OpenRaiser/Intern-Atlas huggingface dataset:
Neural Diffusion Model for Microscopic Cascade Prediction
Venue: arXiv cs.SI 2018
Year: 2018

The prediction of information diffusion or cascade has attracted much attention over the last decade. Most cascade prediction works target on predicting cascade-level macroscopic properties such as the final size of a cascade. Existing microscopic cascade prediction models which focus on user-level modeling either make strong assumptions on how a user gets infected by a cascade or limit themselves to a specific scenario where "who infected whom" information is explicitly labeled. The strong assumptions oversimplify the complex diffusion mechanism and prevent these models from better fitting real-world cascade data. Also, the methods which focus on specific scenarios cannot be generalized to a general setting where the diffusion graph is unobserved. To overcome the drawbacks of previous works, we propose a Neural Diffusion Model (NDM) for general microscopic cascade prediction. NDM makes relaxed assumptions and employs deep learning techniques including attention mechanism and convolutional network for cascade modeling. Both advantages enable our model to go beyond the limitations of previous methods, better fit the diffusion data and generalize to unseen cascades. Experimental results on diffusion prediction task over four realistic cascade datasets show that our model can achieve a relative improvement up to 26% against the best performing baseline in terms of F1 score.
Problems and Solutions of Continuous Deployment: A Systematic Review
Venue: arXiv cs.SE 2018
Year: 2018

Context: The software industry needs to adapt itself to a rapidly changing market. Continuous practices (Continuous Integration, Continuous Delivery and Continuous Deployment), commonly found in Agile development processes, it is possible to deliver new features more frequently to clients, integrating of smaller features is less likely to cause conflicts than the more traditional approach of merging big features less frequently all at once. However, Continuous Deployment is no clear way on the best approaches for their implementation. Objective: The goal of this paper is to identify the challenges and the solutions related to Continuous Deployment, and then see which of those solutions can be applied to which challenges. Method: This paper is a systematic literature review of the problems and the solutions found when implementing the continuous deployment practice inside an organization. It also presents which solution can be applied to which problem. Thirty-one articles published after 2015 were analyzed for this SLR. Results: 22 problems were grouped inside the categories Human and Organizational, Process, Tools, Infrastructure, Application Architecture and Testing. The 19 solutions found were grouped inside the categories Human and Organizational, Architecture, Process and Tools. Solutions have been found for 14 problems and some questions have been identified for future research. Conclusion: this article is to serve as a reference for the practitioner who wants to find how to solve a specific challenge when implementing the continuous deployment practice.
3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects
Venue: arXiv cs.CV 2018
Year: 2018

Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution. Despite their small size (usually $<$10 voxels per object for an image of more than $10^6$ voxels), these markers reflect tissue damage and need to be accounted for to investigate the complete phenotype of complex pathological pathways. In addition to their very small size, variability in shape and appearance leads to high labelling variability across human raters, resulting in a very noisy gold standard. Such objects are notably present in the context of cerebral small vessel disease where enlarged perivascular spaces and lacunes, commonly observed in the ageing population, are thought to be associated with acceleration of cognitive decline and risk of dementia onset. In this work, we redesign the RCNN model to scale to 3D data, and to jointly detect and characterise these important markers of age-related neurovascular changes. We also propose training strategies enforcing the detection of extremely small objects, ensuring a tractable and stable training process.
Do we agree on user interface aesthetics of Android apps?
Venue: arXiv cs.SE 2018
Year: 2018

Context: Visual aesthetics is increasingly seen as an essential factor in perceived usability, interaction, and overall appraisal of user interfaces especially with respect to mobile applications. Yet, a question that remains is how to assess and to which extend users agree on visual aesthetics. Objective: This paper analyzes the inter-rater agreement on visual aesthetics of user interfaces of Android apps as a basis for guidelines and evaluation models. Method: We systematically collected ratings on the visual aesthetics of 100 user interfaces of Android apps from 10 participants and analyzed the frequency distribution, reliability and influencing design aspects. Results: In general, user interfaces of Android apps are perceived more ugly than beautiful. Yet, raters only moderately agree on the visual aesthetics. Disagreements seem to be related to subtle differences with respect to layout, shapes, colors, typography, and background images. Conclusion: Visual aesthetics is a key factor for the success of apps. However, the considerable disagreement of raters on the perceived visual aesthetics indicates the need for a better understanding of this software quality with respect to mobile apps.
A Novel Hierarchical Intrusion Detection System based on Decision Tree and Rules-based Models
Venue: arXiv cs.CR 2018
Year: 2018

This paper proposes a novel intrusion detection system (IDS) that combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes.
Generative Models from the perspective of Continual Learning
Venue: arXiv cs.LG 2018
Year: 2018

Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tuning. We used two quantitative metrics to estimate the generation quality and memory ability. We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10). We found that among all models, the original GAN performs best and among Continual Learning strategies, generative replay outperforms all other methods. Even if we found satisfactory combinations on MNIST and Fashion MNIST, training generative models sequentially on CIFAR10 is particularly instable, and remains a challenge. Our code is available online \footnote{\url{https://github.com/TLESORT/Generative\_Continual\_Learning}}.
Lower bounds for text indexing with mismatches and differences
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we study lower bounds for the fundamental problem of text indexing with mismatches and differences. In this problem we are given a long string of length $n$, the "text", and the task is to preprocess it into a data structure such that given a query string $Q$, one can quickly identify substrings that are within Hamming or edit distance at most $k$ from $Q$. This problem is at the core of various problems arising in biology and text processing. While exact text indexing allows linear-size data structures with linear query time, text indexing with $k$ mismatches (or $k$ differences) seems to be much harder: All known data structures have exponential dependency on $k$ either in the space, or in the time bound. We provide conditional and pointer-machine lower bounds that make a step toward explaining this phenomenon. We start by demonstrating lower bounds for $k = Θ(\log n)$. We show that assuming the Strong Exponential Time Hypothesis, any data structure for text indexing that can be constructed in polynomial time cannot have $\mathcal{O}(n^{1-δ})$ query time, for any $δ>0$. This bound also extends to the setting where we only ask for $(1+\varepsilon)$-approximate solutions for text indexing. However, in many applications the value of $k$ is rather small, and one might hope that for small~$k$ we can develop more efficient solutions. We show that this would require a radically new approach as using the current methods one cannot avoid exponential dependency on $k$ either in the space, or in the time bound for all even $\frac{8}{\sqrt{3}} \sqrt{\log n} \le k = o(\log n)$. Our lower bounds also apply to the dictionary look-up problem, where instead of a text one is given a set of strings.
The alignment of public research supply and industry demand for effective technology transfer: the case of Italy
Venue: arXiv cs.DL 2018
Year: 2018

Italy lags quite behind vis-a'-vis other industrialized countries, in public to private technology transfer. One of the possible causes might be the mismatch between new knowledge supplied by public research and industry demand. We test this hypothesis through a survey of leading public research scientists in four high-tech sectors. The findings show that most research project results seem to be of immediate industrial interest, which contrasts with the low patent and licensing performances of Italian public research institutions. For one third of all the results of the research, there are no Italian companies able to exploit them. The same, however, is not true for the remaining results, which shows that the misalignment between public supply and industry demand alone cannot account for poor technology transfer. What emerges from our investigation is that a closer coordination of research policy and industrial policy is required, as well as closer attention to initiatives which may support the transfer of public research results to domestic industry.
A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network
Venue: arXiv cs.CV 2018
Year: 2018

Image denoising is a classical problem in low level computer vision. Model-based optimization methods and deep learning approaches have been the two main strategies for solving the problem. Model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming. In contrast, deep learning methods have fast testing speed but the performance of these CNNs is still inferior. To address this issue, here we propose a novel deep residual learning model that combines the dilated residual convolution and multi-scale convolution groups. Due to the complex patterns and structures of inside an image, the multiscale convolution group is utilized to learn those patterns and enlarge the receptive field. Specifically, the residual connection and batch normalization are utilized to speed up the training process and maintain the denoising performance. In order to decrease the gridding artifacts, we integrate the hybrid dilated convolution design into our model. To this end, this paper aims to train a lightweight and effective denoiser based on multiscale convolution group. Experimental results have demonstrated that the enhanced denoiser can not only achieve promising denoising results, but also become a strong competitor in practical application.
Speeding-up the Verification Phase of Set Similarity Joins in the GPGPU paradigm
Venue: arXiv cs.DB 2018
Year: 2018

We investigate the problem of exact set similarity joins using a co-process CPU-GPU scheme. The state-of-the-art CPU solutions split the wok in two main phases. First, filtering and index building takes place to reduce the candidate sets to be compared as much as possible; then the pairs are compared to verify whether they should become part of the result. We investigate in-depth solutions for transferring the second, so-called verification phase, to the GPU addressing several challenges regarding the data serialization and layout, the thread management and the techniques to compare sets of tokens. Using real datasets, we provide concrete experimental proofs that our solutions have reached their maximum potential, since they totally overlap verification with CPU tasks, and manage to yield significant speed-ups, up to 2.6X in our cases.
Sources of Complexity in Semantic Frame Parsing for Information Extraction
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes a Semantic Frame parsing System based on sequence labeling methods, precisely BiLSTM models with highway connections, for performing information extraction on a corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The approach proposed in this study relies on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The purpose of this study is to analyze the task complexity, to highlight the factors that make Semantic Frame parsing a difficult task and to provide detailed evaluations of the performance on different types of frames and sentences.
Learning from Web Data: the Benefit of Unsupervised Object Localization
Venue: arXiv cs.CV 2018
Year: 2018

Annotating a large number of training images is very time-consuming. In this background, this paper focuses on learning from easy-to-acquire web data and utilizes the learned model for fine-grained image classification in labeled datasets. Currently, the performance gain from training with web data is incremental, like a common saying "better than nothing, but not by much". Conventionally, the community looks to correcting the noisy web labels to select informative samples. In this work, we first systematically study the built-in gap between the web and standard datasets, i.e. different data distributions between the two kinds of data. Then, in addition to using web labels, we present an unsupervised object localization method, which provides critical insights into the object density and scale in web images. Specifically, we design two constraints on web data to substantially reduce the difference of data distributions for the web and standard datasets. First, we present a method to control the scale, localization and number of objects in the detected region. Second, we propose to select the regions containing objects that are consistent with the web tag. Based on the two constraints, we are able to process web images to reduce the gap, and the processed web data is used to better assist the standard dataset to train CNNs. Experiments on several fine-grained image classification datasets confirm that our method performs favorably against the state-of-the-art methods.
Polygonal approximation of digital planar curve using novel significant measure
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents an iterative smoothing technique for polygonal approximation of digital image boundary. The technique starts with finest initial segmentation points of a curve. The contribution of initially segmented points towards preserving the original shape of the image boundary is determined by computing the significant measure of every initial segmentation points which is sensitive to sharp turns, which may be missed easily when conventional significant measures are used for detecting dominant points. The proposed method differentiates between the situations when a point on the curve between two points on a curve projects directly upon the line segment or beyond this line segment. It not only identifies these situations, but also computes its significant contribution for these situations differently. This situation-specific treatment allows preservation of points with high curvature even as revised set of dominant points are derived. The experimental results show that the proposed technique competes well with the state of the art techniques.
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone
Venue: arXiv cs.AI 2018
Year: 2018

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.
Human-AI Learning Performance in Multi-Armed Bandits
Venue: arXiv cs.AI 2018
Year: 2018

People frequently face challenging decision-making problems in which outcomes are uncertain or unknown. Artificial intelligence (AI) algorithms exist that can outperform humans at learning such tasks. Thus, there is an opportunity for AI agents to assist people in learning these tasks more effectively. In this work, we use a multi-armed bandit as a controlled setting in which to explore this direction. We pair humans with a selection of agents and observe how well each human-agent team performs. We find that team performance can beat both human and agent performance in isolation. Interestingly, we also find that an agent's performance in isolation does not necessarily correlate with the human-agent team's performance. A drop in agent performance can lead to a disproportionately large drop in team performance, or in some settings can even improve team performance. Pairing a human with an agent that performs slightly better than them can make them perform much better, while pairing them with an agent that performs the same can make them them perform much worse. Further, our results suggest that people have different exploration strategies and might perform better with agents that match their strategy. Overall, optimizing human-agent team performance requires going beyond optimizing agent performance, to understanding how the agent's suggestions will influence human decision-making.
Derandomized Distributed Multi-resource Allocation with Little Communication Overhead
Venue: arXiv cs.SY 2018
Year: 2018

We study a class of distributed optimization problems for multiple shared resource allocation in Internet-connected devices. We propose a derandomized version of an existing stochastic additive-increase and multiplicative-decrease (AIMD) algorithm. The proposed solution uses one bit feedback signal for each resource between the system and the Internet-connected devices and does not require inter-device communication. Additionally, the Internet-connected devices do not compromise their privacy and the solution does not dependent on the number of participating devices. In the system, each Internet-connected device has private cost functions which are strictly convex, twice continuously differentiable and increasing. We show empirically that the long-term average allocations of multiple shared resources converge to optimal allocations and the system achieves minimum social cost. Furthermore, we show that the proposed derandomized AIMD algorithm converges faster than the stochastic AIMD algorithm and both the approaches provide approximately same solutions.
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application
Venue: arXiv cs.AI 2018
Year: 2018

In this paper, we present a method using AI techniques to solve a case of pure mathematics applications for finding narrow admissible tuples. The original problem is formulated into a combinatorial optimization problem. In particular, we show how to exploit the local search structure to formulate the problem landscape for dramatic reductions in search space and for non-trivial elimination in search barriers, and then to realize intelligent search strategies for effectively escaping from local minima. Experimental results demonstrate that the proposed method is able to efficiently find best known solutions. This research sheds light on exploiting the local problem structure for an efficient search in combinatorial landscapes as an application of AI to a new problem domain.
Reversible Data Hiding in Encrypted Images based on MSB Prediction and Huffman Coding
Venue: arXiv cs.MM 2018
Year: 2018

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) has attracted increasing attention as a technology that can embed additional data in the encryption domain. In general, an RDHEI method embeds secret data in an encrypted image while ensuring that the embedded data can be extracted error-free and the original image can be restored lossless. In this paper, A high-capacity RDHEI algorithm is proposed. At first, the Most Significant Bits (MSB) of each pixel was predicted adaptively and marked by Huffman coding in the original image. Then, the image was encrypted by a stream cipher method. At last, the vacated space can be used to embed additional data. Experimental results show that our method achieved higher embedding capacity while comparing with the state-of-the-art methods.
Estimation and Restoration of Compositional Degradation Using Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Image restoration from a single image degradation type, such as blurring, hazing, random noise, and compression has been investigated for decades. However, image degradations in practice are often a mixture of several types of degradation. Such compositional degradations complicate restoration because they require the differentiation of different degradation types and levels. In this paper, we propose a convolutional neural network (CNN) model for estimating the degradation properties of a given degraded image. Furthermore, we introduce an image restoration CNN model that adopts the estimated degradation properties as its input. Experimental results show that the proposed degradation estimation model can successfully infer the degradation properties of compositionally degraded images. The proposed restoration model can restore degraded images by exploiting the estimated degradation properties and can achieve both blind and nonblind image restorations.