OpenRaiser/Intern-Atlas huggingface dataset:
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Venue: arXiv cs.IR 2018
Year: 2018

Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multi-relational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items in a unified embedding space where a category-specific relation (category-comp-category) is modeled as a vector translation operating on the embeddings of compatible items from the corresponding categories. By this way, we not only capture the specific notion of compatibility conditioned on a specific pair of complementary categories, but also preserve the global notion of compatibility. We also design a deep fashion item encoder which exploits the complementary characteristic of visual and textual features to represent the fashion products. To the best of our knowledge, this is the first work that uses category-specific complementary relations to model the category-aware compatibility between items in a translation-based embedding space. Extensive experiments demonstrate the effectiveness of TransNFCM over the state-of-the-arts on two real-world datasets.
Sequence to Sequence Learning for Query Expansion
Venue: arXiv cs.IR 2018
Year: 2018

Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction. We therefore assessed the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings' space.
Motion Selective Prediction for Video Frame Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

Existing conditional video prediction approaches train a network from large databases and generalize to previously unseen data. We take the opposite stance, and introduce a model that learns from the first frames of a given video and extends its content and motion, to, eg, double its length. To this end, we propose a dual network that can use in a flexible way both dynamic and static convolutional motion kernels, to predict future frames. The construct of our model gives us the the means to efficiently analyze its functioning and interpret its output. We demonstrate experimentally the robustness of our approach on challenging videos in-the-wild and show that it is competitive wrt related baselines.
Dropout Regularization in Hierarchical Mixture of Experts
Venue: arXiv cs.LG 2018
Year: 2018

Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years. Hierarchical mixture of experts is a hierarchically gated model that defines a soft decision tree where leaves correspond to experts and decision nodes correspond to gating models that softly choose between its children, and as such, the model defines a soft hierarchical partitioning of the input space. In this work, we propose a variant of dropout for hierarchical mixture of experts that is faithful to the tree hierarchy defined by the model, as opposed to having a flat, unitwise independent application of dropout as one has with multi-layer perceptrons. We show that on a synthetic regression data and on MNIST and CIFAR-10 datasets, our proposed dropout mechanism prevents overfitting on trees with many levels improving generalization and providing smoother fits.
A Whole Slide Image Grading Benchmark and Tissue Classification for Cervical Cancer Precursor Lesions with Inter-Observer Variability
Venue: arXiv cs.CV 2018
Year: 2018

The cervical cancer developing from the precancerous lesions caused by the Human Papilloma Virus (HPV) has been one of the preventable cancers with the help of periodic screening. There are two types of grading conventions widely accepted among pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is introduced. The papillae of the cervical epithelium and overlapping cell problems are handled and a tissue classification method with a novel morphological feature exploiting the relative orientation between the BM and the major axis of all nuclei is developed and its performance is evaluated. Besides, the inter-observer variability is also revealed by a thorough comparison among pathologists' decisions, as well as, the final diagnosis.
DBpedia NIF: Open, Large-Scale and Multilingual Knowledge Extraction Corpus
Venue: arXiv cs.CL 2018
Year: 2018

In the past decade, the DBpedia community has put significant amount of effort on developing technical infrastructure and methods for efficient extraction of structured information from Wikipedia. These efforts have been primarily focused on harvesting, refinement and publishing semi-structured information found in Wikipedia articles, such as information from infoboxes, categorization information, images, wikilinks and citations. Nevertheless, still vast amount of valuable information is contained in the unstructured Wikipedia article texts. In this paper, we present DBpedia NIF - a large-scale and multilingual knowledge extraction corpus. The aim of the dataset is two-fold: to dramatically broaden and deepen the amount of structured information in DBpedia, and to provide large-scale and multilingual language resource for development of various NLP and IR task. The dataset provides the content of all articles for 128 Wikipedia languages. We describe the dataset creation process and the NLP Interchange Format (NIF) used to model the content, links and the structure the information of the Wikipedia articles. The dataset has been further enriched with about 25% more links and selected partitions published as Linked Data. Finally, we describe the maintenance and sustainability plans, and selected use cases of the dataset from the TextExt knowledge extraction challenge.
Implementation of Simplex Channels in the Blom's Keys Pre-Distribution Scheme
Venue: arXiv cs.CR 2018
Year: 2018

In article the modification of the Blom's keys preliminary distribution scheme, considering the direction of information streams is suggested. For this modification it is necessary to use function from three variables. Function of formation of key materials will be the asymmetrical. The exponential form of this function which does not increase the volume of key materials is suggested.
Bitcoin Payment-channels for Resource Limited IoT Devices
Venue: arXiv cs.CR 2018
Year: 2018

Resource-constrained devices are unable to maintain a full copy of the Bitcoin Blockchain in memory. This paper proposes a bidirectional payment channel framework for IoT devices. This framework utilizes Bitcoin Lightning-Network-like payment channels with low processing and storage requirements. This protocol enables IoT devices to open and maintain payment channels with traditional Bitcoin nodes without a view of the blockchain. Unlike existing solutions, it does not require a trusted third party to interact with the blockchain nor does it burden the peer-to-peer network in the way SPV clients do. The contribution of this paper includes a secure and crypto-economically fair protocol for bidirectional Bitcoin payment channels. In addition, we demonstrate the security and fairness of the protocol by formulating it as a game in which the equilibrium is reached when all players follow the protocol.
Quantized-Dialog Language Model for Goal-Oriented Conversational Systems
Venue: arXiv cs.CL 2018
Year: 2018

We propose a novel methodology to address dialog learning in the context of goal-oriented conversational systems. The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation. The language model relies on n-grams associated with clusters of utterances. This quantized-dialog language model methodology has been applied to the end-to-end goal-oriented track of the latest Dialog System Technology Challenges (DSTC6). The objective is to find the correct system utterance from a pool of candidates in order to complete a dialog between a user and an automated restaurant-reservation system. Our results show that the technique proposed in this paper achieves high accuracy regarding selection of the correct candidate utterance, and outperforms other state-of-the-art approaches based on neural networks.
Studying oppressive cityscapes of Bangladesh
Venue: arXiv cs.CY 2018
Year: 2018

In a densely populated city like Dhaka (Bangladesh), a growing number of high-rise buildings is an inevitable reality. However, they pose mental health risks for citizens in terms of detachment from natural light, sky view, greenery, and environmental landscapes. The housing economy and rent structure in different areas may or may not take account of such environmental factors. In this paper, we build a computer vision based pipeline to study factors like sky visibility, greenery in the sidewalks, and dominant colors present in streets from a pedestrian's perspective. We show that people in lower economy classes may suffer from lower sky visibility, whereas people in higher economy classes may suffer from lack of greenery in their environment, both of which could be possibly addressed by implementing rent restructuring schemes.
Image Encryption Algorithm Using Natural Interval Extensions
Venue: arXiv cs.CR 2018
Year: 2018

It is known that chaotic systems have widely been used in cryptography. Generally, floating point simulations are used to generate pseudo-random sequence of numbers. Although, it is possible to find some works on the degradation of chaotic systems due to finite precision of digital computers, little attention has been paid to exploit this limitation to formulate efficient process for image encode. This article proposes a novel image encryption method using natural interval extensions. The sequence of arithmetic operations is different in each natural interval extension. This is what we need to produce two different sequences; the difference between these sequences is used to generate the lower bound error, which has been shown to present satisfactory pseudo-random properties. The approach has been successfully tested using the Chua's circuit as the chaotic system. The secret key has presented good properties for encrypting the Lena image.
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
Venue: arXiv cs.LG 2018
Year: 2018

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.
Chart-Text: A Fully Automated Chart Image Descriptor
Venue: arXiv cs.CV 2018
Year: 2018

Images greatly help in understanding, interpreting and visualizing data. Adding textual description to images is the first and foremost principle of web accessibility. Visually impaired users using screen readers will use these textual descriptions to get better understanding of images present in digital contents. In this paper, we propose Chart-Text a novel fully automated system that creates textual description of chart images. Given a PNG image of a chart, our Chart-Text system creates a complete textual description of it. First, the system classifies the type of chart and then it detects and classifies the labels and texts in the charts. Finally, it uses specific image processing algorithms to extract relevant information from the chart images. Our proposed system achieves an accuracy of 99.72% in classifying the charts and an accuracy of 78.9% in extracting the data and creating the corresponding textual description.
We all do better when we work together?
Venue: arXiv cs.NI 2018
Year: 2018

This paper evaluates the impact of a RD signal on traffic crossing the mobile network through a set of experiments in a simulated 5G network scenario built with ns-3. In our experiments, we compare a scenario with no RD treatment (i.e. a single best--effort EPS bearer) with scenarios with RD treatment (i.e. separate EPS bearers to carry RD--partitioned traffic) with honest and cheating users. Our objective is twofold. On the one hand, we want to explore the suitability of RD as a way to harmonise the LTE and Internet QoS models and on the other hand, we aim at providing data to inform protocol design as well as operations-related discussion on the notion of exposing a 1-bit, clear-text \ac{rd} signal from endpoints to the network path when the use of end-to-end encrypted protocols would otherwise prevent flow classification based on DPI. Specifically, the question we want help answer is whether the gain in terms of end users' QoE and radio spectrum efficiency is enough to justify making room for such signal. All the experiments are fully documented and the tooling used is made available as open-source to ensure their reproducibility.
No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between the color channels and the two-dimensional entropy are calculated. In the frequency domain, the two-dimensional entropy and the mutual information of the filtered sub-band images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The source code is available on github https://github.com/jacob6/ENIQA.
Future semantic segmentation of time-lapsed videos with large temporal displacement
Venue: arXiv cs.CV 2018
Year: 2018

An important aspect of video understanding is the ability to predict the evolution of its content in the future. This paper presents a future frame semantic segmentation technique for predicting semantic masks of the current and future frames in a time-lapsed video. We specifically focus on time-lapsed videos with large temporal displacement to highlight the model's ability to capture large motions in time. We first introduce a unique semantic segmentation prediction dataset with over 120,000 time-lapsed sky-video frames and all corresponding semantic masks captured over a span of five years in North America region. The dataset has immense practical value for cloud cover analysis, which are treated as non-rigid objects of interest. %Here the model provides both semantic segmentation of cloud region and solar irradiance emitted from a region from the sky-videos. Next, our proposed recurrent network architecture departs from existing trend of using temporal convolutional networks (TCN) (or feed-forward networks), by explicitly learning an internal representations for the evolution of video content with time. Experimental evaluation shows an improvement of mean IoU over TCNs in the segmentation task by 10.8% for 10 mins (21% over 60 mins) ahead of time predictions. Further, our model simultaneously measures both the current and future solar irradiance from the same video frames with a normalized-MAE of 10.5% over two years. These results indicate that recurrent memory networks with attention mechanism are able to capture complex advective and diffused flow characteristic of dense fluids even with sparse temporal sampling and are more suitable for future frame prediction tasks for longer duration videos.
The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
Venue: arXiv cs.CL 2018
Year: 2018

Clickbait has grown to become a nuisance to social media users and social media operators alike. Malicious content publishers misuse social media to manipulate as many users as possible to visit their websites using clickbait messages. Machine learning technology may help to handle this problem, giving rise to automatic clickbait detection. To accelerate progress in this direction, we organized the Clickbait Challenge 2017, a shared task inviting the submission of clickbait detectors for a comparative evaluation. A total of 13 detectors have been submitted, achieving significant improvements over the previous state of the art in terms of detection performance. Also, many of the submitted approaches have been published open source, rendering them reproducible, and a good starting point for newcomers. While the 2017 challenge has passed, we maintain the evaluation system and answer to new registrations in support of the ongoing research on better clickbait detectors.
Hypergraph Clustering: A Modularity Maximization Approach
Venue: arXiv cs.LG 2018
Year: 2018

Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we generalize the framework of modularity maximization for clustering on hypergraphs. To this end, we introduce a hypergraph null model, analogous to the configuration model on undirected graphs, and a node-degree preserving reduction to work with this model. This is used to define a modularity function that can be maximized using the popular and fast Louvain algorithm. We additionally propose a refinement over this clustering, by reweighting cut hyperedges in an iterative fashion. The efficacy and efficiency of our methods are demonstrated on several real-world datasets.
Spatiotemporal Data Fusion for Precipitation Nowcasting
Venue: arXiv cs.CV 2018
Year: 2018

Precipitation nowcasting using neural networks and ground-based radars has become one of the key components of modern weather prediction services, but it is limited to the regions covered by ground-based radars. Truly global precipitation nowcasting requires fusion of radar and satellite observations. We propose the data fusion pipeline based on computer vision techniques, including novel inpainting algorithm with soft masking.
The Power of Distributed Verifiers in Interactive Proofs
Venue: arXiv cs.DC 2018
Year: 2018

We explore the power of interactive proofs with a distributed verifier. In this setting, the verifier consists of $n$ nodes and a graph $G$ that defines their communication pattern. The prover is a single entity that communicates with all nodes by short messages. The goal is to verify that the graph $G$ belongs to some language in a small number of rounds, and with small communication bound, i.e., the proof size. This interactive model was introduced by Kol, Oshman and Saxena (PODC 2018) as a generalization of non-interactive distributed proofs. They demonstrated the power of interaction in this setting by constructing protocols for problems as Graph Symmetry and Graph Non-Isomorphism -- both of which require proofs of $Ω(n^2)$-bits without interaction. In this work, we provide a new general framework for distributed interactive proofs that allows one to translate standard interactive protocols to ones where the verifier is distributed with short proof size. We show the following: * Every (centralized) computation that can be performed in time $O(n)$ can be translated into three-round distributed interactive protocol with $O(\log n)$ proof size. This implies that many graph problems for sparse graphs have succinct proofs. * Every (centralized) computation implemented by either a small space or by uniform NC circuit can be translated into a distributed protocol with $O(1)$ rounds and $O(\log n)$ bits proof size for the low space case and $polylog(n)$ many rounds and proof size for NC. * We show that for Graph Non-Isomorphism, there is a 4-round protocol with $O(\log n)$ proof size, improving upon the $O(n \log n)$ proof size of Kol et al. * For many problems we show how to reduce proof size below the naturally seeming barrier of $\log n$. We get a 5-round protocols with proof size $O(\log \log n)$ for a family of problems.
What did you see? Personalization, regionalization and the question of the filter bubble in Google's search engine
Venue: arXiv cs.CY 2018
Year: 2018

This report analyzes the Google search results from more than 1,500 volunteer data donors who, in the five weeks leading up to the federal election on September 24th, 2017, automatically searched Google for 16 predefined names of political parties and politicians every four hours. It is based on an adjusted database consisting of more than 8,000,000 data records, which were generated in the context of the research project "#Datenspende: Google und die Bundestagswahl 2017" and sent to us for evaluation. The #Datenspende project was commissioned by six state media authorities. Spiegel Online acted as a media partner. Focal points of the present study are, i.a., the question of the degree of personalization of search results, the proportion of regionalization and the risk of algorithm-based filter bubble formation or reinforcement by the leader in the search engine market.