OpenRaiser/Intern-Atlas huggingface dataset:
An Efficiently Searchable Encrypted Data Structure for Range Queries
Venue: arXiv cs.CR 2017
Year: 2017

At CCS 2015 Naveed et al. presented first attacks on efficiently searchable encryption, such as deterministic and order-preserving encryption. These plaintext guessing attacks have been further improved in subsequent work, e.g. by Grubbs et al. in 2016. Such cryptanalysis is crucially important to sharpen our understanding of the implications of security models. In this paper we present an efficiently searchable, encrypted data structure that is provably secure against these and even more powerful chosen plaintext attacks. Our data structure supports logarithmic-time search with linear space complexity. The indices of our data structure can be used to search by standard comparisons and hence allow easy retrofitting to existing database management systems. We implemented our scheme and show that its search time overhead is only 10 milliseconds compared to non-secure search.
Research on several key technologies in practical speech emotion recognition
Venue: arXiv cs.SD 2017
Year: 2017

In this dissertation the practical speech emotion recognition technology is studied, including several cognitive related emotion types, namely fidgetiness, confidence and tiredness. The high quality of naturalistic emotional speech data is the basis of this research. The following techniques are used for inducing practical emotional speech: cognitive task, computer game, noise stimulation, sleep deprivation and movie clips. A practical speech emotion recognition system is studied based on Gaussian mixture model. A two-class classifier set is adopted for performance improvement under the small sample case. Considering the context information in continuous emotional speech, a Gaussian mixture model embedded with Markov networks is proposed. A further study is carried out for system robustness analysis. First, noise reduction algorithm based on auditory masking properties is fist introduced to the practical speech emotion recognition. Second, to deal with the complicated unknown emotion types under real situation, an emotion recognition method with rejection ability is proposed, which enhanced the system compatibility against unknown emotion samples. Third, coping with the difficulties brought by a large number of unknown speakers, an emotional feature normalization method based on speaker-sensitive feature clustering is proposed. Fourth, by adding the electrocardiogram channel, a bi-modal emotion recognition system based on speech signals and electrocardiogram signals is first introduced. The speech emotion recognition methods studied in this dissertation may be extended into the cross-language speech emotion recognition and the whispered speech emotion recognition.
Information processing features can detect behavioral regimes of dynamical systems
Venue: arXiv cs.IT 2017
Year: 2017

In dynamical systems, local interactions between dynamical units generate correlations which are stored and transmitted throughout the system, generating the macroscopic behavior. However a framework to quantify and study this at the microscopic scale is missing. Here we propose an 'information processing' framework based on Shannon mutual information quantities between the initial and future states. We apply it to the 256 elementary cellular automata (ECA), which are the simplest possible dynamical systems exhibiting behaviors ranging from simple to complex. Our main finding for ECA is that only a few features are needed for full predictability and that the 'information integration' (synergy) feature is always most predictive. Finally we apply the formalism to foreign exchange (FX) and interest-rate swap (IRS) time series data and find that the 2008 financial crisis marks a sudden and sustained regime shift (FX and EUR IRS) resembling tipping point behavior. The USD IRS market exhibits instead a slow and steady progression which appears consistent with the hypothesis that this market is (part of) the driving force behind the crisis. Our work suggests that the proposed framework is a promising way of predicting emergent complex systemic behaviors in terms of the local information processing of units.
Combining Prediction of Human Decisions with ISMCTS in Imperfect Information Games
Venue: arXiv cs.MA 2017
Year: 2017

Monte Carlo Tree Search (MCTS) has been extended to many imperfect information games. However, due to the added complexity that uncertainty introduces, these adaptations have not reached the same level of practical success as their perfect information counterparts. In this paper we consider the development of agents that perform well against humans in imperfect information games with partially observable actions. We introduce the Semi-Determinized-MCTS (SDMCTS), a variant of the Information Set MCTS algorithm (ISMCTS). More specifically, SDMCTS generates a predictive model of the unobservable portion of the opponent's actions from historical behavioral data. Next, SDMCTS performs simulations on an instance of the game where the unobservable portion of the opponent's actions are determined. Thereby, it facilitates the use of the predictive model in order to decrease uncertainty. We present an implementation of the SDMCTS applied to the Cheat Game, a well-known card game, with partially observable (and often deceptive) actions. Results from experiments with 120 subjects playing a head-to-head Cheat Game against our SDMCTS agents suggest that SDMCTS performs well against humans, and its performance improves as the predictive model's accuracy increases.
Flexible Support for Fast Parallel Commutative Updates
Venue: arXiv cs.DC 2017
Year: 2017

Privatizing data is a useful strategy for increasing parallelism in a shared memory multithreaded program. Independent cores can compute independently on duplicates of shared data, combining their results at the end of their computations. Conventional approaches to privatization, however, rely on explicit static or dynamic memory allocation for duplicated state, increasing memory footprint and contention for cache resources, especially in shared caches. In this work, we describe CCache, a system for on-demand privatization of data manipulated by commutative operations. CCache garners the benefits of privatization, without the increase in memory footprint or cache occupancy. Each core in CCache dynamically privatizes commutatively manipulated data, operating on a copy. Periodically or at the end of its computation, the core merges its value with the value resident in memory, and when all cores have merged, the in-memory copy contains the up-to-date value. We describe a low-complexity architectural implementation of CCache that extends a conventional multicore to support on-demand privatization without using additional memory for private copies. We evaluate CCache on several high-value applications, including random access key-value store, clustering, breadth first search and graph ranking, showing speedups upto 3.2X.
Drought Stress Classification using 3D Plant Models
Venue: arXiv cs.CV 2017
Year: 2017

Quantification of physiological changes in plants can capture different drought mechanisms and assist in selection of tolerant varieties in a high throughput manner. In this context, an accurate 3D model of plant canopy provides a reliable representation for drought stress characterization in contrast to using 2D images. In this paper, we propose a novel end-to-end pipeline including 3D reconstruction, segmentation and feature extraction, leveraging deep neural networks at various stages, for drought stress study. To overcome the high degree of self-similarities and self-occlusions in plant canopy, prior knowledge of leaf shape based on features from deep siamese network are used to construct an accurate 3D model using structure from motion on wheat plants. The drought stress is characterized with a deep network based feature aggregation. We compare the proposed methodology on several descriptors, and show that the network outperforms conventional methods.
A New Multifocus Image Fusion Method Using Contourlet Transform
Venue: arXiv cs.CV 2017
Year: 2017

A new multifocus image fusion approach is presented in this paper. First the contourlet transform is used to decompose the source images into different components. Then, some salient features are extracted from components. In order to extract salient features, spatial frequency is used. Subsequently, the best coefficients from the components are selected by the maximum selection rule. Finally, the inverse contourlet transform is applied to the selected coefficients. Experiments show the superiority of the proposed method.
Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition
Venue: arXiv cs.CL 2017
Year: 2017

Named Entity Recognition (NER) is one of the most common tasks of the natural language processing. The purpose of NER is to find and classify tokens in text documents into predefined categories called tags, such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others. Although there is a number of approaches have been proposed for this task in Russian language, it still has a substantial potential for the better solutions. In this work, we studied several deep neural network models starting from vanilla Bi-directional Long Short-Term Memory (Bi-LSTM) then supplementing it with Conditional Random Fields (CRF) as well as highway networks and finally adding external word embeddings. All models were evaluated across three datasets: Gareev's dataset, Person-1000, FactRuEval-2016. We found that extension of Bi-LSTM model with CRF significantly increased the quality of predictions. Encoding input tokens with external word embeddings reduced training time and allowed to achieve state of the art for the Russian NER task.
Functional Characterization of Deformation Fields
Venue: arXiv cs.GR 2017
Year: 2017

In this paper we present a novel representation for deformation fields of 3D shapes, by considering the induced changes in the underlying metric. In particular, our approach allows to represent a deformation field in a coordinate-free way as a linear operator acting on real-valued functions defined on the shape. Such a representation both provides a way to relate deformation fields to other classical functional operators and enables analysis and processing of deformation fields using standard linear-algebraic tools. This opens the door to a wide variety of applications such as explicitly adding extrinsic information into the computation of functional maps, intrinsic shape symmetrization, joint deformation design through precise control of metric distortion, and coordinate-free deformation transfer without requiring pointwise correspondences. Our method is applicable to both surface and volumetric shape representations and we guarantee the equivalence between the operator-based and standard deformation field representation under mild genericity conditions in the discrete setting. We demonstrate the utility of our approach by comparing it with existing techniques and show how our representation provides a powerful toolbox for a wide variety of challenging problems.
WHY: Natural Explanations from a Robot Navigator
Venue: arXiv cs.AI 2017
Year: 2017

Effective collaboration between a robot and a person requires natural communication. When a robot travels with a human companion, the robot should be able to explain its navigation behavior in natural language. This paper explains how a cognitively-based, autonomous robot navigation system produces informative, intuitive explanations for its decisions. Language generation here is based upon the robot's commonsense, its qualitative reasoning, and its learned spatial model. This approach produces natural explanations in real time for a robot as it navigates in a large, complex indoor environment.
Multi-Erasure Locally Recoverable Codes Over Small Fields
Venue: arXiv cs.IT 2017
Year: 2017

Erasure codes play an important role in storage systems to prevent data loss. In this work, we study a class of erasure codes called Multi-Erasure Locally Recoverable Codes (ME-LRCs) for storage arrays. Compared to previous related works, we focus on the construction of ME-LRCs over small fields. We first develop upper and lower bounds on the minimum distance of ME-LRCs. Our main contribution is to propose a general construction of ME-LRCs based on generalized tensor product codes, and study their erasure-correcting properties. A decoding algorithm tailored for erasure recovery is given, and correctable erasure patterns are identified. We then prove that our construction yields optimal ME-LRCs with a wide range of code parameters, and present some explicit ME-LRCs over small fields. Finally, we show that generalized integrated interleaving (GII) codes can be treated as a subclass of generalized tensor product codes, thus defining the exact relation between these codes.
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
Venue: arXiv cs.LG 2017
Year: 2017

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.
Educational game design: game elements for promoting engagement
Venue: arXiv cs.HC 2017
Year: 2017

Engagement in educational games, a recently popular academic topic, has been shown to increase learning performance, as well as a number of attitudinal factors, such as intrinsic interest and motivation. However, there is a lack of research on how games can be designed to promote engagement. This mixed methods case study aimed to discover effective game elements for promoting 17-18 year old high school students' engagement with an educational game. Using within-case and cross-case analyses and triangulated data, 10 elements emerged and were categorized into the constructs of story, gameplay, and atmosphere. Examples and connections to the literature for each element are reported. Findings implicate that educational game design for both learning and engagement is composed of educational-game specific elements, game design for solely engagement is similar for both educational and entertainment games, and a gap on educational game design technique instead of theory should be addressed to further benefit educational game development.
A Branch-and-Cut Algorithm to Design LDPC Codes without Small Cycles in Communication Systems
Venue: arXiv cs.IT 2017
Year: 2017

In a digital communication system, information is sent from one place to another over a noisy communication channel using binary symbols (bits). Original information is encoded by adding redundant bits, which are then used by low--density parity--check (LDPC) codes to detect and correct errors that may have been introduced during transmission. Error correction capability of an LDPC code is severely degraded due to harmful structures such as small cycles in its bipartite graph representation known as Tanner graph (TG). We introduce an integer programming formulation to generate a TG for a given smallest cycle length. We propose a branch-and-cut algorithm for its solution and investigate structural properties of the problem to derive valid inequalities and variable fixing rules. We introduce a heuristic to obtain feasible solutions of the problem. Our computational experiments show that our algorithm can generate LDPC codes without small cycles in acceptable amount of time for practically relevant code lengths.
Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report
Venue: arXiv cs.IR 2017
Year: 2017

Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users to also share comments and opinions through textual reviews. In this paper, we introduce a new recommendation approach which exploits the semantic annotation of user reviews to extract useful and non-trivial information about the items to recommend. It also relies on the knowledge freely available in the Web of Data, notably in DBpedia and Wikidata, to discover other resources connected with the annotated entities. We evaluated our approach in three domains, using both DBpedia and Wikidata. The results showed that our solution provides a better ranking than another recommendation method based on the Web of Data, while it improves in novelty with respect to traditional techniques based on ratings. Additionally, our method achieved a better performance with Wikidata than DBpedia.
A Simple and Efficient MapReduce Algorithm for Data Cube Materialization
Venue: arXiv cs.DB 2017
Year: 2017

Data cube materialization is a classical database operator introduced in Gray et al.~(Data Mining and Knowledge Discovery, Vol.~1), which is critical for many analysis tasks. Nandi et al.~(Transactions on Knowledge and Data Engineering, Vol.~6) first studied cube materialization for large scale datasets using the MapReduce framework, and proposed a sophisticated modification of a simple broadcast algorithm to handle a dataset with a 216GB cube size within 25 minutes with 2k machines in 2012. We take a different approach, and propose a simple MapReduce algorithm which (1) minimizes the total number of copy-add operations, (2) leverages locality of computation, and (3) balances work evenly across machines. As a result, the algorithm shows excellent performance, and materialized a real dataset with a cube size of 35.0G tuples and 1.75T bytes in 54 minutes, with 0.4k machines in 2014.
Thread-Modular Static Analysis for Relaxed Memory Models
Venue: arXiv cs.PL 2017
Year: 2017

We propose a memory-model-aware static program analysis method for accurately analyzing the behavior of concurrent software running on processors with weak consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of our method is a unified framework for deciding the feasibility of inter-thread interferences to avoid propagating spurious data flows during static analysis and thus boost the performance of the static analyzer. We formulate the checking of interference feasibility as a set of Datalog rules which are both efficiently solvable and general enough to capture a range of hardware-level memory models. Compared to existing techniques, our method can significantly reduce the number of bogus alarms as well as unsound proofs. We implemented the method and evaluated it on a large set of multithreaded C programs. Our experiments showthe method significantly outperforms state-of-the-art techniques in terms of accuracy with only moderate run-time overhead.
Mechanism Design for Demand Response Programs with financial and non-monetary (social) Incentives
Venue: arXiv cs.GT 2017
Year: 2017

Most demand management approaches with non-mandatory policies assume full users' cooperation, which may not be the case given users' beliefs, needs and preferences. In this paper we propose a mechanism for demand management including incentives both with and without money. The mechanism is validated by means of simulation, modeling the consumers as a finite multiagent system which evolves until a stable state, and social incentives diffusion using opinion dynamics.
Compressão de dados sem perdas para dispositivos IoT
Venue: arXiv cs.NI 2017
Year: 2017

In environments with energy and processing constraints, such as sensor networks and embedded devices, sending raw information over wireless networks can be costly. In order to reduce the amount of transmitted data and ultimately save energy, we can compress data before transmission. In this paper, we tackle such problem in the IoT domain by deploying two widely used libraries to deliver asynchronous messages and data compression/decompression. We evaluate both our methodology and the compression/decompression performance of the embedded library in a micro-controller for IoT.
Photometric Stabilization for Fast-forward Videos
Venue: arXiv cs.GR 2017
Year: 2017

Videos captured by consumer cameras often exhibit temporal variations in color and tone that are caused by camera auto-adjustments like white-balance and exposure. When such videos are sub-sampled to play fast-forward, as in the increasingly popular forms of timelapse and hyperlapse videos, these temporal variations are exacerbated and appear as visually disturbing high frequency flickering. Previous techniques to photometrically stabilize videos typically rely on computing dense correspondences between video frames, and use these correspondences to remove all color changes in the video sequences. However, this approach is limited in fast-forward videos that often have large content changes and also might exhibit changes in scene illumination that should be preserved. In this work, we propose a novel photometric stabilization algorithm for fast-forward videos that is robust to large content-variation across frames. We compute pairwise color and tone transformations between neighboring frames and smooth these pair-wise transformations while taking in account the possibility of scene/content variations. This allows us to eliminate high-frequency fluctuations, while still adapting to real variations in scene characteristics. We evaluate our technique on a new dataset consisting of controlled synthetic and real videos, and demonstrate that our techniques outperforms the state-of-the-art.
Detection of social signals for recognizing engagement in human-robot interaction
Venue: arXiv cs.HC 2017
Year: 2017

Detection of engagement during a conversation is an important function of human-robot interaction. The level of user engagement can influence the dialogue strategy of the robot. Our motivation in this work is to detect several behaviors which will be used as social signal inputs for a real-time engagement recognition model. These behaviors are nodding, laughter, verbal backchannels and eye gaze. We describe models of these behaviors which have been learned from a large corpus of human-robot interactions with the android robot ERICA. Input data to the models comes from a Kinect sensor and a microphone array. Using our engagement recognition model, we can achieve reasonable performance using the inputs from automatic social signal detection, compared to using manual annotation as input.