OpenRaiser/Intern-Atlas huggingface dataset:
Transforming Question Answering Datasets Into Natural Language Inference Datasets
Venue: arXiv cs.CL 2018
Year: 2018

Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real
Venue: arXiv cs.AI 2018
Year: 2018

Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making. Although there exist a lot of works dealing with modeling driver behavior of a single object, it remains a challenge to make predictions for multiple highly interactive agents that react to each other simultaneously. In this work, we propose a generic probabilistic hierarchical recognition and prediction framework which employs a two-layer Hidden Markov Model (TLHMM) to obtain the distribution of potential situations and a learning-based dynamic scene evolution model to sample a group of future trajectories. Instead of predicting motions of a single entity, we propose to get the joint distribution by modeling multiple interactive agents as a whole system. Moreover, due to the decoupling property of the layered structure, our model is suitable for knowledge transfer from simulation to real world applications as well as among different traffic scenarios, which can reduce the computational efforts of training and the demand for a large data amount. A case study of highway ramp merging scenario is demonstrated to verify the effectiveness and accuracy of the proposed framework.
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accuracy on the Person in Context (PIC) dataset. We propose to group object labels with similar patterns of relationship distribution in the dataset into fewer categories. Label clustering not only mitigates both the large classification space and class imbalance issues, but also potentially increases data samples for each clustered category. We further propose to incorporate depth information as an additional feature into the instance segmentation model. The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver. We have rigorously evaluated the proposed techniques and performed various ablation analysis to validate the benefits of them.
Simultaneous Localization and Mapping (SLAM) using RTAB-MAP
Venue: arXiv cs.RO 2018
Year: 2018

This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.
A novel method of speech information hiding based on 3D-Magic Matrix
Venue: arXiv cs.CR 2018
Year: 2018

Redundant information of low-bit-rate speech is extremely small, thus it's very difficult to implement large capacity steganography on the low-bit-rate speech. Based on multiple vector quantization characteristics of the Line Spectrum Pair (LSP) of the speech codec, this paper proposes a steganography scheme using a 3D-Magic matrix to enlarge capacity and improve quality of speech. A cyclically moving algorithm to construct a 3D-Magic matrix for steganography is proposed in this paper, as well as an embedding and an extracting algorithm of steganography based on the 3D-Magic matrix in low-bit-rate speech codec. Theoretical analysis is provided to demonstrate that the concealment and the hidden capacity are greatly improved with the proposed scheme. Experimental results show the hidden capacity is raised to 200bps in ITU-T G.723.1 codec. Moreover, the quality of steganography speech in Perceptual Evaluation of Speech Quality (PESQ) reduces no more than 4%, indicating a little impact on the quality of speech. In addition, the proposed hidden scheme could prevent being detected by some steganalysis tools effectively.
Fingertip Detection and Tracking for Recognition of Air-Writing in Videos
Venue: arXiv cs.CV 2018
Year: 2018

Air-writing is the process of writing characters or words in free space using finger or hand movements without the aid of any hand-held device. In this work, we address the problem of mid-air finger writing using web-cam video as input. In spite of recent advances in object detection and tracking, accurate and robust detection and tracking of the fingertip remains a challenging task, primarily due to small dimension of the fingertip. Moreover, the initialization and termination of mid-air finger writing is also challenging due to the absence of any standard delimiting criterion. To solve these problems, we propose a new writing hand pose detection algorithm for initialization of air-writing using the Faster R-CNN framework for accurate hand detection followed by hand segmentation and finally counting the number of raised fingers based on geometrical properties of the hand. Further, we propose a robust fingertip detection and tracking approach using a new signature function called distance-weighted curvature entropy. Finally, a fingertip velocity-based termination criterion is used as a delimiter to mark the completion of the air-writing gesture. Experiments show the superiority of the proposed fingertip detection and tracking algorithm over state-of-the-art approaches giving a mean precision of 73.1 % while achieving real-time performance at 18.5 fps, a condition which is of vital importance to air-writing. Character recognition experiments give a mean accuracy of 96.11 % using the proposed air-writing system, a result which is comparable to that of existing handwritten character recognition systems.
Stochastic Gradient Descent Learns State Equations with Nonlinear Activations
Venue: arXiv cs.LG 2018
Year: 2018

We study discrete time dynamical systems governed by the state equation $h_{t+1}=ϕ(Ah_t+Bu_t)$. Here $A,B$ are weight matrices, $ϕ$ is an activation function, and $u_t$ is the input data. This relation is the backbone of recurrent neural networks (e.g. LSTMs) which have broad applications in sequential learning tasks. We utilize stochastic gradient descent to learn the weight matrices from a finite input/state trajectory $(u_t,h_t)_{t=0}^N$. We prove that SGD estimate linearly converges to the ground truth weights while using near-optimal sample size. Our results apply to increasing activations whose derivatives are bounded away from zero. The analysis is based on i) a novel SGD convergence result with nonlinear activations and ii) careful statistical characterization of the state vector. Numerical experiments verify the fast convergence of SGD on ReLU and leaky ReLU in consistence with our theory.
A Public-Key Cryptosystem from Interleaved Goppa Codes
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, a code-based public-key cryptosystem based on interleaved Goppa codes is presented. The scheme is based on encrypting several ciphertexts with the same Goppa code and adding a burst error to them. Possible attacks are outlined and the key size of several choices of parameters is compared to those of known schemes for the same security level. For example, for security level 128 bits, we obtain a key size of 696 Kbits whereas the classical McEliece scheme based on Goppa codes using list decoding requires a key size of 1935 Kbits.
Parameterized Games and Parameterized Automata
Venue: arXiv cs.GT 2018
Year: 2018

We introduce a way to parameterize automata and games on finite graphs with natural numbers. The parameters are accessed essentially by allowing counting down from the parameter value to 0 and branching depending on whether 0 has been reached. The main technical result is that in games, a player can win for some values of the parameters at all, if she can win for some values below an exponential bound. For many winning conditions, this implies decidability of any statements about a player being able to win with arbitrary quantification over the parameter values. While the result seems broadly applicable, a specific motivation comes from the study of chains of strategies in games. Chains of games were recently suggested as a means to define a rationality notion based on dominance that works well with quantitative games by Bassett, Jecker, P., Raskin and Van den Boogard. From the main result of this paper, we obtain generalizations of their decidability results with much simpler proofs. As both a core technical notion in the proof of the main result, and as a notion of potential independent interest, we look at boolean functions defined via graph game forms. Graph game forms have properties akin to monotone circuits, albeit are more concise. We raise some open questions regarding how concise they are exactly, which have a flavour similar to circuit complexity. Answers to these questions could improve the bounds in the main theorem.
On Finding a First-Order Sentence Consistent with a Sample of Strings
Venue: arXiv cs.LO 2018
Year: 2018

We investigate the following problem: given a sample of classified strings, find a first-order sentence of minimal quantifier rank that is consistent with the sample. We represent strings as successor string structures, that is, finite structures with unary predicates to denote symbols in an alphabet, and a successor relation. We use results of the Ehrenfeucht-Fraïssé game over successor string structures in order to design an algorithm to find such sentence. We use conditions characterizing the winning strategies for the Spoiler on successor strings structures in order to define formulas which distinguish two strings. Our algorithm returns a boolean combination of such formulas.
Is Leakage Power a Linear Function of Temperature?
Venue: arXiv cs.AR 2018
Year: 2018

In this work, we present a study of the leakage power modeling techniques commonly used in the architecture community. We further provide an analysis of the error in leakage power estimation using the various modeling techniques. We strongly believe that this study will help researchers determine an appropriate leakage model to use in their work, based on the desired modeling accuracy and speed.
A case study in formal verification of a Java program
Venue: arXiv cs.LO 2018
Year: 2018

We describe a successful attempt to formally verify a simple genetic algorithm written in Java. To this end, we compare several formal verification tools designed for Java, and select Krakatoa as the most appropriate for the task. Based on our experience, we present several suggestions for making the tools more user friendly, which we hope will lead to wider adoption of formal methods. In particular, we discuss at length how useful it would be for provers to perform some form of abduction, that is, for them to guess which extra assumptions they need to prove a statement. It is our opinion that progress in this area would produce the largest improvement in the usability of formal verification tools.
Two-Dimensional Control and Assurance of Data Integrity in Information Systems Based on Residue Number System Codes and Cryptographic Hash Functions
Venue: arXiv cs.CR 2018
Year: 2018

The method of two-dimensional control and assurance of data integrity with the possibility of their recovery for information systems operating under conditions of random errors as well as errors generated through deliberate actions of the attacker is proposed. The data recovery procedure is based on the application of the mathematical apparatus of redundant residue number system codes, and the control (verification of the recovered data validity (reliability, accuracy)) of data integrity is performed by means of cryptographic methods.
Automated Test Generation to Detect Individual Discrimination in AI Models
Venue: arXiv cs.AI 2018
Year: 2018

Dependability on AI models is of utmost importance to ensure full acceptance of the AI systems. One of the key aspects of the dependable AI system is to ensure that all its decisions are fair and not biased towards any individual. In this paper, we address the problem of detecting whether a model has an individual discrimination. Such a discrimination exists when two individuals who differ only in the values of their protected attributes (such as, gender/race) while the values of their non-protected ones are exactly the same, get different decisions. Measuring individual discrimination requires an exhaustive testing, which is infeasible for a non-trivial system. In this paper, we present an automated technique to generate test inputs, which is geared towards finding individual discrimination. Our technique combines the well-known technique called symbolic execution along with the local explainability for generation of effective test cases. Our experimental results clearly demonstrate that our technique produces 3.72 times more successful test cases than the existing state-of-the-art across all our chosen benchmarks.
The Skiplist-Based LSM Tree
Venue: arXiv cs.DB 2018
Year: 2018

Log-Structured Merge (LSM) Trees provide a tiered data storage and retrieval paradigm that is attractive for write-optimized data systems. Maintaining an efficient buffer in memory and deferring updates past their initial write-time, the structure provides quick operations over hot data. Because each layer of the structure is logically separate from the others, the structure is also conducive to opportunistic and granular optimization. In this paper, we introduce the Skiplist-Based LSM Tree (sLSM), a novel system in which the memory buffer of the LSM is composed of a sequence of skiplists. We develop theoretical and experimental results that demonstrate that the breadth of tuning parameters inherent to the sLSM allows it broad flexibility for excellent performance across a wide variety of workloads.
Model Theory of Monadic Predicate Logic with the Infinity Quantifier
Venue: arXiv cs.LO 2018
Year: 2018

This paper establishes model-theoretic properties of $\mathrm{FOE}^{\infty}$, a variation of monadic first-order logic that features the generalised quantifier $\exists^\infty$ (`there are infinitely many'). We provide syntactically defined fragments of $\mathrm{FOE}^{\infty}$ characterising four different semantic properties of $\mathrm{FOE}^{\infty}$-sentences: (1) being monotone and (2) (Scott) continuous in a given set of monadic predicates; (3) having truth preserved under taking submodels or (4) invariant under taking quotients. In each case, we produce an effectively defined map that translates an arbitrary sentence $φ$ to a sentence $φ^{p}$ belonging to the corresponding syntactic fragment, with the property that $φ$ is equivalent to $φ^{p}$ precisely when it has the associated semantic property. Our methodology is first to provide these results in the simpler setting of monadic first-order logic with ($\mathrm{FOE}$) and without ($\mathrm{FO}$) equality, and then move to $\mathrm{FOE}^{\infty}$ by including the generalised quantifier $\exists^\infty$ into the picture. As a corollary of our developments, we obtain that the four semantic properties above are decidable for $\mathrm{FOE}^{\infty}$-sentences. Moreover, our results are directly relevant to the characterisation of automata and expressiveness modulo bisimilirity for variants of monadic second-order logic. This application is developed in a companion paper.
Multilingual Extractive Reading Comprehension by Runtime Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduce the first RC system for languages without RC training data. Given a target language without RC training data and a pivot language with RC training data (e.g. English), our method leverages existing RC resources in the pivot language by combining a competitive RC model in the pivot language with an attentive Neural Machine Translation (NMT) model. We first translate the data from the target to the pivot language, and then obtain an answer using the RC model in the pivot language. Finally, we recover the corresponding answer in the original language using soft-alignment attention scores from the NMT model. We create evaluation sets of RC data in two non-English languages, namely Japanese and French, to evaluate our method. Experimental results on these datasets show that our method significantly outperforms a back-translation baseline of a state-of-the-art product-level machine translation system.
Guiding the Creation of Deep Learning-based Object Detectors
Venue: arXiv cs.CV 2018
Year: 2018

Object detection is a computer vision field that has applications in several contexts ranging from biomedicine and agriculture to security. In the last years, several deep learning techniques have greatly improved object detection models. Among those techniques, we can highlight the YOLO approach, that allows the construction of accurate models that can be employed in real-time applications. However, as most deep learning techniques, YOLO has a steep learning curve and creating models using this approach might be challenging for non-expert users. In this work, we tackle this problem by constructing a suite of Jupyter notebooks that democratizes the construction of object detection models using YOLO. The suitability of our approach has been proven with a dataset of stomata images where we have achieved a mAP of 90.91%.
Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa
Venue: arXiv cs.LG 2018
Year: 2018

Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore different geographical feature representations in the context of predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model performance using "area between the curves" (ABC) to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, (c) whether a simple binary representation, or a richer, spectral analysis-elicited representation perform better, and (d) whether spectral analysis-based representations can be improved upon by leveraging geographically-descriptive features. In exploring (d), we devise a similarity-based spectral analysis procedure, which allows for the combination of geographically relational and geographically descriptive features. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark among all models. We also find that geographical features improve predictive performance, and that better performance is obtained using richer, spectral analysis-elicited features. Furthermore, we find that similarity-based spectral analysis-elicited representations improve upon the original spectral analysis results by approximately 40%.
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
Venue: arXiv cs.CV 2018
Year: 2018

Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for segmentation have to depend on pretrained optical flow models, leading to suboptimal solutions for the problem. End-to-end sequential learning to explore spatialtemporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation dataset only contains 90 short video clips. To solve this problem, we build a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS). Our dataset contains 4,453 YouTube video clips and 94 object categories. This is by far the largest video object segmentation dataset to our knowledge and has been released at http://youtube-vos.org. We further evaluate several existing state-of-the-art video object segmentation algorithms on this dataset which aims to establish baselines for the development of new algorithms in the future.
Assessing and Addressing Algorithmic Bias - But Before We Get There
Venue: arXiv cs.CY 2018
Year: 2018

Algorithmic and data bias are gaining attention as a pressing issue in popular press - and rightly so. However, beyond these calls to action, standard processes and tools for practitioners do not readily exist to assess and address unfair algorithmic and data biases. The literature is relatively scattered and the needed interdisciplinary approach means that very different communities are working on the topic. We here provide a number of challenges encountered in assessing and addressing algorithmic and data bias in practice. We describe an early approach that attempts to translate the literature into processes for (production) teams wanting to assess both intended data and algorithm characteristics and unintended, unfair biases.
Downlink Performance of Uplink Fractional Power Control in 5G Massive MIMO Systems
Venue: arXiv cs.NI 2018
Year: 2018

Uplink power control is an efficient scheme to mitigate pilot contamination in massive multiple-input multiple-output (MIMO) systems. In this work, we provide a comprehensive study on the effects of fractional power control (FPC) on the downlink performance of the most relevant fifth generation (5G) massive MIMO deployments. Specifically, we perform thorough system simulations based on the most recent three dimensional spatial channel model released by the 3rd Generation Partnership Project to evaluate the impact of different deployment-related parameters such as pilot reuse factor, beamforming criterion, and base station array size. Our results indicate the most suitable tuning of the FPC parameters and show that optimized FPC provides huge gains in the cell border throughput when compared to a baseline scheme with all the users transmitting at maximum power. Moreover, our simulations also demonstrate that the effectiveness of FPC grows in scenarios with severe pilot contamination, confirming that implementing this feature is essential in realistic deployments.