OpenRaiser/Intern-Atlas huggingface dataset:
Stream Reasoning-Based Control of Caching Strategies in CCN Routers
Venue: arXiv cs.AI 2016
Year: 2016

Content-Centric Networking (CCN) research addresses the mismatch between the modern usage of the Internet and its outdated architecture. Importantly, CCN routers may locally cache frequently requested content in order to speed up delivery to end users. Thus, the issue of caching strategies arises, i.e., which content shall be stored and when it should be replaced. In this work, we employ novel techniques towards intelligent administration of CCN routers that autonomously switch between existing strategies in response to changing content request patterns. In particular, we present a router architecture for CCN networks that is controlled by rule-based stream reasoning, following the recent formal framework LARS which extends Answer Set Programming for streams. The obtained possibility for flexible router configuration at runtime allows for faster experimentation and may thus help to advance the further development of CCN. Moreover, the empirical evaluation of our feasibility study shows that the resulting caching agent may give significant performance gains.
Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional features for Online Handwritten Chinese Character Recognition (OLHCCR). On one hand, SSDCNN can learn the representation of Online Handwritten Chinese Character (OLHCC) by incorporating the natural sequence information of the strokes. On the other hand, SSDCNN can incorporate eight-directional features in a natural way. In order to train SSDCNN, we divide the process of training into two stages: 1) The training data is used to pre-train the whole architecture until the performance tends to converge. 2) Fully-connected neural network which is used to combine the stroke sequence-dependent representation with eight-directional features and softmax layer are further trained. Experiments were conducted on the OLHCCR competition tasks of ICDAR 2013. Results show that, SSDCNN can reduce the recognition error by 50\% (5.13\% vs 2.56\%) compared to the model which only use eight-directional features. The proposed SSDCNN achieves 97.44\% accuracy which reduces the recognition error by about 1.9\% compared with the best submitted system on ICDAR2013 competition. These results indicate that SSDCNN can exploit the stroke sequence information to learn high-quality representation of OLHCC. It also shows that the learnt representation and the classical eight-directional features complement each other within the SSDCNN architecture.
Improved Knowledge Base Completion by Path-Augmented TransR Model
Venue: arXiv cs.AI 2016
Year: 2016

Knowledge base completion aims to infer new relations from existing information. In this paper, we propose path-augmented TransR (PTransR) model to improve the accuracy of link prediction. In our approach, we base PTransR model on TransR, which is the best one-hop model at present. Then we regularize TransR with information of relation paths. In our experiment, we evaluate PTransR on the task of entity prediction. Experimental results show that PTransR outperforms previous models.
A Fault-Tolerant Distributed Detection of Two Simultaneous Events in Wireless Sensor Networks
Venue: arXiv cs.SY 2016
Year: 2016

Wireless Sensor Networks (WSNs) consist of many low cost and light sensors dispersed in an area to monitor the physical environment. Event detection in WSN area, especially detection of multi-events at the same time, is an important problem. This article is a new attempt for detection of two simultaneous events based on distributed data processing structure and Bayesian criteria. For accurate detection of two simultaneous events, we proposed new decision rules based on likelihood ratio test and also derived probability of detection error based on Bayesian criteria. In addition to multi-event detection, the proposed method is expanded to a fault-tolerant procedure if there are faults in decision making of sensors. Performance of the proposed approach is demonstrated for detection of events in different circumstances. Results show the effectiveness of the algorithm for fault-tolerant multi-event detection.
Towards end-to-end optimisation of functional image analysis pipelines
Venue: arXiv cs.CV 2016
Year: 2016

The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pipeline of data processing modules, each involving a variety of parameters. These parameters are frequently set according to the local goal of each specific module, not accounting for the rest of the pipeline. Given recent success of neural network research in many different domains, we propose to convert the whole data pipeline into a deep neural network, where the parameters involved are jointly optimised by the network to best serve a common global goal. As a proof of concept, we develop a module able to adaptively apply the most suitable spatial smoothing to every brain volume for each specific neuroimaging task, and we validate its results in a standard brain decoding experiment.
Cost minimization of network services with buffer and end-to-end deadline constraints
Venue: arXiv cs.NI 2016
Year: 2016

Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such is virtual networking services, so called services chains, which consist of several interconnected virtual network functions. This allows for the capacity to be scaled up and down by adding or removing virtual resources. In this work, we develop a model of a service chain and pose the dynamic allocation of resources as an optimization problem. We design and present a set of strategies to allot virtual network nodes in an optimal fashion subject to latency and buffer constraints.
Improved phase-unwrapping method using geometric constraints
Venue: arXiv cs.CV 2016
Year: 2016

Conventional dual-frequency fringe projection algorithm often suffers from phase unwrapping failure when the frequency ratio between the high frequency and the low one is too large. Zhang et.al. proposed an enhanced two-frequency phase-shifting method to use geometric constraints of digital fringe projection(DFP) to reduce the noise impact due to the large frequency ratio. However, this method needs to calibrate the DFP system and calculate the minimum phase map at the nearest position from the camera perspective, these procedures are are relatively complex and more time-cosuming. In this paper, we proposed an improved method, which eliminates the system calibration and determination in Zhang's method,meanwhile does not need to use the low frequency fringe pattern. In the proposed method,we only need a set of high frequency fringe patterns to measure the object after the high frequency is directly estimated by the experiment. Thus the proposed method can simplify the procedure and improve the speed. Finally, the experimental evaluation is conducted to prove the validity of the proposed method.The results demonstrate that the proposed method can overcome the main disadvantages encountered by Zhang's method.
Learning and Fusing Multimodal Features from and for Multi-task Facial Computing
Venue: arXiv cs.CV 2016
Year: 2016

We propose a deep learning-based feature fusion approach for facial computing including face recognition as well as gender, race and age detection. Instead of training a single classifier on face images to classify them based on the features of the person whose face appears in the image, we first train four different classifiers for classifying face images based on race, age, gender and identification (ID). Multi-task features are then extracted from the trained models and cross-task-feature training is conducted which shows the value of fusing multimodal features extracted from multi-tasks. We have found that features trained for one task can be used for other related tasks. More interestingly, the features trained for a task with more classes (e.g. ID) and then used in another task with fewer classes (e.g. race) outperforms the features trained for the other task itself. The final feature fusion is performed by combining the four types of features extracted from the images by the four classifiers. The feature fusion approach improves the classifications accuracy by a 7.2%, 20.1%, 22.2%, 21.8% margin, respectively, for ID, age, race and gender recognition, over the results of single classifiers trained only on their individual features. The proposed method can be applied to applications in which different types of data or features can be extracted.
On Two Conjectures about Permutation Trinomials over $\mathbb{F}_{3^{2k}}$
Venue: arXiv cs.IT 2016
Year: 2016

Permutation polynomials with few terms attracts researchers' interest in recent years due to their simple algebraic form and some additional extraordinary properties. In this paper, by analyzing the quadratic factors of a fifth-degree polynomial and a seventh-degree polynomial over the finite field $\mathbb{F}_{3^{2k}}$, two conjectures on permutation trinomials over $\mathbb{F}_{3^{2k}}$ proposed recently by Li, Qu, Li and Fu are settled, where $k$ is a positive integer.
Performance Improvement of an AS-friendly Peer Selection Algorithm for P2P Live Streaming
Venue: arXiv cs.NI 2016
Year: 2016

Minimum Physical Hop (MPH) has been proposed as a peer selection algorithm for decreasing inter-AS (Autonomous System) traffic volume in P2P live streaming. In MPH, a newly joining peer selects a peer whose physical hop count (i.e., the number of ASes traversed on the content delivery path) from it is the minimum as its providing peer. However, MPH shows high inter-AS traffic volume when the number of joining peers is large. In this paper, we propose IMPH that tries to further decrease the inter-AS traffic volume by distributing peers with one logical hop count (i.e., the number of peers or origin streaming servers (OSSes) traversed on the content delivery path from an OSS to the peer) to many ASes and encouraging the following peers to find their providing peers within the same AS. Numerical examples show that IMPH achieves at the maximum of 64% lower inter-AS traffic volume than MPH.
Localization for Wireless Sensor Networks: A Neural Network Approach
Venue: arXiv cs.NI 2016
Year: 2016

As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images
Venue: arXiv cs.CV 2016
Year: 2016

Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state-of-the-art (36.8% specificity compared to 12.5%). Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).
Analysis and Modeling of Social Influence in High Performance Computing Workloads
Venue: arXiv cs.DC 2016
Year: 2016

Social influence among users (e.g., collaboration on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and cluster workload logs, this paper identifies, analyzes, and quantifies the level of social influence across HPC users. We show the existence of a social graph that is characterized by a pattern of dominant users and followers. This pattern also follows a power-law distribution, which is consistent with those observed in mainstream social networks. Given its potential impact on HPC workloads prediction and scheduling, we propose a fast-converging, computationally-efficient online learning algorithm for identifying social groups. Extensive evaluation shows that our online algorithm can (1) quickly identify the social relationships by using a small portion of incoming jobs and (2) can efficiently track group evolution over time.
Generalization of metric classification algorithms for sequences classification and labelling
Venue: arXiv cs.LG 2016
Year: 2016

The article deals with the issue of modification of metric classification algorithms. In particular, it studies the algorithm k-Nearest Neighbours for its application to sequential data. A method of generalization of metric classification algorithms is proposed. As a part of it, there has been developed an algorithm for solving the problem of classification and labelling of sequential data. The advantages of the developed algorithm of classification in comparison with the existing one are also discussed in the article. There is a comparison of the effectiveness of the proposed algorithm with the algorithm of CRF in the task of chunking in the open data set CoNLL2000.
Stakeholder Involvement: A Success Factor for Achieving Better UX Integration
Venue: arXiv cs.SE 2016
Year: 2016

Stakeholder involvement is one of the major success factors in integrating user experience (UX) practices into software development processes and organizations. It is also a necessity for agile software development. However, practitioners still have limited access to guidelines on successful involvement of UX stakeholders in agile settings. Moreover, agile UX literature does not well address the specific characteristics of UX and it does not clearly differentiate between UX and usability work. This paper presents two guidelines for supporting stakeholder involvement in both UX integration and the daily UX work. In particular, we focus on the special characteristics of UX: being dynamic, subjective, holistic, and context-dependent. The guidelines clarify practical implications of these characteristics for practitioners. In addition, they can help researchers in addressing these characteristics better in agile UX research.
Term-Class-Max-Support (TCMS): A Simple Text Document Categorization Approach Using Term-Class Relevance Measure
Venue: arXiv cs.IR 2016
Year: 2016

In this paper, a simple text categorization method using term-class relevance measures is proposed. Initially, text documents are processed to extract significant terms present in them. For every term extracted from a document, we compute its importance in preserving the content of a class through a novel term-weighting scheme known as Term_Class Relevance (TCR) measure proposed by Guru and Suhil (2015) [1]. In this way, for every term, its relevance for all the classes present in the corpus is computed and stored in the knowledgebase. During testing, the terms present in the test document are extracted and the term-class relevance of each term is obtained from the stored knowledgebase. To achieve quick search of term weights, Btree indexing data structure has been adapted. Finally, the class which receives maximum support in terms of term-class relevance is decided to be the class of the given test document. The proposed method works in logarithmic complexity in testing time and simple to implement when compared to any other text categorization techniques available in literature. The experiments conducted on various benchmarking datasets have revealed that the performance of the proposed method is satisfactory and encouraging.
Convergence rate of stochastic k-means
Venue: arXiv cs.LG 2016
Year: 2016

We analyze online and mini-batch k-means variants. Both scale up the widely used Lloyd 's algorithm via stochastic approximation, and have become popular for large-scale clustering and unsupervised feature learning. We show, for the first time, that they have global convergence towards local optima at $O(\frac{1}{t})$ rate under general conditions. In addition, we show if the dataset is clusterable, with suitable initialization, mini-batch k-means converges to an optimal k-means solution with $O(\frac{1}{t})$ convergence rate with high probability. The k-means objective is non-convex and non-differentiable: we exploit ideas from non-convex gradient-based optimization by providing a novel characterization of the trajectory of k-means algorithm on its solution space, and circumvent its non-differentiability via geometric insights about k-means update.
Approximations of the Densest k-Subhypergraph and Set Union Knapsack problems
Venue: arXiv cs.DS 2016
Year: 2016

For any given $ε>0$ we provide an algorithm for the Densest $k$-Subhypergraph Problem with an approximation ratio of at most $O(n^{θ_m+2ε})$ for $θ_m=\frac{1}{2}m-\frac{1}{2}-\frac{1}{2m}$ and run time at most $O(n^{m-2+1/ε})$, where the hyperedges have at most $m$ vertices. We use this result to give an algorithm for the Set Union Knapsack Problem with an approximation ratio of at most $O(n^{α_m+ε})$ for $α_m=\frac{2}{3}[m-1-\frac{2m-2}{m^2+m-1}]$ and run time at most $O(n^{5(m-2)+9/ε})$, where the subsets have at most $m$ elements. The author is not aware of any previous results on the approximation of either of these two problems.
Joint Relay-User Beamforming Design in Full-Duplex Two-Way Relay Channel
Venue: arXiv cs.IT 2016
Year: 2016

A full-duplex two-way relay channel with multiple antennas is considered. For this three-node network, the beamforming design needs to suppress self-interference. While a traditional way is to apply zero-forcing for self-interference mitigation, it may harm the desired signals. In this paper, a design which reserves a fraction of self-interference is proposed by solving a quality-of-service constrained beamforming design problem. Since the problem is challenging due to the loop self-interference, a convergence-guaranteed alternating optimization algorithm is proposed to jointly design the relay-user beamformers. Numerical results show that the proposed scheme outperforms zero-forcing method, and achieves a transmit power close to the ideal case.
Spatio-Temporal Attention Models for Grounded Video Captioning
Venue: arXiv cs.CV 2016
Year: 2016

Automatic video captioning is challenging due to the complex interactions in dynamic real scenes. A comprehensive system would ultimately localize and track the objects, actions and interactions present in a video and generate a description that relies on temporal localization in order to ground the visual concepts. However, most existing automatic video captioning systems map from raw video data to high level textual description, bypassing localization and recognition, thus discarding potentially valuable information for content localization and generalization. In this work we present an automatic video captioning model that combines spatio-temporal attention and image classification by means of deep neural network structures based on long short-term memory. The resulting system is demonstrated to produce state-of-the-art results in the standard YouTube captioning benchmark while also offering the advantage of localizing the visual concepts (subjects, verbs, objects), with no grounding supervision, over space and time.
Polylogarithmic Bounds on the Competitiveness of Min-cost (Bipartite) Perfect Matching with Delays
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of online Min-cost Perfect Matching with Delays (MPMD) recently introduced by Emek et al, (STOC 2016). This problem is defined on an underlying $n$-point metric space. An adversary presents real-time requests online at points of the metric space, and the algorithm is required to match them, possibly after keeping them waiting for some time. The cost incurred is the sum of the distances between matched pairs of points (the connection cost), and the sum of the waiting times of the requests (the delay cost). We present an algorithm with a competitive ratio of $O(\log n)$, which improves the upper bound of $O(\log^2n+\logΔ)$ of Emek et al, by removing the dependence on $Δ$, the aspect ratio of the metric space (which can be unbounded as a function of $n$). The core of our algorithm is a deterministic algorithm for MPMD on metrics induced by edge-weighted trees of height $h$, whose cost is guaranteed to be at most $O(1)$ times the connection cost plus $O(h)$ times the delay cost of every feasible solution. The reduction from MPMD on arbitrary metrics to MPMD on trees is achieved using the result on embedding $n$-point metric spaces into distributions over weighted hierarchically separated trees of height $O(\log n)$, with distortion $O(\log n)$. We also prove a lower bound of $Ω(\sqrt{\log n})$ on the competitive ratio of any randomized algorithm. This is the first lower bound which increases with $n$, and is attained on the metric of $n$ equally spaced points on a line. The problem of Min-cost Bipartite Perfect Matching with Delays (MBPMD) is the same as MPMD except that every request is either positive or negative, and requests can be matched only if they have opposite polarity. We prove an upper bound of $O(\log n)$ and a lower bound of $Ω(\log^{1/3}n)$ on the competitive ratio of MBPMD with a more involved analysis.