OpenRaiser/Intern-Atlas huggingface dataset:
Cloud Gaming With Foveated Graphics
Venue: arXiv cs.MM 2018
Year: 2018

Cloud gaming enables playing high end games, originally designed for PC or game console setups, on low end devices, such as net-books and smartphones, by offloading graphics rendering to GPU powered cloud servers. However, transmitting the high end graphics requires a large amount of available network bandwidth, even though it is a compressed video stream. Foveated video encoding (FVE) reduces the bandwidth requirement by taking advantage of the non-uniform acuity of human visual system and by knowing where the user is looking. We have designed and implemented a system for cloud gaming with foveated graphics using a consumer grade real-time eye tracker and an open source cloud gaming platform. In this article, we describe the system and its evaluation through measurements with representative games from different genres to understand the effect of parameterization of the FVE scheme on bandwidth requirements and to understand its feasibility from the latency perspective. We also present results from a user study. The results suggest that it is possible to find a "sweet spot" for the encoding parameters so that the users hardly notice the presence of foveated encoding but at the same time the scheme yields most of the bandwidth savings achievable.
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Automating classification and segmentation process of abnormal regions in different body organs has a crucial role in most of medical imaging applications such as funduscopy, endoscopy, and dermoscopy. Detecting multiple abnormalities in each type of images is necessary for better and more accurate diagnosis procedure and medical decisions. In recent years portable medical imaging devices such as capsule endoscopy and digital dermatoscope have been introduced and made the diagnosis procedure easier and more efficient. However, these portable devices have constrained power resources and limited computational capability. To address this problem, we propose a bifurcated structure for convolutional neural networks performing both classification and segmentation of multiple abnormalities simultaneously. The proposed network is first trained by each abnormality separately. Then the network is trained using all abnormalities. In order to reduce the computational complexity, the network is redesigned to share some features which are common among all abnormalities. Later, these shared features are used in different settings (directions) to segment and classify the abnormal region of the image. Finally, results of the classification and segmentation directions are fused to obtain the classified segmentation map. Proposed framework is simulated using four frequent gastrointestinal abnormalities as well as three dermoscopic lesions and for evaluation of the proposed framework the results are compared with the corresponding ground truth map. Properties of the bifurcated network like low complexity and resource sharing make it suitable to be implemented as a part of portable medical imaging devices.
A Generic Multi-modal Dynamic Gesture Recognition System using Machine Learning
Venue: arXiv cs.HC 2018
Year: 2018

Human computer interaction facilitates intelligent communication between humans and computers, in which gesture recognition plays a prominent role. This paper proposes a machine learning system to identify dynamic gestures using tri-axial acceleration data acquired from two public datasets. These datasets, uWave and Sony, were acquired using accelerometers embedded in Wii remotes and smartwatches, respectively. A dynamic gesture signed by the user is characterized by a generic set of features extracted across time and frequency domains. The system was analyzed from an end-user perspective and was modelled to operate in three modes. The modes of operation determine the subsets of data to be used for training and testing the system. From an initial set of seven classifiers, three were chosen to evaluate each dataset across all modes rendering the system towards mode-neutrality and dataset-independence. The proposed system is able to classify gestures performed at varying speeds with minimum preprocessing, making it computationally efficient. Moreover, this system was found to run on a low-cost embedded platform - Raspberry Pi Zero (USD 5), making it economically viable.
Cocktails, but no party: multipath-enabled private audio
Venue: arXiv cs.SD 2018
Year: 2018

We describe a private audio messaging system that uses echoes to unscramble messages at a few predetermined locations in a room. The system works by splitting the audio into short chunks and emitting them from different loudspeakers. The chunks are filtered so that as they echo around the room, they sum to noise everywhere except at a few chosen focusing spots where they exactly reproduce the intended messages. Unlike in the case of standard personal audio zones, the proposed method renders sound outside the focusing spots unintelligible. Our method essentially depends on echoes: the room acts as a mixing system such that at given points we get the desired output. Finally, we only require a modest number of loudspeakers and only a few impulse response measurements at points where the messages should be delivered. We demonstrate the effectiveness of the proposed method via objective quantitative metrics as well as informal listening experiments in a real room.
An investigation of a deep learning based malware detection system
Venue: arXiv cs.CR 2018
Year: 2018

We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and a False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.
Semantic Interoperability Middleware Architecture for Heterogeneous Environmental Data Sources
Venue: arXiv cs.AI 2018
Year: 2018

Data heterogeneity hampers the effort to integrate and infer knowledge from vast heterogeneous data sources. An application case study is described, in which the objective was to semantically represent and integrate structured data from sensor devices with unstructured data in the form of local indigenous knowledge. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not well supported yet. To combat the incompatibility issues, a dedicated semantic middleware solution is required. In this paper, we describe and evaluate a cross-domain middleware architecture that semantically integrates and generate inference from heterogeneous data sources. These use of semantic technology for predicting and forecasting complex environmental phenomenon will increase the degree of accuracy of environmental monitoring systems.
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations
Venue: arXiv cs.SI 2018
Year: 2018

In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction method named Resource Allocation Index (RA) is adopted for privacy attacks. Several defense methods are proposed, including heuristic and evolutionary approaches, to protect targeted links from RA attacks via evolutionary perturbations. This is the first time to study privacy protection on targeted links against link-prediction-based attacks. Some links are randomly selected from the network as targeted links for experimentation. The simulation results on six real-world networks demonstrate the superiority of the evolutionary perturbation approach for target defense against RA attacks. Moreover, transferring experiments show that, although the evolutionary perturbation approach is designed to against RA attacks, it is also effective against other link-prediction-based attacks.
A #SAT Algorithm for Small Constant-Depth Circuits with PTF gates
Venue: arXiv cs.CC 2018
Year: 2018

We show that there is a randomized algorithm that, when given a small constant-depth Boolean circuit $C$ made up of gates that compute constant-degree Polynomial Threshold functions or PTFs (i.e., Boolean functions that compute signs of constant-degree polynomials), counts the number of satisfying assignments to $C$ in significantly better than brute-force time. Formally, for any constants $d,k$, there is an $ε> 0$ such that the algorithm counts the number of satisfying assignments to a given depth-$d$ circuit $C$ made up of $k$-PTF gates such that $C$ has size at most $n^{1+ε}$. The algorithm runs in time $2^{n-n^{Ω(ε)}}$. Before our result, no algorithm for beating brute-force search was known even for a single degree-$2$ PTF (which is a depth-$1$ circuit of linear size). The main new tool is the use of a learning algorithm for learning degree-$1$ PTFs (or Linear Threshold Functions) using comparison queries due to Kane, Lovett, Moran and Zhang (FOCS 2017). We show that their ideas fit nicely into a memoization approach that yields the #SAT algorithms.
A Distributed Learning Architecture for Scientific Imaging Problems
Venue: arXiv cs.DC 2018
Year: 2018

Current trends in scientific imaging are challenged by the emerging need of integrating sophisticated machine learning with Big Data analytics platforms. This work proposes an in-memory distributed learning architecture for enabling sophisticated learning and optimization techniques on scientific imaging problems, which are characterized by the combination of variant information from different origins. We apply the resulting, Spark-compliant, architecture on two emerging use cases from the scientific imaging domain, namely: (a) the space variant deconvolution of galaxy imaging surveys (astrophysics), (b) the super-resolution based on coupled dictionary training (remote sensing). We conduct evaluation studies considering relevant datasets, and the results report at least 60\% improvement in time response against the conventional computing solutions. Ultimately, the offered discussion provides useful practical insights on the impact of key Spark tuning parameters on the speedup achieved, and the memory/disk footprint.
Latent Space Optimal Transport for Generative Models
Venue: arXiv cs.LG 2018
Year: 2018

Variational Auto-Encoders enforce their learned intermediate latent-space data distribution to be a simple distribution, such as an isotropic Gaussian. However, this causes the posterior collapse problem and loses manifold structure which can be important for datasets such as facial images. A GAN can transform a simple distribution to a latent-space data distribution and thus preserve the manifold structure, but optimizing a GAN involves solving a Min-Max optimization problem, which is difficult and not well understood so far. Therefore, we propose a GAN-like method to transform a simple distribution to a data distribution in the latent space by solving only a minimization problem. This minimization problem comes from training a discriminator between a simple distribution and a latent-space data distribution. Then, we can explicitly formulate an Optimal Transport (OT) problem that computes the desired mapping between the two distributions. This means that we can transform a distribution without solving the difficult Min-Max optimization problem. Experimental results on an eight-Gaussian dataset show that the proposed OT can handle multi-cluster distributions. Results on the MNIST and the CelebA datasets validate the effectiveness of the proposed method.
Equivalence between pathbreadth and strong pathbreadth
Venue: arXiv cs.CC 2018
Year: 2018

We say that a given graph $G = (V, E)$ has \emph{pathbreadth} at most $ρ$, denoted $\pb(G) \leq ρ$, if there exists a Roberston and Seymour's path decomposition where every bag is contained in the $ρ$-neighbourhood of some vertex. Similarly, we say that $G$ has \emph{strong pathbreadth} at most $ρ$, denoted $\spb(G) \leq ρ$, if there exists a Roberston and Seymour's path decomposition where every bag is the complete $ρ$-neighbourhood of some vertex. It is straightforward that $\pb(G) \leq \spb(G)$ for any graph $G$. Inspired from a close conjecture in [Leitert and Dragan, COCOA'16], we prove in this note that $\spb(G) \leq 4 \cdot \pb(G)$.
Hamiltonicity in Convex Bipartite Graphs
Venue: arXiv cs.DM 2018
Year: 2018

For a connected graph, the Hamiltonian cycle (path) is a simple cycle (path) that spans all the vertices in the graph. It is known from \cite{muller,garey} that HAMILTONIAN CYCLE (PATH) are NP-complete in general graphs and chordal bipartite graphs. A convex bipartite graph $G$ with bipartition $(X,Y)$ and an ordering $X=(x_1,\ldots,x_n)$, is a bipartite graph such that for each $y \in Y$, the neighborhood of $y$ in $X$ appears consecutively. $G$ is said to have convexity with respect to $X$. Further, convex bipartite graphs are a subclass of chordal bipartite graphs. In this paper, we present a necessary and sufficient condition for the existence of a Hamiltonian cycle in convex bipartite graphs and further we obtain a linear-time algorithm for this graph class. We also show that Chvatal's necessary condition is sufficient for convex bipartite graphs. The closely related problem is HAMILTONIAN PATH whose complexity is open in convex bipartite graphs. We classify the class of convex bipartite graphs as {\em monotone} and {\em non-monotone} graphs. For monotone convex bipartite graphs, we present a linear-time algorithm to output a Hamiltonian path. We believe that these results can be used to obtain algorithms for Hamiltonian path problem in non-monotone convex bipartite graphs. It is important to highlight (a) in \cite{keil,esha}, it is incorrectly claimed that Hamiltonian path problem in convex bipartite graphs is polynomial-time solvable by referring to \cite{muller} which actually discusses Hamiltonian cycle (b) the algorithm appeared in \cite{esha} for the longest path problem (Hamiltonian path problem) in biconvex and convex bipartite graphs have an error and it does not compute an optimum solution always. We present an infinite set of counterexamples in support of our claim.
Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository
Venue: arXiv cs.LG 2018
Year: 2018

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Nowadays, deep learning can be employed to a wide ranges of fields including medicine, engineering, etc. In deep learning, Convolutional Neural Network (CNN) is extensively used in the pattern and sequence recognition, video analysis, natural language processing, spam detection, topic categorization, regression analysis, speech recognition, image classification, object detection, segmentation, face recognition, robotics, and control. The benefits associated with its near human level accuracies in large applications lead to the growing acceptance of CNN in recent years. The primary contribution of this paper is to analyze the impact of the pattern of the hidden layers of a CNN over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the Modified National Institute of Standards and Technology (MNIST) dataset. Also, is to observe the variations of accuracies of the network for various numbers of hidden layers and epochs and to make comparison and contrast among them. The system is trained utilizing stochastic gradient and backpropagation algorithm and tested with feedforward algorithm.
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.
A Network Topology Approach to Bot Classification
Venue: arXiv cs.SI 2018
Year: 2018

Automated social agents, or bots, are increasingly becoming a problem on social media platforms. There is a growing body of literature and multiple tools to aid in the detection of such agents on online social networking platforms. We propose that the social network topology of a user would be sufficient to determine whether the user is a automated agent or a human. To test this, we use a publicly available dataset containing users on Twitter labelled as either automated social agent or human. Using an unsupervised machine learning approach, we obtain a detection accuracy rate of 70%.
Algorithmic Obfuscation over GF($2^m$)
Venue: arXiv cs.CR 2018
Year: 2018

Galois Field arithmetic blocks are the key components in many security applications, such as Elliptic Curve Cryptography (ECC) and the S-Boxes of the Advanced Encryption Standard (AES) cipher. This paper introduces a novel hardware intellectual property (IP) protection technique by obfuscating arithmetic functions over Galois Field (GF), specifically, focusing on obfuscation of GF multiplication that underpins complex GF arithmetic and elliptic curve point arithmetic functions. Obfuscating GF multiplication circuits is important because the choice of irreducible polynomials in GF multiplication has the great impact on the performance of the hardware designs, and because the significant effort is spent on finding an optimum irreducible polynomial for a given field, which can provide one company a competitive advantage over another.
Context-Dependent Diffusion Network for Visual Relationship Detection
Venue: arXiv cs.CV 2018
Year: 2018

Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images. Different from pure object recognition tasks, the relation triplets of subject-predicate-object lie on an extreme diversity space, such as \textit{person-behind-person} and \textit{car-behind-building}, while suffering from the problem of combinatorial explosion. In this paper, we propose a context-dependent diffusion network (CDDN) framework to deal with visual relationship detection. To capture the interactions of different object instances, two types of graphs, word semantic graph and visual scene graph, are constructed to encode global context interdependency. The semantic graph is built through language priors to model semantic correlations across objects, whilst the visual scene graph defines the connections of scene objects so as to utilize the surrounding scene information. For the graph-structured data, we design a diffusion network to adaptively aggregate information from contexts, which can effectively learn latent representations of visual relationships and well cater to visual relationship detection in view of its isomorphic invariance to graphs. Experiments on two widely-used datasets demonstrate that our proposed method is more effective and achieves the state-of-the-art performance.
Unsupervised Sense-Aware Hypernymy Extraction
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.