse-to-fine manner. We qualitatively and quantitatively evaluate our model through a large set of experiments on synthetic and real motion-blur datasets. We also provide in-depth analysis of our model in connection with related approaches to highlight the effectiveness and favorability of our approach. Furthermore, we showcase the applicability of the flow estimated by our method on deblurring and moving object segmentation tasks.
Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

Contrast is a distinctive image feature that tells if it has adequate visual quality or not. On many occasions, images are captured with low-contrast due to inevitable obstacles. Therefore, an improved type-II fuzzy set-based algorithm is developed to enhance the contrast of various color and grayscale images properly while preserving the brightness and providing natural colors. The proposed algorithm utilizes new upper and lower ranges, amended Hamacher t-conorm, and a transform-based gamma correction method to provide the enhanced images. The proposed algorithm is assessed with artificial and real contrast distorted images, compared with twelve specialized methods, and the outcomes are evaluated using four advanced metrics. From the obtained results of experiments and comparisons, the developed algorithm demonstrated the ability to process various color and grayscale images, performed the best among the comparative methods, and scored the best in all four quality evaluation metrics. The findings of this study are significant because the proposed algorithm has low-complexity and can adjust the contrast of different images expeditiously, which enables it to be used with different imaging modalities especially those with limited hardware resources or produce high-resolution images.
Motion Blur Removal With Quality Assessment Guidance

Non-uniform blind motion deblurring is a challenging yet fundamental task in the computer vision field, which aims to restore the latent sharp image from the blurry input. Recently, deep-learning-based methods have made significant improvement and progress, on the metric of PSNR. They achieve good results mainly because they adopt Mean Squared Error (MSE) as the optimization objective, in addition to their good model design. However, simple adoption of the PSNR metric and the MSE loss, has non-ignorable disadvantages. PSNR cannot always succeed in assessing the deblurred quality in accordance with the human visual system (HVS), and MSE guides the network to generate over-smoothed images. To address these problems, we are the first to propose the deep-learning-based multi-scale non-reference quality assessment network (Deep DEBLUR-IQA) for assessing the quality of deblurred results. Moreover, a deblurring network of high efficiency is presented. It is more than 50 times faster than other SOTA multi-scale Convolution Neural Network (CNN) methods, with the newly propose Residual Dilated Block (RDB) and Light ResBlock (LRB). The deblurring network's performance can be further boosted with Multiple Dilation Block (MDB), with an acceptable speed decrease. Finally, and most importantly, we are the first to let Deep DEBLUR-IQA guide the deblurring network's optimization. This IQA-guided enhancement paradigm can significantly improve the deblurring results’ subjective quality while achieving excellent PSNR. Experimental results demonstrate that the proposed method performs favorably against state-of-the-art methods quantitatively and qualitatively.