OpenRaiser/Intern-Atlas huggingface dataset:
Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification
Venue: arXiv cs.CV 2018
Year: 2018

In the past decades, Vector Quantization (VQ) model has been very popular across different pattern recognition areas, especially for feature-based tasks. However, the classification or regression performance of VQ-based systems always confronts the feature mismatch problem, which will heavily affect the performance of them. In this paper, we propose a two-stage iterative Procrustes algorithm (TIPM) to address the feature mismatch problem for VQ-based applications. At the first stage, the algorithm will remove mismatched feature vector pairs for a pair of input feature sets. Then, the second stage will collect those correct matched feature pairs that were discarded during the first stage. To evaluate the effectiveness of the proposed TIPM algorithm, speaker verification is used as the case study in this paper. The experiments were conducted on the TIMIT database and the results show that TIPM can improve VQ-based speaker verification performance clean condition and all noisy conditions.
Social-Feature Enabled Communications among Devices towards Smart IoT Community
Venue: arXiv cs.NI 2018
Year: 2018

Future IoT is expected to get ubiquitous connection and access in a global scale. In the meantime, with the empowered communications capability for IoT devices, IoT will evolve to be highly autonomous, even under the help from infrastructure, and thus will gradually establish the smart IoT community. One of the major challenges imposed on the smart IoT communities is the socialization of IoT communications, because massive IoT accesses make centralized control very hard and also face the shortage of spectrum resources. Towards these issues, we in this article first present the overview and discussions on social features affecting connections among devices. Then, we motivate studies on the statistical characteristics of social features for connections among devices towards smart IoT. We further propose the queuing model under unified asymptotic analyses framework to characterize the statistical social features, with emphases on typical social metrics such as credit, reputation, centrality, etc. How to apply these features for network optimization is further suggested. Finally, we share our opinion on the open problems of social-aware design towards future smart IoT.
Efficient Evaluation of the Number of False Alarm Criterion
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involvement for tuning detection thresholds, and allow one to account fairly for different pattern sizes. On the other hand, a-contrario criteria become intractable when the pattern complexity in terms of parametrization increases. In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick. This space allows us to store partial computations which are required by the a-contrario criterion, and to evaluate the significance with a lower computational cost than by following a straightforward approach. The method is illustrated on synthetic examples on patterns with various parametrizations up to five dimensions. In order to demonstrate how to apply this generic concept in a real scenario, we consider a difficult crack detection task in still images, which has been addressed in the literature with various local and global detection strategies. We model cracks as bounded segments, detected by the proposed a-contrario criterion, which allow us to introduce additional spatial constraints based on their relative alignment. On this application, the proposed strategy yields state-of the-art results, and underlines its potential for handling complex pattern detection tasks.
Window Opening Model using Deep Learning Methods
Venue: arXiv cs.LG 2018
Year: 2018

Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards the over-represented class where windows remained closed. In addition, they require tuning for each occupant which can not be efficiently scaled to the increased number of occupants. This paper presents a window opening model for commercial buildings using deep learning methods. The model is trained using data from occupants from an office building in Germany. In total the model is evaluated using almost 20 mio. data points from 3 independent buildings, located in Aachen, Frankfurt and Philadelphia. Eventually, the results of 3100 core hours of model development are summarized, which makes this study the largest of its kind in window states modeling. Additionally, the practical potential of the proposed model was tested by incorporating it in the Modelica-based thermal building simulation. The resulting evaluation accuracy and F1 scores on the office buildings ranged between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 % points in case of sparse input data, while the F1 score remained high.
Foreign English Accent Adjustment by Learning Phonetic Patterns
Venue: arXiv cs.SD 2018
Year: 2018

State-of-the-art automatic speech recognition (ASR) systems struggle with the lack of data for rare accents. For sufficiently large datasets, neural engines tend to outshine statistical models in most natural language processing problems. However, a speech accent remains a challenge for both approaches. Phonologists manually create general rules describing a speaker's accent, but their results remain underutilized. In this paper, we propose a model that automatically retrieves phonological generalizations from a small dataset. This method leverages the difference in pronunciation between a particular dialect and General American English (GAE) and creates new accented samples of words. The proposed model is able to learn all generalizations that previously were manually obtained by phonologists. We use this statistical method to generate a million phonological variations of words from the CMU Pronouncing Dictionary and train a sequence-to-sequence RNN to recognize accented words with 59% accuracy.
A note on the integrality gap of the configuration LP for restricted Santa Claus
Venue: arXiv cs.DS 2018
Year: 2018

In the restricted Santa Claus problem we are given resources $\mathcal R$ and players $\mathcal P$. Every resource $j\in\mathcal R$ has a value $v_j$ and every player $i$ desires a set $\mathcal R(i)$ of resources. We are interested in distributing the resources to players that desire them. The quality of a solution is measured by the least happy player, i.e., the lowest sum of resource values. This value should be maximized. The local search algorithm by Asadpour et al. and its connection to the configuration LP has proved itself to be a very influential technique for this and related problems. In the original proof, a local search was used to obtain a bound of $4$ for the ratio of the fractional to the integral optimum of the configuration LP (integrality gap). This bound is non-constructive since the local search has not been shown to terminate in polynomial time. On the negative side, the worst instance known has an integrality gap of $2$. Although much progress was made in this area, neither bound has been improved since. We present a better analysis that shows the integrality gap is not worse than $3 + 5/6 \approx 3.8333$.
Rule- and context-based dynamic business process modelling and simulation
Venue: arXiv cs.SE 2018
Year: 2018

The traditional approach used to implement a business process (BP) in today's information systems (IS) no longer covers the actual needs of the dynamically changing business. Therefore, a necessity for a new approach of dynamic business process (DBP) modelling and simulation has arisen. To date, existing approaches to DBP modelling and simulation have been incomplete, i.e. they lack theory or a case study or both. Furthermore, there is no commonly accepted definition of BDP. Current BP modelling tools are suitable almost solely for the modelling and simulation of a static BP that strictly prescribes which activities, and in which sequence, to execute. Usually, a DBP is not defined strictly at the beginning of its execution, and it changes under new conditions at runtime. In our paper, we propose six requirements of DBP and an approach for rule- and context-based DBP modelling and simulation. The approach is based on changing BP rules, BP actions and their sequences at process instance runtime, according to the new business system context. Based on the proposed approach, a reference architecture and prototype of a DBP simulation tool were developed. Modelling and simulation were carried out using this prototype, and the case study shows correspondence to the needs of dynamically changing business, as well as possibilities for modelling and simulating DBP.
On a Class of Stochastic Multilayer Networks
Venue: arXiv cs.SI 2018
Year: 2018

In this paper, we introduce a new class of stochastic multilayer networks. A stochastic multilayer network is the aggregation of $M$ networks (one per layer) where each is a subgraph of a foundational network $G$. Each layer network is the result of probabilistically removing links and nodes from $G$. The resulting network includes any link that appears in at least $K$ layers. This model is an instance of a non-standard site-bond percolation model. Two sets of results are obtained: first, we derive the probability distribution that the $M$-layer network is in a given configuration for some particular graph structures (explicit results are provided for a line and an algorithm is provided for a tree), where a configuration is the collective state of all links (each either active or inactive). Next, we show that for appropriate scalings of the node and link selection processes in a layer, links are asymptotically independent as the number of layers goes to infinity, and follow Poisson distributions. Numerical results are provided to highlight the impact of having several layers on some metrics of interest (including expected size of the cluster a node belongs to in the case of the line). This model finds applications in wireless communication networks with multichannel radios, multiple social networks with overlapping memberships, transportation networks, and, more generally, in any scenario where a common set of nodes can be linked via co-existing means of connectivity.
Linguistic Characteristics of Censorable Language on SinaWeibo
Venue: arXiv cs.CL 2018
Year: 2018

This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability.
Sparse Representation and Non-Negative Matrix Factorization for image denoise
Venue: arXiv cs.CV 2018
Year: 2018

Recently, the problem of blind image separation has been widely investigated, especially the medical image denoise which is the main step in medical diag-nosis. Removing the noise without affecting relevant features of the image is the main goal. Sparse decomposition over redundant dictionaries become of the most used approaches to solve this problem. NMF codes naturally favor sparse, parts-based representations. In sparse representation, signals represented as a linear combination of a redundant dictionary atoms. In this paper, we propose an algorithm based on sparse representation over the redundant dictionary and Non-Negative Matrix Factorization (N-NMF). The algorithm initializes a dic-tionary based on training samples constructed from noised image, then it searches for the best representation for the source by using the approximate matching pursuit (AMP). The proposed N-NMF gives a better reconstruction of an image from denoised one. We have compared our numerical results with different image denoising techniques and we have found the performance of the proposed technique is promising. Keywords: Image denoising, sparse representation, dictionary learning, matching pursuit, non-negative matrix factorization.
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis
Venue: arXiv cs.LG 2018
Year: 2018

Recurrent auto-encoder model summarises sequential data through an encoder structure into a fixed-length vector and then reconstructs the original sequence through the decoder structure. The summarised vector can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the decoder output so that it performs partial reconstruction. The fixed-length vector therefore represents features in the selected dimensions only. In addition, we propose using rolling fixed window approach to generate training samples from unbounded time series data. The change of time series features over time can be summarised as a smooth trajectory path. The fixed-length vectors are further analysed using additional visualisation and unsupervised clustering techniques. The proposed method can be applied in large-scale industrial processes for sensors signal analysis purpose, where clusters of the vector representations can reflect the operating states of the industrial system.
Network Classification in Temporal Networks Using Motifs
Venue: arXiv cs.SI 2018
Year: 2018

Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accuracy by up $10\%$ compared to the state-of-the-art embedding method in network classification, for tasks such as classifying network type, identifying communities in email exchange network, and identifying users given their app-switching behaviors.
On the Performance of NOMA with Hybrid ARQ
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate the outage performance of hybrid automatic repeat request with chase combining (HARQ-CC) assisted downlink non-orthogonal multiple access (NOMA) systems. A closed-form expression of the individual outage probability and the diversity gain are obtained firstly. Based on the developed analytical outage probability, a tradeoff between the minimum number of retransmissions and the transmit power allocation coefficient is then provided for a given target rate. The provided simulation results demonstrate the accuracy of the developed analytical results. Moreover, it is shown that NOMA combined with the HARQ-CC can achieve a significant advantage when only average channel state information is known at the transmitter. Particularly, the performance of the user with less transmit power in NOMA systems can be efficiently improved by utilizing HARQ-CC.
Improved Time and Space Bounds for Dynamic Range Mode
Venue: arXiv cs.DS 2018
Year: 2018

Given an array A of $n$ elements, we wish to support queries for the most frequent and least frequent element in a subrange $[l, r]$ of $A$. We also wish to support updates that change a particular element at index $i$ or insert/ delete an element at index $i$. For the range mode problem, our data structure supports all operations in $O(n^{2/3})$ deterministic time using only $O(n)$ space. This improves two results by Chan et al. \cite{C14}: a linear space data structure supporting update and query operations in $\tilde{O}(n^{3/4})$ time and an $O(n^{4/3})$ space data structure supporting update and query operations in $\tilde{O}(n^{2/3})$ time. For the range least frequent problem, we address two variations. In the first, we are allowed to answer with an element of $A$ that may not appear in the query range, and in the second, the returned element must be present in the query range. For the first variation, we develop a data structure that supports queries in $\tilde{O}(n^{2/3})$ time, updates in $O(n^{2/3})$ time, and occupies $O(n)$ space. For the second variation, we develop a Monte Carlo data structure that supports queries in $O(n^{2/3})$ time, updates in $\tilde{O}(n^{2/3})$ time, and occupies $\tilde{O}(n)$ space, but requires that updates are made independently of the results of previous queries. The Monte Carlo data structure is also capable of answering $k$-frequency queries; that is, the problem of finding an element of given frequency in the specified query range. Previously, no dynamic data structures were known for least frequent element or $k$-frequency queries.
Online Facility Location with Deletions
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we study three previously unstudied variants of the online Facility Location problem, considering an intrinsic scenario when the clients and facilities are not only allowed to arrive to the system, but they can also depart at any moment. We begin with the study of a natural fully-dynamic online uncapacitated model where clients can be both added and removed. When a client arrives, then it has to be assigned either to an existing facility or to a new facility opened at the client's location. However, when a client who has been also one of the open facilities is to be removed, then our model has to allow to reconnect all clients that have been connected to that removed facility. In this model, we present an optimal O(log n_act / log log n_act)-competitive algorithm, where n_act is the number of active clients at the end of the input sequence. Next, we turn our attention to the capacitated Facility Location problem. We first note that if no deletions are allowed, then one can achieve an optimal competitive ratio of O(log n/ log log n), where n is the length of the sequence. However, when deletions are allowed, the capacitated version of the problem is significantly more challenging than the uncapacitated one. We show that still, using a more sophisticated algorithmic approach, one can obtain an online O(log m + log c log n)-competitive algorithm for the capacitated Facility Location problem in the fully dynamic model, where m is number of points in the input metric and c is the capacity of any open facility.
Effects of Some Lattice Reductions on the Success Probability of the Zero-Forcing Decoder
Venue: arXiv cs.IT 2018
Year: 2018

Zero-forcing (ZF) decoder is a commonly used approximation solution of the integer least squares problem which arises in communications and many other applications. Numerically simulations have shown that the LLL reduction can usually improve the success probability $P_{ZF}$ of the ZF decoder. In this paper, we first rigorously show that both SQRD and V-BLAST, two commonly used lattice reductions, have no effect on $P_{ZF}$. Then, we show that LLL reduction can improve $P_{ZF}$ when $n=2$, we also analyze how the parameter $δ$ in the LLL reduction affects the enhancement of $P_{ZF}$. Finally, an example is given which shows that the LLL reduction decrease $P_{ZF}$ when $n\geq3$.
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
Venue: arXiv cs.CL 2018
Year: 2018

Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a \textit{Seq2Seq Modality Translation Model} and a \textit{Hierarchical Seq2Seq Modality Translation Model}. We also explore multiple different variations on the multimodal inputs and outputs of these seq2seq models. Our experiments on multimodal sentiment analysis using the CMU-MOSI dataset indicate that our methods learn informative multimodal representations that outperform the baselines and achieve improved performance on multimodal sentiment analysis, specifically in the Bimodal case where our model is able to improve F1 Score by twelve points. We also discuss future directions for multimodal Seq2Seq methods.
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Venue: arXiv cs.SE 2018
Year: 2018

Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency. Smart contracts are programs running on top of the blockchain consensus protocol to enable people make agreements while minimizing trusts. Millions of smart contracts have been deployed in various decentralized applications. The security vulnerabilities within those smart contracts pose significant threats to their applications. Indeed, many critical security vulnerabilities within smart contracts on Ethereum platform have caused huge financial losses to their users. In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities. ContractFuzzer generates fuzzing inputs based on the ABI specifications of smart contracts, defines test oracles to detect security vulnerabilities, instruments the EVM to log smart contracts runtime behaviors, and analyzes these logs to report security vulnerabilities. Our fuzzing of 6991 smart contracts has flagged more than 459 vulnerabilities with high precision. In particular, our fuzzing tool successfully detects the vulnerability of the DAO contract that leads to USD 60 million loss and the vulnerabilities of Parity Wallet that have led to the loss of $30 million and the freezing of USD 150 million worth of Ether.
UniParse: A universal graph-based parsing toolkit
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes the design and use of the graph-based parsing framework and toolkit UniParse, released as an open-source python software package. UniParse as a framework novelly streamlines research prototyping, development and evaluation of graph-based dependency parsing architectures. UniParse does this by enabling highly efficient, sufficiently independent, easily readable, and easily extensible implementations for all dependency parser components. We distribute the toolkit with ready-made configurations as re-implementations of all current state-of-the-art first-order graph-based parsers, including even more efficient Cython implementations of both encoders and decoders, as well as the required specialised loss functions.
Decision method choice in a human posture recognition context
Venue: arXiv cs.AI 2018
Year: 2018

Human posture recognition provides a dynamic field that has produced many methods. Using fuzzy subsets based data fusion methods to aggregate the results given by different types of recognition processes is a convenient way to improve recognition methods. Nevertheless, choosing a defuzzification method to imple-ment the decision is a crucial point of this approach. The goal of this paper is to present an approach where the choice of the defuzzification method is driven by the constraints of the final data user, which are expressed as limitations on indica-tors like confidence or accuracy. A practical experimentation illustrating this ap-proach is presented: from a depth camera sensor, human posture is interpreted and the defuzzification method is selected in accordance with the constraints of the final information consumer. The paper illustrates the interest of the approach in a context of postures based human robot communication.