OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Device Selection and Access PolicyGeneration based on User Preference for IoTActivity Workflow
Venue: arXiv cs.CR 2019
Year: 2019

The emerging Internet of Things (IoT) has lead to a dramatic increase in type, quantity, and the number of functions that can be offered in a smart environment. Future smart environments will be even richer in terms of the number of devices and functionality provided by them. This poses two major challenges a) an end user has to search through a vast number of IoT devices to identify the suitable devices that satisfy their preferences, and b) it is extremely difficult for users to manually define fine-grained security policies to support workflows involving multiple functions. This paper introduces an intelligent new approach to overcome these challenges by a) enabling users to describe their required functionalities in the form of activity workflow, b) automatically selecting a group of devices to satisfy users functional requirements and maximise their preferences over device usage, c) systematically generating local network access control policies to ensure enforce the principle of least privilege. We study different heuristic search algorithms to find the preferred devices for a given workflow. Experiments results show that the Genetic Algorithm is the best, among the algorithms that we test, as it offers a balance between efficiency and effectiveness.
Online Bin Covering with Limited Migration
Venue: arXiv cs.DS 2019
Year: 2019

Semi-online models where decisions may be revoked in a limited way have been studied extensively in the last years. This is motivated by the fact that the pure online model is often too restrictive to model real-world applications, where some changes might be allowed. A well-studied measure of the amount of decisions that can be revoked is the migration factor $β$: When an object $o$ of size $s(o)$ arrives, the decisions for objects of total size at most $β\cdot s(o)$ may be revoked. Usually $β$ should be a constant. This means that a small object only leads to small changes. This measure has been successfully investigated for different, classic problems such as bin packing or makespan minimization. The dual of makespan minimization - the Santa Claus or machine covering problem - has also been studied, whereas the dual of bin packing - the bin covering problem - has not been looked at from such a perspective. In this work, we extensively study the bin covering problem with migration in different scenarios. We develop algorithms both for the static case - where only insertions are allowed - and for the dynamic case, where items may also depart. We also develop lower bounds for these scenarios both for amortized migration and for worst-case migration showing that our algorithms have nearly optimal migration factor and asymptotic competitive ratio (up to an arbitrary small $\eps$). We therefore resolve the competitiveness of the bin covering problem with migration.
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
Venue: arXiv cs.LG 2019
Year: 2019

Principal Component Analysis (PCA) is a popular tool for dimensionality reduction and feature extraction in data analysis. There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA). However, standard PCA and PPCA are not robust, as they are sensitive to outliers. To alleviate this problem, this paper introduces the Self-Paced Learning mechanism into PPCA, and proposes a novel method called Self-Paced Probabilistic Principal Component Analysis (SP-PPCA). Furthermore, we design the corresponding optimization algorithm based on the alternative search strategy and the expectation-maximization algorithm. SP-PPCA looks for optimal projection vectors and filters out outliers iteratively. Experiments on both synthetic problems and real-world datasets clearly demonstrate that SP-PPCA is able to reduce or eliminate the impact of outliers.
Robust Network Design for Software-Defined IP/Optical Backbones
Venue: arXiv cs.NI 2019
Year: 2019

Recently, Internet service providers (ISPs) have gained increased flexibility in how they configure their in-ground optical fiber into an IP network. This greater control has been made possible by (i) the maturation of software defined networking (SDN), and (ii) improvements in optical switching technology. Whereas traditionally, at network design time, each IP link was assigned a fixed optical path and bandwidth, modern colorless and directionless Reconfigurable Optical Add/Drop Multiplexers (CD ROADMs) allow a remote SDN controller to remap the IP topology to the optical underlay on the fly. Consequently, ISPs face new opportunities and challenges in the design and operation of their backbone networks. Specifically, ISPs must determine how best to design their networks to take advantage of the new capabilities; they need an automated way to generate the least expensive network design that still delivers all offered traffic, even in the presence of equipment failures. This problem is difficult because of the physical constraints governing the placement of optical regenerators, a piece of optical equipment necessary for maintaining an optical signal over long stretches of fiber. As a solution, we present an integer linear program (ILP) which (1) solves the equipment-placement network design problem; (2) determines the optimal mapping of IP links to the optical infrastructure for any given failure scenario; and (3) determines how best to route the offered traffic over the IP topology. To scale to larger networks, we also describe an efficient heuristic that finds nearly optimal network designs in a fraction of the time. Further, in our experiments our ILP offers cost savings of up to 29% compared to traditional network design techniques.
Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay
Venue: arXiv cs.CR 2019
Year: 2019

Fake audio detection is expected to become an important research area in the field of smart speakers such as Google Home, Amazon Echo and chatbots developed for these platforms. This paper presents replay attack vulnerability of voice-driven interfaces and proposes a countermeasure to detect replay attack on these platforms. This paper presents a novel framework to model replay attack distortion, and then use a non-learning-based method for replay attack detection on smart speakers. The reply attack distortion is modeled as a higher-order nonlinearity in the replay attack audio. Higher-order spectral analysis (HOSA) is used to capture characteristics distortions in the replay audio. Effectiveness of the proposed countermeasure scheme is evaluated on original speech as well as corresponding replayed recordings. The replay attack recordings are successfully injected into the Google Home device via Amazon Alexa using the drop-in conferencing feature.
Minimal Separators in Graphs
Venue: arXiv cs.DM 2019
Year: 2019

The Known Menger's theorem states that in a finite graph, the size of a minimum separator set of any pair of vertices is equal to the maximum number of disjoint paths that can be found between these two vertices. In this paper, we study the minimal separators of two non-adjacent vertices in a finite graph, and we give a new elementary proof of Menger's theorem.
Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks
Venue: arXiv cs.SY 2019
Year: 2019

Load forecasting plays a critical role in the operation and planning of power systems. By using input features such as historical loads and weather forecasts, system operators and utilities build forecast models to guide decision making in commitment and dispatch. As the forecasting techniques becomes more sophisticated, however, they also become more vulnerable to cybersecurity threats. In this paper, we study the vulnerability of a class of load forecasting algorithms and analyze the potential impact on the power system operations, such as load shedding and increased dispatch costs. Specifically, we propose data injection attack algorithms that require minimal assumptions on the ability of the adversary. The attacker does not need to have knowledge about the load forecasting model or the underlying power system. Surprisingly, our results indicate that standard load forecasting algorithms are quite vulnerable to the designed black-box attacks. By only injecting malicious data in temperature from online weather forecast APIs, an attacker could manipulate load forecasts in arbitrary directions and cause significant and targeted damages to system operations.
A Multi-Agent Deep Reinforcement Learning based Spectrum Allocation Framework for D2D Communications
Venue: arXiv cs.NI 2019
Year: 2019

Device-to-device (D2D) communication has been recognized as a promising technique to improve spectrum efficiency. However, D2D transmission as an underlay causes severe interference, which imposes a technical challenge to spectrum allocation. Existing centralized schemes require global information, which can cause serious signaling overhead. While existing distributed solution requires frequent information exchange between users and cannot achieve global optimization. In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named Neighbor-Agent Actor Critic (NAAC). NAAC uses neighbor users' historical information for centralized training but is executed distributedly without that information, which not only has no signal interaction during execution, but also utilizes cooperation between users to further optimize system performance. The simulation results show that the proposed framework can effectively reduce the outage probability of cellular links, improve the sum rate of D2D links and have good convergence.
Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
Venue: arXiv cs.CV 2019
Year: 2019

Despite that the performance of image-to-image translation has been significantly improved by recent progress in generative models, current methods still suffer from severe degradation in training stability and sample quality when applied to the high-resolution situation. In this work, we present a novel training framework for GANs, namely biphasic learning, to achieve image-to-image translation in multiple visual domains at $1024^2$ resolution. Our core idea is to design an adjustable objective function that varies across training phases. Within the biphasic learning framework, we propose a novel inherited adversarial loss to achieve the enhancement of model capacity and stabilize the training phase transition. Furthermore, we introduce a perceptual-level consistency loss through mutual information estimation and maximization. To verify the superiority of the proposed method, we apply it to a wide range of face-related synthesis tasks and conduct experiments on multiple large-scale datasets. Through comprehensive quantitative analyses, we demonstrate that our method significantly outperforms existing methods.
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

Recently, a simple combination of passage retrieval using off-the-shelf IR techniques and a BERT reader was found to be very effective for question answering directly on Wikipedia, yielding a large improvement over the previous state of the art on a standard benchmark dataset. In this paper, we present a data augmentation technique using distant supervision that exploits positive as well as negative examples. We apply a stage-wise approach to fine tuning BERT on multiple datasets, starting with data that is "furthest" from the test data and ending with the "closest". Experimental results show large gains in effectiveness over previous approaches on English QA datasets, and we establish new baselines on two recent Chinese QA datasets.
Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms
Venue: arXiv cs.CY 2019
Year: 2019

Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find, stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly-rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.
Universal Bounding Box Regression and Its Applications
Venue: arXiv cs.CV 2019
Year: 2019

Bounding-box regression is a popular technique to refine or predict localization boxes in recent object detection approaches. Typically, bounding-box regressors are trained to regress from either region proposals or fixed anchor boxes to nearby bounding boxes of a pre-defined target object classes. This paper investigates whether the technique is generalizable to unseen classes and is transferable to other tasks beyond supervised object detection. To this end, we propose a class-agnostic and anchor-free box regressor, dubbed Universal Bounding-Box Regressor (UBBR), which predicts a bounding box of the nearest object from any given box. Trained on a relatively small set of annotated images, UBBR successfully generalizes to unseen classes, and can be used to improve localization in many vision problems. We demonstrate its effectivenss on weakly supervised object detection and object discovery.
The Bang Calculus and the Two Girard's Translations
Venue: arXiv cs.LO 2019
Year: 2019

We study the two Girard's translations of intuitionistic implication into linear logic by exploiting the bang calculus, a paradigmatic functional language with an explicit box-operator that allows both call-by-name and call-by-value lambda-calculi to be encoded in. We investigate how the bang calculus subsumes both call-by-name and call-by-value lambda-calculi from a syntactic and a semantic viewpoint.
From Linear Logic to Cyclic Sharing
Venue: arXiv cs.LO 2019
Year: 2019

We present a translation from Multiplicative Exponential Linear Logic to a simply-typed lambda calculus with cyclic sharing. This translation is derived from a simple observation on the Int-construction on traced monoidal categories. It turns out that the translation is a mixture of the call-by-name CPS translation and the Geometry of Interaction-based interpretation.
Towards Realistic Battery-DoS Protection of Implantable Medical Devices
Venue: arXiv cs.CR 2019
Year: 2019

Modern Implantable Medical Devices (IMDs) feature wireless connectivity, which makes them vulnerable to security attacks. Particular to IMDs is the battery Denial-of-Service attack whereby attackers aim to fully deplete the battery by occupying the IMD with continuous authentication requests. Zero-Power Defense (ZPD) based on energy harvesting is known to be an excellent protection against these attacks. This paper establishes essential design specifications for employing ZPD techniques in IMDs, offers a critical review of ZPD techniques found in literature and, subsequently, gives crucial recommendations for developing comprehensive ZPD solutions.
Model Comparison Games for Horn Description Logics
Venue: arXiv cs.LO 2019
Year: 2019

Horn description logics are syntactically defined fragments of standard description logics that fall within the Horn fragment of first-order logic and for which ontology-mediated query answering is in PTime for data complexity. They were independently introduced in modal logic to capture the intersection of Horn first-order logic with modal logic. In this paper, we introduce model comparison games for the basic Horn description logic hornALC (corresponding to the basic Horn modal logic) and use them to obtain an Ehrenfeucht-Fraïssé type definability result and a van Benthem style expressive completeness result for hornALC. We also establish a finite model theory version of the latter. The Ehrenfeucht-Fraïssé type definability result is used to show that checking hornALC indistinguishability of models is ExpTime-complete, which is in sharp contrast to ALC indistinguishability (i.e., bisimulation equivalence) checkable in PTime. In addition, we explore the behavior of Horn fragments of more expressive description and modal logics by defining a Horn guarded fragment of first-order logic and introducing model comparison games for it.
New techniques for fault-tolerant decomposition of Multi-Controlled Toffoli gate
Venue: arXiv cs.ET 2019
Year: 2019

Physical implementation of scalable quantum architectures faces an immense challenge in form of fragile quantum states. To overcome it, quantum architectures with fault tolerance is desirable. This is achieved currently by using surface code along with a transversal gate set. This dictates the need for decomposition of universal Multi Control Toffoli~(MCT) gates using a transversal gate set. Additionally, the transversal non-Clifford phase gate incurs high latency which makes it an important factor to consider during decomposition.Besides, the decomposition of large Multi-control Toffoli~(MCT) gate without ancilla presents an additional hurdle. In this manuscript, we address both of these issues by introducing Clifford+$Z_N$ gate library. We present an ancilla free decomposition of MCT gates with linear phase depth and quadratic phase count. Furthermore, we provide a technique for decomposition of MCT gates in unit phase depth using the Clifford+$Z_N$ library, albeit at the cost of ancillary lines and quadratic phase count.
Semantic Source Code Models Using Identifier Embeddings
Venue: arXiv cs.SE 2019
Year: 2019

The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities. As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data-driven methods. In this context, the rich semantics hidden in source code identifiers provide opportunities for building semantic representations of code which can assist tasks of code search and reuse. To this end, we deliver in the form of pretrained vector space models, distributed code representations for six popular programming languages, namely, Java, Python, PHP, C, C++, and C#. The models are produced using fastText, a state-of-the-art library for learning word representations. Each model is trained on data from a single programming language; the code mined for producing all models amounts to over 13.000 repositories. We indicate dissimilarities between natural language and source code, as well as variations in coding conventions in between the different programming languages we processed. We describe how these heterogeneities guided the data preprocessing decisions we took and the selection of the training parameters in the released models. Finally, we propose potential applications of the models and discuss limitations of the models.
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Venue: arXiv cs.LG 2019
Year: 2019

Recently, deep models have been successfully applied in several applications, especially with low-level representations. However, sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models. Column Networks, a deep architecture, can succinctly capture such domain structure and interactions, but may still be prone to sub-optimal learning from sparse and noisy samples. Inspired by the success of human-advice guided learning in AI, especially in data-scarce domains, we propose Knowledge-augmented Column Networks that leverage human advice/knowledge for better learning with noisy/sparse samples. Our experiments demonstrate that our approach leads to either superior overall performance or faster convergence (i.e., both effective and efficient).
A Fast Dictionary Learning Method for Coupled Feature Space Learning
Venue: arXiv cs.LG 2019
Year: 2019

In this letter, we propose a novel computationally efficient coupled dictionary learning method that enforces pairwise correlation between the atoms of dictionaries learned to represent the underlying feature spaces of two different representations of the same signals, e.g., representations in different modalities or representations of the same signals measured with different qualities. The jointly learned correlated feature spaces represented by coupled dictionaries are used in sparse representation based classification, recognition and reconstruction tasks. The presented experimental results show that the proposed coupled dictionary learning method has a significantly lower computational cost. Moreover, the visual presentation of jointly learned dictionaries shows that the pairwise correlations between the corresponding atoms are ensured.
The Efficiency Threshold for the Offspring Population Size of the ($μ$, $λ$) EA
Venue: arXiv cs.NE 2019
Year: 2019

Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation. In this work, we make progress in understanding the interplay between parent and offspring population size of the $(μ,λ)$ EA. Previous works, roughly speaking, indicate that for $λ\ge (1+\varepsilon) e μ$, this EA easily optimizes the OneMax function, whereas an offspring population size $λ\le (1 -\varepsilon) e μ$ leads to an exponential runtime. Motivated also by the observation that in the efficient regime the $(μ,λ)$ EA loses its ability to escape local optima, we take a closer look into this phase transition. Among other results, we show that when $μ\le n^{1/2 - c}$ for any constant $c > 0$, then for any $λ\le e μ$ we have a super-polynomial runtime. However, if $μ\ge n^{2/3 + c}$, then for any $λ\ge e μ$, the runtime is polynomial. For the latter result we observe that the $(μ,λ)$ EA profits from better individuals also because these, by creating slightly worse offspring, stabilize slightly sub-optimal sub-populations. While these first results close to the phase transition do not yet give a complete picture, they indicate that the boundary between efficient and super-polynomial is not just the line $λ= e μ$, and that the reasons for efficiency or not are more complex than what was known so far.
Synthesising 3D Facial Motion from "In-the-Wild" Speech
Venue: arXiv cs.CV 2019
Year: 2019

Synthesising 3D facial motion from speech is a crucial problem manifesting in a multitude of applications such as computer games and movies. Recently proposed methods tackle this problem in controlled conditions of speech. In this paper, we introduce the first methodology for 3D facial motion synthesis from speech captured in arbitrary recording conditions ("in-the-wild") and independent of the speaker. For our purposes, we captured 4D sequences of people uttering 500 words, contained in the Lip Reading Words (LRW) a publicly available large-scale in-the-wild dataset, and built a set of 3D blendshapes appropriate for speech. We correlate the 3D shape parameters of the speech blendshapes to the LRW audio samples by means of a novel time-warping technique, named Deep Canonical Attentional Warping (DCAW), that can simultaneously learn hierarchical non-linear representations and a warping path in an end-to-end manner. We thoroughly evaluate our proposed methods, and show the ability of a deep learning model to synthesise 3D facial motion in handling different speakers and continuous speech signals in uncontrolled conditions.