OpenRaiser/Intern-Atlas huggingface dataset:
Visual Affordance and Function Understanding: A Survey
Venue: arXiv cs.CV 2018
Year: 2018

Nowadays, robots are dominating the manufacturing, entertainment and healthcare industries. Robot vision aims to equip robots with the ability to discover information, understand it and interact with the environment. These capabilities require an agent to effectively understand object affordances and functionalities in complex visual domains. In this literature survey, we first focus on Visual affordances and summarize the state of the art as well as open problems and research gaps. Specifically, we discuss sub-problems such as affordance detection, categorization, segmentation and high-level reasoning. Furthermore, we cover functional scene understanding and the prevalent functional descriptors used in the literature. The survey also provides necessary background to the problem, sheds light on its significance and highlights the existing challenges for affordance and functionality learning.
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Venue: arXiv cs.CV 2018
Year: 2018

Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of computer vision algorithms in order to analyse the information captured from an on-board camera. Such applications include detecting vehicles for emergency response and traffic monitoring. This paper therefore, explores the trade-offs involved in the development of a single-shot object detector based on deep convolutional neural networks (CNNs) that can enable UAVs to perform vehicle detection under a resource constrained environment such as in a UAV. The paper presents a holistic approach for designing such systems; the data collection and training stages, the CNN architecture, and the optimizations necessary to efficiently map such a CNN on a lightweight embedded processing platform suitable for deployment on UAVs. Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall accuracy of ~95%. Overall, the proposed architecture is suitable for UAV applications, utilizing low-power embedded processors that can be deployed on commercial UAVs.
Unveiling Scholarly Communities over Knowledge Graphs
Venue: arXiv cs.DL 2018
Year: 2018

Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables the implementation of knowledge-driven tasks such as semantic retrieval, query processing, and question answering, as well as solutions to knowledge discovery tasks including pattern discovery and link prediction. In this paper, we tackle the problem of knowledge discovery in scholarly knowledge graphs, i.e., graphs that integrate scholarly data, and present Korona, a knowledge-driven framework able to unveil scholarly communities for the prediction of scholarly networks. Korona implements a graph partition approach and relies on semantic similarity measures to determine relatedness between scholarly entities. As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and papers of the Semantic Web area, and apply Korona to uncover co-authorship networks. Results observed from our empirical evaluation suggest that exploiting semantics in scholarly knowledge graphs enables the identification of previously unknown relations between researchers. By extending the ontology, these observations can be generalized to other scholarly entities, e.g., articles or institutions, for the prediction of other scholarly patterns, e.g., co-citations or academic collaboration.
Approximation algorithms on $k-$ cycle covering and $k-$ clique covering
Venue: arXiv cs.DM 2018
Year: 2018

Given a weighted graph $G(V,E)$ with weight $\mathbf w: E\rightarrow Z^{|E|}_{+}$. A $k-$cycle covering is an edge subset $A$ of $E$ such that $G-A$ has no $k-$cycle. The minimum weight of $k-$cycle covering is the weighted covering number on $k-$cycle, denoted by $τ_{k}(G_{w})$. In this paper, we design a $k-1/2$ approximation algorithm for the weighted covering number on $k-$cycle when $k$ is odd. Given a weighted graph $G(V,E)$ with weight $\mathbf w: E\rightarrow Z^{|E|}_{+}$. A $k-$clique covering is an edge subset $A$ of $E$ such that $G-A$ has no $k-$clique. The minimum weight of $k-$clique covering is the weighted covering number on $k-$clique, denoted by $\widetilde{τ_{k}}(G_{w})$. In this paper, we design a $(k^{2}-k-1)/2$ approximation algorithm for the weighted covering number on $k-$clique. Last, we discuss the relationship between $k-$clique covering and $k-$clique packing in complete graph $K_{n}$.
Distinct patterns of syntactic agreement errors in recurrent networks and humans
Venue: arXiv cs.CL 2018
Year: 2018

Determining the correct form of a verb in context requires an understanding of the syntactic structure of the sentence. Recurrent neural networks have been shown to perform this task with an error rate comparable to humans, despite the fact that they are not designed with explicit syntactic representations. To examine the extent to which the syntactic representations of these networks are similar to those used by humans when processing sentences, we compare the detailed pattern of errors that RNNs and humans make on this task. Despite significant similarities (attraction errors, asymmetry between singular and plural subjects), the error patterns differed in important ways. In particular, in complex sentences with relative clauses error rates increased in RNNs but decreased in humans. Furthermore, RNNs showed a cumulative effect of attractors but humans did not. We conclude that at least in some respects the syntactic representations acquired by RNNs are fundamentally different from those used by humans.
Fake news as we feel it: perception and conceptualization of the term "fake news" in the media
Venue: arXiv cs.CL 2018
Year: 2018

In this article, we quantitatively analyze how the term "fake news" is being shaped in news media in recent years. We study the perception and the conceptualization of this term in the traditional media using eight years of data collected from news outlets based in 20 countries. Our results not only corroborate previous indications of a high increase in the usage of the expression "fake news", but also show contextual changes around this expression after the United States presidential election of 2016. Among other results, we found changes in the related vocabulary, in the mentioned entities, in the surrounding topics and in the contextual polarity around the term "fake news", suggesting that this expression underwent a change in perception and conceptualization after 2016. These outcomes expand the understandings on the usage of the term "fake news", helping to comprehend and more accurately characterize this relevant social phenomenon linked to misinformation and manipulation.
Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning
Venue: arXiv cs.SD 2018
Year: 2018

We propose a method to perform audio event detection under the common constraint that only limited training data are available. In training a deep learning system to perform audio event detection, two practical problems arise. Firstly, most datasets are "weakly labelled" having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose a data-efficient training of a stacked convolutional and recurrent neural network. This neural network is trained in a multi instance learning setting for which we introduce a new loss function that leads to improved training compared to the usual approaches for weakly supervised learning. We successfully test our approach on two low-resource datasets that lack temporal labels.
Improving Explainable Recommendations with Synthetic Reviews
Venue: arXiv cs.IR 2018
Year: 2018

An important task for a recommender system to provide interpretable explanations for the user. This is important for the credibility of the system. Current interpretable recommender systems tend to focus on certain features known to be important to the user and offer their explanations in a structured form. It is well known that user generated reviews and feedback from reviewers have strong leverage over the users' decisions. On the other hand, recent text generation works have been shown to generate text of similar quality to human written text, and we aim to show that generated text can be successfully used to explain recommendations. In this paper, we propose a framework consisting of popular review-oriented generation models aiming to create personalised explanations for recommendations. The interpretations are generated at both character and word levels. We build a dataset containing reviewers' feedback from the Amazon books review dataset. Our cross-domain experiments are designed to bridge from natural language processing to the recommender system domain. Besides language model evaluation methods, we employ DeepCoNN, a novel review-oriented recommender system using a deep neural network, to evaluate the recommendation performance of generated reviews by root mean square error (RMSE). We demonstrate that the synthetic personalised reviews have better recommendation performance than human written reviews. To our knowledge, this presents the first machine-generated natural language explanations for rating prediction.
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach
Venue: arXiv cs.AR 2018
Year: 2018

In spite of maturity to the modern electronic design automation (EDA) tools, optimized designs at architectural stage may become sub-optimal after going through physical design flow. Adder design has been such a long studied fundamental problem in VLSI industry yet designers cannot achieve optimal solutions by running EDA tools on the set of available prefix adder architectures. In this paper, we enhance a state-of-the-art prefix adder synthesis algorithm to obtain a much wider solution space in architectural domain. On top of that, a machine learning-based design space exploration methodology is applied to predict the Pareto frontier of the adders in physical domain, which is infeasible by exhaustively running EDA tools for innumerable architectural solutions. Considering the high cost of obtaining the true values for learning, an active learning algorithm is utilized to select the representative data during learning process, which uses less labeled data while achieving better quality of Pareto frontier. Experimental results demonstrate that our framework can achieve Pareto frontier of high quality over a wide design space, bridging the gap between architectural and physical designs.
Reconstructing Latent Orderings by Spectral Clustering
Venue: arXiv cs.DS 2018
Year: 2018

Spectral clustering uses a graph Laplacian spectral embedding to enhance the cluster structure of some data sets. When the embedding is one dimensional, it can be used to sort the items (spectral ordering). A number of empirical results also suggests that a multidimensional Laplacian embedding enhances the latent ordering of the data, if any. This also extends to circular orderings, a case where unidimensional embeddings fail. We tackle the task of retrieving linear and circular orderings in a unifying framework, and show how a latent ordering on the data translates into a filamentary structure on the Laplacian embedding. We propose a method to recover it, illustrated with numerical experiments on synthetic data and real DNA sequencing data. The code and experiments are available at https://github.com/antrec/mdso.
Asymptotically Optimal Estimation Algorithm for the Sparse Signal with Arbitrary Distributions
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose a sparse signal estimation algorithm that is suitable for many wireless communication systems, especially for the future millimeter wave and underwater communication systems. This algorithm is not only asymptotically optimal, but also robust to the distribution of non-zero entries of the sparse signal. Then, we derive its upper bound and lower bound, and show that the Mean Square Error (MSE) of the proposed algorithm can approach the Minimum Mean Square Error (MMSE) bound when the Signal Noise Ratio (SNR) goes to infinite or zero. Numerical simulations verify our theoretical analysis and also show that the proposed algorithm converges faster than existing algorithms, e.g., TSR-DFT, AMP, etc.
Revisiting Cross Modal Retrieval
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a cross-modal retrieval system that leverages on image and text encoding. Most multimodal architectures employ separate networks for each modality to capture the semantic relationship between them. However, in our work image-text encoding can achieve comparable results in terms of cross-modal retrieval without having to use a separate network for each modality. We show that text encodings can capture semantic relationships between multiple modalities. In our knowledge, this work is the first of its kind in terms of employing a single network and fused image-text embedding for cross-modal retrieval. We evaluate our approach on two famous multimodal datasets: MS-COCO and Flickr30K.
Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

The general ability to analyze and classify the 3D kinematics of the human form is an essential step in the development of socially adept humanoid robots. A variety of different types of signals can be used by machines to represent and characterize actions such as RGB videos, infrared maps, and optical flow. In particular, skeleton sequences provide a natural 3D kinematic description of human motions and can be acquired in real time using RGB+D cameras. Moreover, skeleton sequences are generalizable to characterize the motions of both humans and humanoid robots. The Globally Optimal Reparameterization Algorithm (GORA) is a novel, recently proposed algorithm for signal alignment in which signals are reparameterized to a globally optimal universal standard timescale (UST). Here, we introduce a variant of GORA for humanoid action recognition with skeleton sequences, which we call GORA-S. We briefly review the algorithm's mathematical foundations and contextualize them in the problem of action recognition with skeleton sequences. Subsequently, we introduce GORA-S and discuss parameters and numerical techniques for its effective implementation. We then compare its performance with that of the DTW and FastDTW algorithms, in terms of computational efficiency and accuracy in matching skeletons. Our results show that GORA-S attains a complexity that is significantly less than that of any tested DTW method. In addition, it displays a favorable balance between speed and accuracy that remains invariant under changes in skeleton sampling frequency, lending it a degree of versatility that could make it well-suited for a variety of action recognition tasks.
Using Deep Neural Networks to Translate Multi-lingual Threat Intelligence
Venue: arXiv cs.CL 2018
Year: 2018

The multilingual nature of the Internet increases complications in the cybersecurity community's ongoing efforts to strategically mine threat intelligence from OSINT data on the web. OSINT sources such as social media, blogs, and dark web vulnerability markets exist in diverse languages and hinder security analysts, who are unable to draw conclusions from intelligence in languages they don't understand. Although third party translation engines are growing stronger, they are unsuited for private security environments. First, sensitive intelligence is not a permitted input to third party engines due to privacy and confidentiality policies. In addition, third party engines produce generalized translations that tend to lack exclusive cybersecurity terminology. In this paper, we address these issues and describe our system that enables threat intelligence understanding across unfamiliar languages. We create a neural network based system that takes in cybersecurity data in a different language and outputs the respective English translation. The English translation can then be understood by an analyst, and can also serve as input to an AI based cyber-defense system that can take mitigative action. As a proof of concept, we have created a pipeline which takes Russian threats and generates its corresponding English, RDF, and vectorized representations. Our network optimizes translations on specifically, cybersecurity data.
Bilgisayar Muhendisligi Egitiminde Teknoloji Egilimlerinin Takip Edilmesi
Venue: arXiv cs.CY 2018
Year: 2018

Today computers have become an integral part of life. However, most people's interaction with computers in on end-user-level. Computer engineers are needed while designing and developing a structure of computer systems, software and hardware systems and also they need when implementing and solving problems while using these systems. Training of qualified computer engineers is vital to have a say in future technology. Recently, big data analysis, cloud technologies, wearable technologies, mobile and online services become popular. For that reason, computer engineering education should update itself regularly and keep up with the latest improvements. In this study, it is touched on some topics which are suggested to extend computer engineering curricula such as big data analyses, wearable technologies internet of things, cloud technologies, identity management and cyber security which are expected to widening in the area and also demanded that computer engineering student should be qualified on. Related topics will be described and usage areas will be explained, developments and future roles will be mentioned and also expected achievements will be described. These achievement's relevance with learning outcomes of departments which are accredited by MUDEK will be defined.
Doubly Stochastic Adversarial Autoencoder
Venue: arXiv cs.LG 2018
Year: 2018

Any autoencoder network can be turned into a generative model by imposing an arbitrary prior distribution on its hidden code vector. Variational Autoencoder (VAE) [2] uses a KL divergence penalty to impose the prior, whereas Adversarial Autoencoder (AAE) [1] uses {\it generative adversarial networks} GAN [3]. GAN trades the complexities of {\it sampling} algorithms with the complexities of {\it searching} Nash equilibrium in minimax games. Such minimax architectures get trained with the help of data examples and gradients flowing through a generator and an adversary. A straightforward modification of AAE is to replace the adversary with the maximum mean discrepancy (MMD) test [4-5]. This replacement leads to a new type of probabilistic autoencoder, which is also discussed in our paper. We propose a novel probabilistic autoencoder in which the adversary of AAE is replaced with a space of {\it stochastic} functions. This replacement introduces a new source of randomness, which can be considered as a continuous control for encouraging {\it explorations}. This prevents the adversary from fitting too closely to the generator and therefore leads to a more diverse set of generated samples.
Socio-Material Network Analysis: A Mixed Method Study of Five European Artistic Collectives
Venue: arXiv cs.CY 2018
Year: 2018

In this paper, I argue that we can better understand the relationship between social structure and materiality by combining qualitative analysis of practices in shared physical space with statistical analysis. Drawing on the two-mode approach, I treat social and material structures together with the relationship between them as a two-level socio-material network. In a mixed method study, formalized ethnographic data on such networks in five European artistic collectives are subjected to multilevel exponential random graph modelling. It sheds light on how different types of interpersonal ties condition the engagement of individuals with similar materiality over time.
CRAM: Efficient Hardware-Based Memory Compression for Bandwidth Enhancement
Venue: arXiv cs.AR 2018
Year: 2018

This paper investigates hardware-based memory compression designs to increase the memory bandwidth. When lines are compressible, the hardware can store multiple lines in a single memory location, and retrieve all these lines in a single access, thereby increasing the effective memory bandwidth. However, relocating and packing multiple lines together depending on the compressibility causes a line to have multiple possible locations. Therefore, memory compression designs typically require metadata to specify the compressibility of the line. Unfortunately, even in the presence of dedicated metadata caches, maintaining and accessing this metadata incurs significant bandwidth overheads and can degrade performance by as much as 40%. Ideally, we want to implement memory compression while eliminating the bandwidth overheads of metadata accesses. This paper proposes CRAM, a bandwidth-efficient design for memory compression that is entirely hardware based and does not require any OS support or changes to the memory modules or interfaces. CRAM uses a novel implicit-metadata mechanism, whereby the compressibility of the line can be determined by scanning the line for a special marker word, eliminating the overheads of metadata access. CRAM is equipped with a low-cost Line Location Predictor (LLP) that can determine the location of the line with 98% accuracy. Furthermore, we also develop a scheme that can dynamically enable or disable compression based on the bandwidth cost of storing compressed lines and the bandwidth benefits of obtaining compressed lines, ensuring no degradation for workloads that do not benefit from compression. Our evaluations, over a diverse set of 27 workloads, show that CRAM provides a speedup of up to 73% (average 6%) without causing slowdown for any of the workloads, and consuming a storage overhead of less than 300 bytes at the memory controller.
Multivariate Public Key Cryptography and Digital Signature
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, algorithms for multivariate public key cryptography and digital signature are described. Plain messages and encrypted messages are arrays, consisting of elements from a fixed finite ring or field. The encryption and decryption algorithms are based on multivariate mappings. The security of the private key depends on the difficulty of solving a system of parametric simultaneous multivariate equations involving polynomial or exponential mappings. The method is a general purpose utility for most data encryption, digital certificate or digital signature applications. For security protocols of the application layer level in the OSI model, the methods described in this paper are useful.