OpenRaiser/Intern-Atlas huggingface dataset:
Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark
Venue: arXiv cs.CV 2016
Year: 2016

Recent deep learning based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition (HCCR) by learning discriminative representations directly from raw data. Nevertheless, we believe that the long-and-well investigated domain-specific knowledge should still help to boost the performance of HCCR. By integrating the traditional normalization-cooperated direction-decomposed feature map (directMap) with the deep convolutional neural network (convNet), we are able to obtain new highest accuracies for both online and offline HCCR on the ICDAR-2013 competition database. With this new framework, we can eliminate the needs for data augmentation and model ensemble, which are widely used in other systems to achieve their best results. This makes our framework to be efficient and effective for both training and testing. Furthermore, although directMap+convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective. A new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer. The adaptation process can be efficiently and effectively implemented in an unsupervised manner. By adding the adaptation layer into the pre-trained convNet, it can adapt to the new handwriting styles of particular writers, and the recognition accuracy can be further improved consistently and significantly. This paper gives an overview and comparison of recent deep learning based approaches for HCCR, and also sets new benchmarks for both online and offline HCCR.
Automatic 3D Reconstruction for Symmetric Shapes
Venue: arXiv cs.CV 2016
Year: 2016

Generic 3D reconstruction from a single image is a difficult problem. A lot of data loss occurs in the projection. A domain based approach to reconstruction where we solve a smaller set of problems for a particular use case lead to greater returns. The project provides a way to automatically generate full 3-D renditions of actual symmetric images that have some prior information provided in the pipeline by a recognition algorithm. We provide a critical analysis on how this can be enhanced and improved to provide a general reconstruction framework for automatic reconstruction for any symmetric shape.
Tightening McCormick Relaxations for Nonlinear Programs via Dynamic Multivariate Partitioning
Venue: arXiv cs.SY 2016
Year: 2016

In this work, we propose a two-stage approach to strengthen piecewise McCormick relaxations for mixed-integer nonlinear programs (MINLP) with multi-linear terms. In the first stage, we exploit Constraint Programing (CP) techniques to contract the variable bounds. In the second stage we partition the variables domains using a dynamic multivariate partitioning scheme. Instead of equally partitioning the domains of variables appearing in multi-linear terms, we construct sparser partitions yet tighter relax- ations by iteratively partitioning the variable domains in regions of interest. This approach decouples the number of partitions from the size of the variable domains, leads to a significant reduction in computation time, and limits the number of binary variables that are introduced by the partitioning. We demonstrate the performance of our algorithm on well-known benchmark problems from MINLPLIB and discuss the computational benefits of CP-based bound tightening procedures.
Wireless network signals with moderately correlated shadowing still appear Poisson
Venue: arXiv cs.NI 2016
Year: 2016

We consider the point process of signal strengths emitted from transmitters in a wireless network and observed at a fixed position. In our model, transmitters are placed deterministically or randomly according to a hard core or Poisson point process and signals are subjected to power law path loss and random propagation effects that may be correlated between transmitters. We provide bounds on the distance between the point process of signal strengths and a Poisson process with the same mean measure, assuming correlated log-normal shadowing. For "strong shadowing" and moderate correlations, we find that the signal strengths are close to a Poisson process, generalizing a recently shown analogous result for independent shadowing.
Statistical Parametric Speech Synthesis Using Bottleneck Representation From Sequence Auto-encoder
Venue: arXiv cs.SD 2016
Year: 2016

In this paper, we describe a statistical parametric speech synthesis approach with unit-level acoustic representation. In conventional deep neural network based speech synthesis, the input text features are repeated for the entire duration of phoneme for mapping text and speech parameters. This mapping is learnt at the frame-level which is the de-facto acoustic representation. However much of this computational requirement can be drastically reduced if every unit can be represented with a fixed-dimensional representation. Using recurrent neural network based auto-encoder, we show that it is indeed possible to map units of varying duration to a single vector. We then use this acoustic representation at unit-level to synthesize speech using deep neural network based statistical parametric speech synthesis technique. Results show that the proposed approach is able to synthesize at the same quality as the conventional frame based approach at a highly reduced computational cost.
GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation
Venue: arXiv cs.IR 2016
Year: 2016

Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. However, previous POI recommendation systems model check-in sequences based on either tensor factorization or Markov chain model, which cannot capture contextual check-in information in sequences. The contextual check-in information implies the complementary functions among POIs that compose an individual's daily check-in sequence. In this paper, we exploit the embedding learning technique to capture the contextual check-in information and further propose the \textit{\textbf{SE}}quential \textit{\textbf{E}}mbedding \textit{\textbf{R}}ank (\textit{SEER}) model for POI recommendation. In particular, the \textit{SEER} model learns user preferences via a pairwise ranking model under the sequential constraint modeled by the POI embedding learning method. Furthermore, we incorporate two important factors, i.e., temporal influence and geographical influence, into the \textit{SEER} model to enhance the POI recommendation system. Due to the temporal variance of sequences on different days, we propose a temporal POI embedding model and incorporate the temporal POI representations into a temporal preference ranking model to establish the \textit{T}emporal \textit{SEER} (\textit{T-SEER}) model. In addition, We incorporate the geographical influence into the \textit{T-SEER} model and develop the \textit{\textbf{Geo-Temporal}} \textit{\textbf{SEER}} (\textit{GT-SEER}) model.
Fansmitter: Acoustic Data Exfiltration from (Speakerless) Air-Gapped Computers
Venue: arXiv cs.CR 2016
Year: 2016

Because computers may contain or interact with sensitive information, they are often air-gapped and in this way kept isolated and disconnected from the Internet. In recent years the ability of malware to communicate over an air-gap by transmitting sonic and ultrasonic signals from a computer speaker to a nearby receiver has been shown. In order to eliminate such acoustic channels, current best practice recommends the elimination of speakers (internal or external) in secure computers, thereby creating a so-called 'audio-gap'. In this paper, we present Fansmitter, a malware that can acoustically exfiltrate data from air-gapped computers, even when audio hardware and speakers are not present. Our method utilizes the noise emitted from the CPU and chassis fans which are present in virtually every computer today. We show that a software can regulate the internal fans' speed in order to control the acoustic waveform emitted from a computer. Binary data can be modulated and transmitted over these audio signals to a remote microphone (e.g., on a nearby mobile phone). We present Fansmitter's design considerations, including acoustic signature analysis, data modulation, and data transmission. We also evaluate the acoustic channel, present our results, and discuss countermeasures. Using our method we successfully transmitted data from air-gapped computer without audio hardware, to a smartphone receiver in the same room. We demonstrated the effective transmission of encryption keys and passwords from a distance of zero to eight meters, with bit rate of up to 900 bits/hour. We show that our method can also be used to leak data from different types of IT equipment, embedded systems, and IoT devices that have no audio hardware, but contain fans of various types and sizes.
Analytical Coverage Probability of a Typical User In Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

In a Poisson Point Process (PPP) network model, in which the locations of Base Stations (BSs) are randomly distributed according to a Spatial Poisson Process, has been recently used as a tractable stochastic model to analyse the performance of downlink Heterogeneous Cellular Networks (HCNs). The HCN is modelled as a multi-tier cellular network where each tier is characterised by the transmission power level, propagation path loss exponent and density of BSs. The current works on HCN enabling Intercell Interference Coordination (ICIC) technique usually deal with Strict Frequency Reuse (FR) or Soft FR with a reuse factor of $Δ=1$ in a Rayleigh fading channel. It has been assumed that all Base Stations (BSs) transmit continuously which leads to a reduction on the impact of number of users and RBs on network performance. In this paper, the performance of Soft FR with a reuse factor of $Δ>1$ in Rayleigh-Lognormal fading channel is evaluated. The impact of the number of users and Resource Blocks (RBs) on Intercell Interference (ICI) are presented for Round Robin scheduling and indicator functions. The results show that there are opposite trends between coverage probability of Cell-Center User (CCU) and Cell-Edge User (CEU).
Secure Multiparty Sessions with Topics
Venue: arXiv cs.LO 2016
Year: 2016

Multiparty session calculi have been recently equipped with security requirements, in order to guarantee properties such as access control and leak freedom. However, the proposed security requirements seem to be overly restrictive in some cases. In particular, a party is not allowed to communicate any kind of public information after receiving a secret information. This does not seem justified in case the two pieces of information are totally unrelated. The aim of the present paper is to overcome this restriction, by designing a type discipline for a simple multiparty session calculus, which classifies messages according to their topics and allows unrestricted sequencing of messages on independent topics.
Parallel Monitors for Self-adaptive Sessions
Venue: arXiv cs.LO 2016
Year: 2016

The paper presents a data-driven model of self-adaptivity for multiparty sessions. System choreography is prescribed by a global type. Participants are incarnated by processes associated with monitors, which control their behaviour. Each participant can access and modify a set of global data, which are able to trigger adaptations in the presence of critical changes of values. The use of the parallel composition for building global types, monitors and processes enables a significant degree of flexibility: an adaptation step can dynamically reconfigure a set of participants only, without altering the remaining participants, even if the two groups communicate.
Multiparty Compatibility for Concurrent Objects
Venue: arXiv cs.PL 2016
Year: 2016

Objects and actors are communicating state machines, offering and consuming different services at different points in their lifecycle. Two complementary challenges arise when programming such systems. When objects interact, their state machines must be "compatible", so that services are requested only when they are available. Dually, when objects refine other objects, their state machines must be "compliant", so that services are honoured whenever they are promised. In this paper we show how the idea of multiparty compatibility from the session types literature can be applied to both of these problems. We present an untyped language in which concurrent objects are checked automatically for compatibility and compliance. For simple objects, checking can be exhaustive and has the feel of a type system. More complex objects can be partially validated via test cases, leading to a methodology closer to continuous testing. Our proof-of-concept implementation is limited in some important respects, but demonstrates the potential value of the approach and the relationship to existing software development practices.
Towards the Integration of an Intuitionistic First-Order Prover into Coq
Venue: arXiv cs.LO 2016
Year: 2016

An efficient intuitionistic first-order prover integrated into Coq is useful to replay proofs found by external automated theorem provers. We propose a two-phase approach: An intuitionistic prover generates a certificate based on the matrix characterization of intuitionistic first-order logic; the certificate is then translated into a sequent-style proof.
Efficient Timestamps for Capturing Causality
Venue: arXiv cs.DC 2016
Year: 2016

Consider an asynchronous system consisting of processes that communicate via message-passing. The processes communicate over a potentially {\em incomplete} communication network consisting of reliable bidirectional communication channels. Thus, not every pair of processes is necessarily able to communicate with each other directly. % For instance, when the communication network is a {\em star} graph, there is a {\em central} process % that can communicate with all the remaining processes (which are called {\em radial} processes), % but the radial processes cannot communicate with each other directly. The goal of the algorithms discussed in this paper is to assign timestamps to the events at all the processes such that (a) distinct events are assigned distinct timestamps, and (b) the happened-before relationship between the events can be inferred from the timestamps. We consider three types of algorithms for assigning timestamps to events: (i) Online algorithms that must (greedily) assign a timestamp to each event when the event occurs. (ii) Offline algorithms that assign timestamps to event after a finite execution is complete. (iii) Inline algorithms that assign a timestamp to each event when it occurs, but may modify some elements of a timestamp again at a later time. For specific classes of graphs, particularly {\em star} graphs and graphs with connectivity $\geq 1$, the paper presents bounds on the length of vector timestamps assigned by an {\em online} algorithm. The paper then presents an {\em inline} algorithm, which typically assigns substantially smaller timestamps than the optimal-length {\em online} vector timestamps. In particular, the inline algorithm assigns timestamp in the form of a tuple containing $2c+2$ integer elements, where $c$ is the size of the vertex cover for the underlying communication graph.
F-index of Total Transformation Graphs
Venue: arXiv cs.DM 2016
Year: 2016

The F-index of a graph $G$ is the sum of the cubes of the degrees of the vertices of $G$. In this paper, explicit expressions for the F-index of different transformation graphs of type ${{G}^{xyz}}$ with $x, y, z\in \{-, + \}$ are obtained. F-index for semitotal point graph and semitotal line graph are also obtained here.
Bandit-Based Random Mutation Hill-Climbing
Venue: arXiv cs.AI 2016
Year: 2016

The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it is better than or equal to it. In this work, we propose to use a novel method to select the neighbour solution using a set of independent multi- armed bandit-style selection units which results in a bandit-based Random Mutation Hill-Climbing algorithm. The new algorithm significantly outperforms Random Mutation Hill-Climbing in both OneMax (in noise-free and noisy cases) and Royal Road problems (in the noise-free case). The algorithm shows particular promise for discrete optimisation problems where each fitness evaluation is expensive.
Impact of robot responsiveness and adult involvement on children's social behaviours in human-robot interaction
Venue: arXiv cs.RO 2016
Year: 2016

A key challenge in developing engaging social robots is creating convincing, autonomous and responsive agents, which users perceive, and treat, as social beings. As a part of the collaborative project: Expressive Agents for Symbiotic Education and Learning (EASEL), this study examines the impact of autonomous response to children's speech, by the humanoid robot Zeno, on their interactions with it as a social entity. Results indicate that robot autonomy and adult assistance during HRI can substantially influence children's behaviour during interaction and their affect after. Children working with a fully-autonomous, responsive robot demonstrated greater physical activity following robot instruction than those working with a less responsive robot, which required adult assistance to interact with. During dialogue with the robot, children working with the fully-autonomous robot also looked towards the robot in anticipation of its vocalisations on more occasions. In contrast, a less responsive robot, requiring adult assistance to interact with, led to greater self-report positive affect and more occasions of children looking to the robot in response to its vocalisations. We discuss the broader implications of these findings in terms of anthropomorphism of social robots and in relation to the overall project strategy to further the understanding of how interactions with social robots could lead to task-appropriate symbiotic relationships.
Introducing a Calculus of Effects and Handlers for Natural Language Semantics
Venue: arXiv cs.CL 2016
Year: 2016

In compositional model-theoretic semantics, researchers assemble truth-conditions or other kinds of denotations using the lambda calculus. It was previously observed that the lambda terms and/or the denotations studied tend to follow the same pattern: they are instances of a monad. In this paper, we present an extension of the simply-typed lambda calculus that exploits this uniformity using the recently discovered technique of effect handlers. We prove that our calculus exhibits some of the key formal properties of the lambda calculus and we use it to construct a modular semantics for a small fragment that involves multiple distinct semantic phenomena.
Desain dan Implementasi Sistem Digital Assistant Berbasis Google Glass pada Rumah Sakit
Venue: arXiv cs.CY 2016
Year: 2016

In order to improve the performance of the hospital, one solution that is needed is a system that can facilitate functional medical staff in the discharge of their duties. This paper discusses the design and implementation of digital assistant system designed for functional medical staff at the hospital. This system is implemented in Google Glass, PC, tablet, and smart phones. Google Glass device is equipped with two modules, which are patient data module with face recognition feature and communication module with livestreaming feature. Tablet devices and mobile phones are equipped with the personnel module and display medical records module. Tablet devices are also equipped with prescription writing. The whole process in all four devices require webservice to be executed. The implementation results indicate that the four software has been built based on the design and integrated with HIS (Hospital Information System) database via web service. ---- Demi meningkatkan kinerja rumah sakit, salah satu solusi yang diperlukan adalah sebuah sistem yang dapat mempermudah staf medik fungsional dalam menunaikan tugasnya. Makalah ini membahas mengenai desain dan implementasi sistem digital assistant yang dirancang untuk staf medik fungsional di rumah sakit. Sistem ini diimplementasikan pada perangkat keras Google Glass, PC, tablet, dan ponsel pintar. Perangkat Google Glass dilengkapi dengan dua fitur yaitu face recognition dan live streaming. Perangkat tablet dan ponsel dilengkapi dengan modul personalia dan modul tampilan rekam medis. Perangkat tablet juga dilengkapi dengan fitur penulisan resep. Seluruh proses pada keempat perangkat tersebut membutuhkan web service untuk dapat dieksekusi. Hasil implementasi mengindikasikan bahwa keempat perangkat lunak telah sesuai dengan rancangan dan terintegrasikan dengan database SIRS tingkat rumah sakit melalui web service.
Rating Effects on Social News Posts and Comments
Venue: arXiv cs.SI 2016
Year: 2016

At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in-vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02% on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings on average, of posts by 5.15% and of comments by 37.4%. Compared to the control group, the probability of reaching a high rating (>=2000) for posts is increased by 24.6% when posts receive the positive treatment and for comments is decreased by 46.6% when comments receive the negative treatment.
A Note on Efficiency of Downsampling and Color Transformation in Image Quality Assessment
Venue: arXiv cs.MM 2016
Year: 2016

Several existing and successful full reference image quality assessment (IQA) models use linear color transformation and downsampling before measuring similarity or quality of images. This paper indicates to the right order of these two procedures and that the existing models have not chosen the more efficient approach. In addition, efficiency of these metrics is not compared in a fair basis in the literature.
Detection and Tracking of Liquids with Fully Convolutional Networks
Venue: arXiv cs.CV 2016
Year: 2016

Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network. Our results show that the best liquid detection results are achieved when aggregating data over multiple frames, in contrast to standard image segmentation. They also show that the LSTM network outperforms the other two in both tasks. This suggests that LSTM-based neural networks have the potential to be a key component for enabling robots to handle liquids using robust, closed-loop controllers.