OpenRaiser/Intern-Atlas huggingface dataset:
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers
Venue: arXiv cs.LG 2020
Year: 2020

Transfer learning --- transferring learned knowledge --- has brought a paradigm shift in the way models are trained. The lucrative benefits of improved accuracy and reduced training time have shown promise in training models with constrained computational resources and fewer training samples. Specifically, publicly available text-based models such as GloVe and BERT that are trained on large corpus of datasets have seen ubiquitous adoption in practice. In this paper, we ask, "can transfer learning in text prediction models be exploited to perform misclassification attacks?" As our main contribution, we present novel attack techniques that utilize unintended features learnt in the teacher (public) model to generate adversarial examples for student (downstream) models. To the best of our knowledge, ours is the first work to show that transfer learning from state-of-the-art word-based and sentence-based teacher models increase the susceptibility of student models to misclassification attacks. First, we propose a novel word-score based attack algorithm for generating adversarial examples against student models trained using context-free word-level embedding model. On binary classification tasks trained using the GloVe teacher model, we achieve an average attack accuracy of 97% for the IMDB Movie Reviews and 80% for the Fake News Detection. For multi-class tasks, we divide the Newsgroup dataset into 6 and 20 classes and achieve an average attack accuracy of 75% and 41% respectively. Next, we present length-based and sentence-based misclassification attacks for the Fake News Detection task trained using a context-aware BERT model and achieve 78% and 39% attack accuracy respectively. Thus, our results motivate the need for designing training techniques that are robust to unintended feature learning, specifically for transfer learned models.
Table Structure Extraction with Bi-directional Gated Recurrent Unit Networks
Venue: arXiv cs.CV 2020
Year: 2020

Tables present summarized and structured information to the reader, which makes table structure extraction an important part of document understanding applications. However, table structure identification is a hard problem not only because of the large variation in the table layouts and styles, but also owing to the variations in the page layouts and the noise contamination levels. A lot of research has been done to identify table structure, most of which is based on applying heuristics with the aid of optical character recognition (OCR) to hand pick layout features of the tables. These methods fail to generalize well because of the variations in the table layouts and the errors generated by OCR. In this paper, we have proposed a robust deep learning based approach to extract rows and columns from a detected table in document images with a high precision. In the proposed solution, the table images are first pre-processed and then fed to a bi-directional Recurrent Neural Network with Gated Recurrent Units (GRU) followed by a fully-connected layer with soft max activation. The network scans the images from top-to-bottom as well as left-to-right and classifies each input as either a row-separator or a column-separator. We have benchmarked our system on publicly available UNLV as well as ICDAR 2013 datasets on which it outperformed the state-of-the-art table structure extraction systems by a significant margin.
HyGCN: A GCN Accelerator with Hybrid Architecture
Venue: arXiv cs.DC 2020
Year: 2020

In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority-based off-chip memory access coordination to improve off-chip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509$\times$ speedup with 2500$\times$ energy reduction and average 6.5$\times$ speedup with 10$\times$ energy reduction, respectively.
A Soft Recommender System for Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

Recent social recommender systems benefit from friendship graph to make an accurate recommendation, believing that friends in a social network have exactly the same interests and preferences. Some studies have benefited from hard clustering algorithms (such as K-means) to determine the similarity between users and consequently to define degree of friendships. In this paper, we went a step further to identify true friends for making even more realistic recommendations. we calculated the similarity between users, as well as the dependency between a user and an item. Our hypothesis is that due to the uncertainties in user preferences, the fuzzy clustering, instead of the classical hard clustering, is beneficial in accurate recommendations. We incorporated the C-means algorithm to get different membership degrees of soft users' clusters. Then, the users' similarity metric is defined according to the soft clusters. Later, in a training scheme we determined the latent representations of users and items, extracting from the huge and sparse user-item-tag matrix using matrix factorization. In the parameter tuning, we found the optimum coefficients for the influence of our soft social regularization and the user-item dependency terms. Our experimental results convinced that the proposed fuzzy similarity metric improves the recommendations in real data compared to the baseline social recommender system with the hard clustering.
A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets
Venue: arXiv cs.DC 2020
Year: 2020

Cloud Service Brokers (CSBs) facilitate complex resource allocation decisions, efficiently mapping dynamic tenant demands onto dynamic provider offers, where several objectives should ideally be considered. This work proposes for the first time a pure multi-objective formulation of a broker-oriented Virtual Machine Placement (VMP) problem for dynamic environments, simultaneously optimizing the following objective functions: (i) Total Infrastructure CPU (TICPU), (ii) Total Infrastructure Memory (TIMEM) and (iii) Total Infrastructure Price (TIP) while considering load balancing across providers. To solve the formulated multi-objective problem, a Multi-Objective Evolutionary Algorithm (MOEA) is proposed. Considering that each time a demand (or offer) change occurs, a set of non-dominated solutions is found by Pareto-based algorithms as the one proposed, different selection strategies were evaluated in order to automatically select a convenient solution. Additionally, the proposed algorithm, including the considered selection strategies, was compared against mono-objective state-of-the-art alternatives in different scenarios with real data from providers in actual markets. Experimental results demonstrate that a pure multi-objective optimization approach considering the preferred solution selection strategy (S3) outperformed other mono-objective evaluated alternatives.
Extracting Multiple Viewpoint Models from Relational Databases
Venue: arXiv cs.DB 2020
Year: 2020

Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in reallife processes often different case notions are intertwined. For example, events of the same order handling process may refer to customers, orders, order lines, deliveries, and payments. Therefore, we propose to use Multiple Viewpoint (MVP) models that relate events through objects and that relate activities through classes. The required event data are much closer to existing relational databases. MVP models provide a holistic view on the process, but also allow for the extraction of classical event logs using different viewpoints. This way existing process mining techniques can be used for each viewpoint without the need for new data extractions and transformations. We provide a toolchain allowing for the discovery of MVP models (annotated with performance and frequency information) from relational databases. Moreover, we demonstrate that classical process mining techniques can be applied to any selected viewpoint.
Watching the Weak Link into Your Home: An Inspection and Monitoring Toolkit for TR-069
Venue: arXiv cs.NI 2020
Year: 2020

TR-069 is a standard for the remote management of end-user devices by service providers. Despite being implemented in nearly a billion devices, almost no research has been published on the security and privacy aspects of TR-069. The first contribution of this paper is a study of the TR-069 ecosystem and techniques to inspect TR-069 communication. We find that the majority of analyzed providers do not use recommended security measures, such as TLS. Second, we present a TR-069 honeyclient to both analyze TR-069 behavior of providers and test configuration servers for security vulnerabilities. We find that popular open-source configuration servers use insecure methods to authenticate clients. TR-069 implementations based on these servers expose, for instance, their users' internet telephony credentials. Third, we develop components for a distributed system to continuously monitor activities in providers' TR-069 deployments. Our setup consists of inexpensive hardware sensors deployed on customer premises and centralized log collectors. We perform real-world measurements and find that the purported security benefits of TR-069 are not realized as providers' firmware update processes are lacking.
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Venue: arXiv cs.LG 2020
Year: 2020

Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial for understanding disease pathology and predicting prognoses. To this end, we develop deep diffusion processes (DDP) to model "dynamic comorbidity networks", i.e., the temporal relationships between comorbid disease onsets expressed through a dynamic graph. A DDP comprises events modelled as a multi-dimensional point process, with an intensity function parameterized by the edges of a dynamic weighted graph. The graph structure is modulated by a neural network that maps patient history to edge weights, enabling rich temporal representations for disease trajectories. The DDP parameters decouple into clinically meaningful components, which enables serving the dual purpose of accurate risk prediction and intelligible representation of disease pathology. We illustrate these features in experiments using cancer registry data.
Adaptive Coding for Two-Way Lossy Source-Channel Communication
Venue: arXiv cs.IT 2020
Year: 2020

An adaptive joint source-channel coding (JSCC) scheme is presented for transmitting correlated sources over discrete-memoryless two-way channels subject to distortion constraints. The proposed JSCC scheme makes use of the previously transmitted and received channel signals as well as the sources' correlation to facilitate coordination between terminals. It is shown that the adaptive scheme strictly subsumes prior lossy coding methods for two-way simultaneous transmission and yields a new adaptive separate source-channel coding result. Two examples are given to show the scheme's advantages.
Digesting the Elephant -- Experiences with Interactive Production Quality Path Tracing of the Moana Island Scene
Venue: arXiv cs.GR 2020
Year: 2020

New algorithmic and hardware developments over the past two decades have enabled interactive ray tracing of small to modest sized scenes, and are finding growing popularity in scientific visualization and games. However, interactive ray tracing has not been as widely explored in the context of production film rendering, where challenges due to the complexity of the models and, from a practical standpoint, their unavailability to the wider research community, have posed significant challenges. The recent release of the Disney Moana Island Scene has made one such model available to the community for experimentation. In this paper, we detail the challenges posed by this scene to an interactive ray tracer, and the solutions we have employed and developed to enable interactive path tracing of the scene with full geometric and shading detail, with the goal of providing insight and guidance to other researchers.
Computing Curved Area Labels in Near-Real Time
Venue: arXiv cs.HC 2020
Year: 2020

In the Area Labeling Problem one is after placing the label of a geographic area. Given the outer boundary of the area and an optional set of holes. The goal is to find a label position such that the label spans the area and is conform to its shape. The most recent research in this field from Barrault in 2001 proposes an algorithm to compute label placements based on curved support lines. His solution has some drawbacks as he is evaluating many very similar solutions. Furthermore he needs to restrict the search space due to performance issues and therefore might miss interesting solutions. We propose a solution that evaluates the search space more broadly and much more efficient. To achieve this we compute a skeleton of the polygon. The skeleton is pruned such that edges close to the boundary polygon are removed. In the so pruned skeleton we choose a set of candidate paths to be longest distinct subpaths of the graph. Based on these candidates the label support lines are computed and the label positions evaluated. Keywords: Area lettering \and Automated label placement \and Digital cartography \and Geographic information sciences \and Geometric Optimization.
Killing Stubborn Mutants with Symbolic Execution
Venue: arXiv cs.SE 2020
Year: 2020

We introduce SeMu, a Dynamic Symbolic Execution technique that generates test inputs capable of killing stubborn mutants (killable mutants that remain undetected after a reasonable amount of testing). SeMu aims at mutant propagation (triggering erroneous states to the program output) by incrementally searching for divergent program behaviours between the original and the mutant versions. We model the mutant killing problem as a symbolic execution search within a specific area in the programs' symbolic tree. In this framework, the search area is defined and controlled by parameters that allow scalable and cost-effective mutant killing. We integrate SeMu in KLEE and experimented with Coreutils (a benchmark frequently used in symbolic execution studies). Our results show that our modelling plays an important role in mutant killing. Perhaps more importantly, our results also show that, within a two-hour time limit, SeMu kills 37% of the stubborn mutants, where KLEE kills none and where the mutant infection strategy (strategy suggested by previous research) kills 17%.
The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms
Venue: arXiv cs.HC 2020
Year: 2020

Recently, the HCI community has seen increased interest in the design of teaching augmentation (TA): tools that extend and complement teachers' pedagogical abilities during ongoing classroom activities. Examples of TA systems are emerging across multiple disciplines, taking various forms: e.g., ambient displays, wearables, or learning analytics dashboards. However, these diverse examples have not been analyzed together to derive more fundamental insights into the design of teaching augmentation. Addressing this opportunity, we broadly synthesize existing cases to propose the TA framework. Our framework specifies a rich design space in five dimensions, to support the design and analysis of teaching augmentation. We contextualize the framework using existing designs cases, to surface underlying design trade-offs: for example, balancing actionability of presented information with teachers' needs for professional autonomy, or balancing unobtrusiveness with informativeness in the design of TA systems. Applying the TA framework, we identify opportunities for future research and design.
TanGi: Tangible Proxies for Embodied Object Exploration and Manipulation in Virtual Reality
Venue: arXiv cs.HC 2020
Year: 2020

Exploring and manipulating complex virtual objects is challenging due to limitations of conventional controllers and free-hand interaction techniques. We present the TanGi toolkit which enables novices to rapidly build physical proxy objects using Composable Shape Primitives. TanGi also provides Manipulators allowing users to build objects including movable parts, making them suitable for rich object exploration and manipulation in VR. With a set of different use cases and applications we show the capabilities of the TanGi toolkit, and evaluate its use. In a study with 16 participants, we demonstrate that novices can quickly build physical proxy objects using the Composable Shape Primitives, and explore how different levels of object embodiment affect virtual object exploration. In a second study with 12 participants we evaluate TanGi's Manipulators, and investigate the effectiveness of embodied interaction. Findings from this study show that TanGi's proxies outperform traditional controllers, and were generally favored by participants.
Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification
Venue: arXiv cs.CY 2020
Year: 2020

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.
Multi-Antenna Jamming in Covert Communication
Venue: arXiv cs.IT 2020
Year: 2020

Covert communication conceals transmission of messages from Alice to Bob out of a watchful adversary, Willie, who tries to determine if a transmission took place or not. While covert communication in a basic, vanilla setting where all variables are known to Willie, results in the well-known square-root law, when a jammer is present and assists Alice by creating uncertainty in Willie's decoder, a strictly positive transmission rate is possible. In this work, we analyze the case where the jammer is equipped with multiple antennas. Specifically, we analyze the effect of multiple antennas at the jammer on Alice's transmission power and consequently on the transmission rate. We consider both cases, one in which the channel knowledge is known and one in which it is unknown by the jammer. We formulate several optimization problems for the transmission strategies of the jammer, to maximize his assistance to Alice, in terms of maximizing a ratio between Willie's and Bob's noise variances. When the channel information is known to the jammer, we show that the optimal strategy of the jammer is to perform beamforming towards a single direction with all his available power. This direction though, is not trivial, since it reflects an optimal tradeoff point between minimizing the interference at Bob and maximizing the interference at Willie. When the channel knowledge is unknown, we show that the optimal strategy of the jammer is either to transmit isotropically to all directions or to the null-space of Bob, where this choice depends on certain channel conditions. This is in contrast to current schemes in the literature. Furthermore, we extend the optimization problems to the case where Bob is also equipped with multiple antennas, and provide insightful results, shown to be asymptotically optimal, accompanied by simulations.
Age-Partitioned Bloom Filters
Venue: arXiv cs.DS 2020
Year: 2020

Bloom filters (BF) are widely used for approximate membership queries over a set of elements. BF variants allow removals, sets of unbounded size or querying a sliding window over an unbounded stream. However, for this last case the best current approaches are dictionary based (e.g., based on Cuckoo Filters or TinyTable), and it may seem that BF-based approaches will never be competitive to dictionary-based ones. In this paper we present Age-Partitioned Bloom Filters, a BF-based approach for duplicate detection in sliding windows that not only is competitive in time-complexity, but has better space usage than current dictionary-based approaches (e.g., SWAMP), at the cost of some moderate slack. APBFs retain the BF simplicity, unlike dictionary-based approaches, important for hardware-based implementations, and can integrate known improvements such as double hashing or blocking. We present an Age-Partitioned Blocked Bloom Filter variant which can operate with 2-3 cache-line accesses per insertion and around 2-4 per query, even for high accuracy filters.
Camera-Based Adaptive Trajectory Guidance via Neural Networks
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we introduce a novel method to capture visual trajectories for navigating an indoor robot in dynamic settings using streaming image data. First, an image processing pipeline is proposed to accurately segment trajectories from noisy backgrounds. Next, the captured trajectories are used to design, train, and compare two neural network architectures for predicting acceleration and steering commands for a line following robot over a continuous space in real time. Lastly, experimental results demonstrate the performance of the neural networks versus human teleoperation of the robot and the viability of the system in environments with occlusions and/or low-light conditions.