OpenRaiser/Intern-Atlas huggingface dataset:
P2P-NET: Bidirectional Point Displacement Net for Shape Transform
Venue: arXiv cs.GR 2018
Year: 2018

We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e.g., meso-skeletons and surfaces, partial and complete scans, etc. The architecture of the P2P-NET is that of a bi-directional point displacement network, which transforms a source point set to a target point set with the same cardinality, and vice versa, by applying point-wise displacement vectors learned from data. P2P-NET is trained on paired shapes from the source and target domains, but without relying on point-to-point correspondences between the source and target point sets. The training loss combines two uni-directional geometric losses, each enforcing a shape-wise similarity between the predicted and the target point sets, and a cross-regularization term to encourage consistency between displacement vectors going in opposite directions. We develop and present several different applications enabled by our general-purpose bidirectional P2P-NET to highlight the effectiveness, versatility, and potential of our network in solving a variety of point-based shape transformation problems.
Diversity and Interdisciplinarity: How Can One Distinguish and Recombine Disparity, Variety, and Balance?
Venue: arXiv cs.DL 2018
Year: 2018

The dilemma which remained unsolved using Rao-Stirling diversity, namely of how variety and balance can be combined into "dual concept diversity" (Stirling, 1998, pp. 48f.) can be clarified by using Nijssen et al.'s (1998) argument that the Gini coefficient is a perfect indicator of balance. However, the Gini coefficient is not an indicator of variety; this latter term can be operationalized independently as relative variety. The three components of diversity--variety, balance, and disparity--can thus be clearly distinguished and independently operationalized as measures varying between zero and one. The new diversity indicator ranges with more resolving power in the empirical case.
Generalized Hadamard-Product Fusion Operators for Visual Question Answering
Venue: arXiv cs.LG 2018
Year: 2018

We propose a generalized class of multimodal fusion operators for the task of visual question answering (VQA). We identify generalizations of existing multimodal fusion operators based on the Hadamard product, and show that specific non-trivial instantiations of this generalized fusion operator exhibit superior performance in terms of OpenEnded accuracy on the VQA task. In particular, we introduce Nonlinearity Ensembling, Feature Gating, and post-fusion neural network layers as fusion operator components, culminating in an absolute percentage point improvement of $1.1\%$ on the VQA 2.0 test-dev set over baseline fusion operators, which use the same features as input. We use our findings as evidence that our generalized class of fusion operators could lead to the discovery of even superior task-specific operators when used as a search space in an architecture search over fusion operators.
Aggression-annotated Corpus of Hindi-English Code-mixed Data
Venue: arXiv cs.CL 2018
Year: 2018

As the interaction over the web has increased, incidents of aggression and related events like trolling, cyberbullying, flaming, hate speech, etc. too have increased manifold across the globe. While most of these behaviour like bullying or hate speech have predated the Internet, the reach and extent of the Internet has given these an unprecedented power and influence to affect the lives of billions of people. So it is of utmost significance and importance that some preventive measures be taken to provide safeguard to the people using the web such that the web remains a viable medium of communication and connection, in general. In this paper, we discuss the development of an aggression tagset and an annotated corpus of Hindi-English code-mixed data from two of the most popular social networking and social media platforms in India, Twitter and Facebook. The corpus is annotated using a hierarchical tagset of 3 top-level tags and 10 level 2 tags. The final dataset contains approximately 18k tweets and 21k facebook comments and is being released for further research in the field.
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Venue: arXiv cs.MM 2018
Year: 2018

Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The~experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100\% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.
Semantic See-Through Rendering on Light Fields
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel semantic light field (LF) refocusing technique that can achieve unprecedented see-through quality. Different from prior art, our semantic see-through (SST) differentiates rays in their semantic meaning and depth. Specifically, we combine deep learning and stereo matching to provide each ray a semantic label. We then design tailored weighting schemes for blending the rays. Although simple, our solution can effectively remove foreground residues when focusing on the background. At the same time, SST maintains smooth transitions in varying focal depths. Comprehensive experiments on synthetic and new real indoor and outdoor datasets demonstrate the effectiveness and usefulness of our technique.
Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
Venue: arXiv cs.IR 2018
Year: 2018

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em topic matrix factorization} (Topic MF) successfully exploit social relations and item reviews, respectively, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.
BoSy: An Experimentation Framework for Bounded Synthesis
Venue: arXiv cs.LO 2018
Year: 2018

We present BoSy, a reactive synthesis tool based on the bounded synthesis approach. Bounded synthesis ensures the minimality of the synthesized implementation by incrementally increasing a bound on the size of the solutions it considers. For each bound, the existence of a solution is encoded as a logical constraint solving problem that is solved by an appropriate solver. BoSy constructs bounded synthesis encodings into SAT, QBF, DQBF, EPR, and SMT, and interfaces to solvers of the corresponding type. When supported by the solver, BoSy extracts solutions as circuits, which can, if desired, be verified with standard hardware model checkers. BoSy won the LTL synthesis track at SYNTCOMP 2016. In addition to its use as a synthesis tool, BoSy can also be used as an experimentation and performance evaluation framework for various types of satisfiability solvers.
Discrete Morse theory for the collapsibility of supremum sections
Venue: arXiv cs.DM 2018
Year: 2018

The Dushnik-Miller dimension of a poset $\le$ is the minimal number $d$ of linear extensions $\le_1, \ldots , \le_d$ of $\le$ such that $\le$ is the intersection of $\le_1, \ldots , \le_d$. Supremum sections are simplicial complexes introduced by Scarf and are linked to the Dushnik-Miller as follows: the inclusion poset of a simplicial complex is of Dushnik-Miller dimension at most $d$ if and only if it is included in a supremum section coming from a representation of dimension $d$. Collapsibility is a topoligical property of simplicial complexes which has been introduced by Whitehead and which resembles to shellability. While Ossona de Mendez proved in that a particular type of supremum sections are shellable, we show in this article that supremum sections are in general collapsible thanks to the discrete Morse theory developped by Forman.
Secure Web Access Control Algorithm
Venue: arXiv cs.CR 2018
Year: 2018

The paper presents a flexible and efficient method to secure the access to a Web site implemented in PHP script language. The algorithm is based on the PHP session mechanism. The proposed method is a general one and offers the possibility to implement a PHP based secured access to a Web site, through a portal page and using an additional script included in any site page, which is required to be accessed only by registered users. This paper presents the design, implementation and integration of the algorithm on any generic WEB site.
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
Venue: arXiv cs.CV 2018
Year: 2018

In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision towards a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 27x faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search towards promising neural-network configurations.
A multilayer backpropagation saliency detection algorithm and its applications
Venue: arXiv cs.CV 2018
Year: 2018

Saliency detection is an active topic in the multimedia field. Most previous works on saliency detection focus on 2D images. However, these methods are not robust against complex scenes which contain multiple objects or complex backgrounds. Recently, depth information supplies a powerful cue for saliency detection. In this paper, we propose a multilayer backpropagation saliency detection algorithm based on depth mining by which we exploit depth cue from three different layers of images. The proposed algorithm shows a good performance and maintains the robustness in complex situations. Experiments' results show that the proposed framework is superior to other existing saliency approaches. Besides, we give two innovative applications by this algorithm, such as scene reconstruction from multiple images and small target object detection in video.
Algorithm Configuration: Learning policies for the quick termination of poor performers
Venue: arXiv cs.AI 2018
Year: 2018

One way to speed up the algorithm configuration task is to use short runs instead of long runs as much as possible, but without discarding the configurations that eventually do well on the long runs. We consider the problem of selecting the top performing configurations of the Conditional Markov Chain Search (CMCS), a general algorithm schema that includes, for examples, VNS. We investigate how the structure of performance on short tests links with those on long tests, showing that significant differences arise between test domains. We propose a "performance envelope" method to exploit the links; that learns when runs should be terminated, but that automatically adapts to the domain.
On Chatbots Exhibiting Goal-Directed Autonomy in Dynamic Environments
Venue: arXiv cs.AI 2018
Year: 2018

Conversation interfaces (CIs), or chatbots, are a popular form of intelligent agents that engage humans in task-oriented or informal conversation. In this position paper and demonstration, we argue that chatbots working in dynamic environments, like with sensor data, can not only serve as a promising platform to research issues at the intersection of learning, reasoning, representation and execution for goal-directed autonomy; but also handle non-trivial business applications. We explore the underlying issues in the context of Water Advisor, a preliminary multi-modal conversation system that can access and explain water quality data.
Min-Max Tours for Task Allocation to Heterogeneous Agents
Venue: arXiv cs.MA 2018
Year: 2018

We consider a scenario consisting of a set of heterogeneous mobile agents located at a depot, and a set of tasks dispersed over a geographic area. The agents are partitioned into different types. The tasks are partitioned into specialized tasks that can only be done by agents of a certain type, and generic tasks that can be done by any agent. The distances between each pair of tasks are specified, and satisfy the triangle inequality. Given this scenario, we address the problem of allocating these tasks among the available agents (subject to type compatibility constraints) while minimizing the maximum cost to tour the allocation by any agent and return to the depot. This problem is NP-hard, and we give a three phase algorithm to solve this problem that provides 5-factor approximation, regardless of the total number of agents and the number of agents of each type. We also show that in the special case where there is only one agent of each type, the algorithm has an approximation factor of 4.
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
Venue: arXiv cs.CV 2018
Year: 2018

Recent advances in computer vision using deep learning with RGB imagery (e.g., object recognition and detection) have been made possible thanks to the development of large annotated RGB image datasets. In contrast, multispectral image (MSI) and hyperspectral image (HSI) datasets contain far fewer labeled images, in part due to the wide variety of sensors used. These annotations are especially limited for semantic segmentation, or pixel-wise classification, of remote sensing imagery because it is labor intensive to generate image annotations. Low-shot learning algorithms can make effective inferences despite smaller amounts of annotated data. In this paper, we study low-shot learning using self-taught feature learning for semantic segmentation. We introduce 1) an improved self-taught feature learning framework for HSI and MSI data and 2) a semi-supervised classification algorithm. When these are combined, they achieve state-of-the-art performance on remote sensing datasets that have little annotated training data available. These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery.
Accelerating Empowerment Computation with UCT Tree Search
Venue: arXiv cs.AI 2018
Year: 2018

Models of intrinsic motivation present an important means to produce sensible behaviour in the absence of extrinsic rewards. Applications in video games are varied, and range from intrinsically motivated general game-playing agents to non-player characters such as companions and enemies. The information-theoretic quantity of Empowerment is a particularly promising candidate motivation to produce believable, generic and robust behaviour. However, while it can be used in the absence of external reward functions that would need to be crafted and learned, empowerment is computationally expensive. In this paper, we propose a modified UCT tree search method to mitigate empowerment's computational complexity in discrete and deterministic scenarios. We demonstrate how to modify a Monte-Carlo Search Tree with UCT to realise empowerment maximisation, and discuss three additional modifications that facilitate better sampling. We evaluate the approach both quantitatively, by analysing how close our approach gets to the baseline of exhaustive empowerment computation with varying amounts of computational resources, and qualitatively, by analysing the resulting behaviour in a Minecraft-like scenario.
A Web Scraping Methodology for Bypassing Twitter API Restrictions
Venue: arXiv cs.IR 2018
Year: 2018

Retrieving information from social networks is the first and primordial step many data analysis fields such as Natural Language Processing, Sentiment Analysis and Machine Learning. Important data science tasks relay on historical data gathering for further predictive results. Most of the recent works use Twitter API, a public platform for collecting public streams of information, which allows querying chronological tweets for no more than three weeks old. In this paper, we present a new methodology for collecting historical tweets within any date range using web scraping techniques bypassing for Twitter API restrictions.
The Future of CISE Distributed Research Infrastructure
Venue: arXiv cs.NI 2018
Year: 2018

Shared research infrastructure that is globally distributed and widely accessible has been a hallmark of the networking community. This paper presents an initial snapshot of a vision for a possible future of mid-scale distributed research infrastructure aimed at enabling new types of research and discoveries. The paper is written from the perspective of "lessons learned" in constructing and operating the Global Environment for Network Innovations (GENI) infrastructure and attempts to project future concepts and solutions based on these lessons. The goal of this paper is to engage the community to contribute new ideas and to inform funding agencies about future research directions to realize this vision.
Reconfigurable Antenna Multiple Access for 5G mmWave Systems
Venue: arXiv cs.IT 2018
Year: 2018

This paper aims to realize a new multiple access technique based on recently proposed millimeter-wave reconfigurable antenna architectures. To this end, first we show that integration of the existing reconfigurable antenna systems with the well-known non-orthogonal multiple access (NOMA) technique causes a significant degradation in sum rate due to the inevitable power division in reconfigurable antennas. To circumvent this fundamental limit, a new multiple access technique is proposed. The technique which is called reconfigurable antenna multiple access (RAMA) transmits only each user's intended signal at the same time/frequency/code, which makes RAMA an inter-user interference-free technique. Two different cases are considered, i.e., RAMA with partial and full channel state information (CSI). In the first case, CSI is not required and only the direction of arrival for a specific user is used. Our analytical results indicate that with partial CSI and for symmetric channels, RAMA outperforms NOMA in terms of sum rate. Further, the analytical result indicates that RAMA for asymmetric channels achieves better sum rate than NOMA when less power is assigned to users that experience better channel quality. In the second case, RAMA with full CSI allocates optimal power to each user which leads to higher achievable rates compared to NOMA for both symmetric and asymmetric channels. The numerical computations demonstrate the analytical findings.
Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions
Venue: arXiv cs.CV 2018
Year: 2018

Depthwise convolutions provide significant performance benefits owing to the reduction in both parameters and mult-adds. However, training depthwise convolution layers with GPUs is slow in current deep learning frameworks because their implementations cannot fully utilize the GPU capacity. To address this problem, in this paper we present an efficient method (called diagonalwise refactorization) for accelerating the training of depthwise convolution layers. Our key idea is to rearrange the weight vectors of a depthwise convolution into a large diagonal weight matrix so as to convert the depthwise convolution into one single standard convolution, which is well supported by the cuDNN library that is highly-optimized for GPU computations. We have implemented our training method in five popular deep learning frameworks. Evaluation results show that our proposed method gains $15.4\times$ training speedup on Darknet, $8.4\times$ on Caffe, $5.4\times$ on PyTorch, $3.5\times$ on MXNet, and $1.4\times$ on TensorFlow, compared to their original implementations of depthwise convolutions.
A Faster FPTAS for the Subset-Sums Ratio Problem
Venue: arXiv cs.DS 2018
Year: 2018

The Subset-Sums Ratio problem (SSR) is an optimization problem in which, given a set of integers, the goal is to find two subsets such that the ratio of their sums is as close to 1 as possible. In this paper we develop a new FPTAS for the SSR problem which builds on techniques proposed in [D. Nanongkai, Simple FPTAS for the subset-sums ratio problem, Inf. Proc. Lett. 113 (2013)]. One of the key improvements of our scheme is the use of a dynamic programming table in which one dimension represents the difference of the sums of the two subsets. This idea, together with a careful choice of a scaling parameter, yields an FPTAS that is several orders of magnitude faster than the best currently known scheme of [C. Bazgan, M. Santha, Z. Tuza, Efficient approximation algorithms for the Subset-Sums Equality problem, J. Comp. System Sci. 64 (2) (2002)].
Minimal Linear Codes over Finite Fields
Venue: arXiv cs.IT 2018
Year: 2018

As a special class of linear codes, minimal linear codes have important applications in secret sharing and secure two-party computation. Constructing minimal linear codes with new and desirable parameters has been an interesting research topic in coding theory and cryptography. Ashikhmin and Barg showed that $w_{\min}/w_{\max}> (q-1)/q$ is a sufficient condition for a linear code over the finite field $\gf(q)$ to be minimal, where $q$ is a prime power, $w_{\min}$ and $w_{\max}$ denote the minimum and maximum nonzero weights in the code, respectively. The first objective of this paper is to present a sufficient and necessary condition for linear codes over finite fields to be minimal. The second objective of this paper is to construct an infinite family of ternary minimal linear codes satisfying $w_{\min}/w_{\max}\leq 2/3$. To the best of our knowledge, this is the first infinite family of nonbinary minimal linear codes violating Ashikhmin and Barg's condition.