OpenRaiser/Intern-Atlas huggingface dataset:
Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we introduce a novel methodology for characterising the performance of deep learning networks (ResNets and DenseNet) with respect to training convergence and generalisation as a function of mini-batch size and learning rate for image classification. This methodology is based on novel measurements derived from the eigenvalues of the approximate Fisher information matrix, which can be efficiently computed even for high capacity deep models. Our proposed measurements can help practitioners to monitor and control the training process (by actively tuning the mini-batch size and learning rate) to allow for good training convergence and generalisation. Furthermore, the proposed measurements also allow us to show that it is possible to optimise the training process with a new dynamic sampling training approach that continuously and automatically change the mini-batch size and learning rate during the training process. Finally, we show that the proposed dynamic sampling training approach has a faster training time and a competitive classification accuracy compared to the current state of the art.
SINE: Scalable Incomplete Network Embedding
Venue: arXiv cs.SI 2018
Year: 2018

Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms all operate under the assumption that networks are complete. Thus, their performance is vulnerable to missing data and suffers from poor scalability. In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates a probabilistic learning framework that separately models pairs of node-context and node-attribute relationships. Different from existing attributed network embedding algorithms, SINE provides greater flexibility to make the best of useful information and mitigate negative effects of missing information on representation learning. A stochastic gradient descent based online algorithm is derived to learn node representations, allowing SINE to scale up to large-scale networks with high learning efficiency. We evaluate the effectiveness and efficiency of SINE through extensive experiments on real-world networks. Experimental results confirm that SINE outperforms state-of-the-art baselines in various tasks, including node classification, node clustering, and link prediction, under settings with missing links and node attributes. SINE is also shown to be scalable and efficient on large-scale networks with millions of nodes/edges and high-dimensional node features. The source code of this paper is available at https://github.com/daokunzhang/SINE.
Morph: Flexible Acceleration for 3D CNN-based Video Understanding
Venue: arXiv cs.LG 2018
Year: 2018

The past several years have seen both an explosion in the use of Convolutional Neural Networks (CNNs) and the design of accelerators to make CNN inference practical. In the architecture community, the lion share of effort has targeted CNN inference for image recognition. The closely related problem of video recognition has received far less attention as an accelerator target. This is surprising, as video recognition is more computationally intensive than image recognition, and video traffic is predicted to be the majority of internet traffic in the coming years. This paper fills the gap between algorithmic and hardware advances for video recognition by providing a design space exploration and flexible architecture for accelerating 3D Convolutional Neural Networks (3D CNNs) - the core kernel in modern video understanding. When compared to (2D) CNNs used for image recognition, efficiently accelerating 3D CNNs poses a significant engineering challenge due to their large (and variable over time) memory footprint and higher dimensionality. To address these challenges, we design a novel accelerator, called Morph, that can adaptively support different spatial and temporal tiling strategies depending on the needs of each layer of each target 3D CNN. We codesign a software infrastructure alongside the Morph hardware to find good-fit parameters to control the hardware. Evaluated on state-of-the-art 3D CNNs, Morph achieves up to 3.4x (2.5x average) reduction in energy consumption and improves performance/watt by up to 5.1x (4x average) compared to a baseline 3D CNN accelerator, with an area overhead of 5%. Morph further achieves a 15.9x average energy reduction on 3D CNNs when compared to Eyeriss.
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos
Venue: arXiv cs.CV 2018
Year: 2018

Activity recognition in videos in a deep-learning setting---or otherwise---uses both static and pre-computed motion components. The method of combining the two components, whilst keeping the burden on the deep network less, still remains uninvestigated. Moreover, it is not clear what the level of contribution of individual components is, and how to control the contribution. In this work, we use a combination of CNN-generated static features and motion features in the form of motion tubes. We propose three schemas for combining static and motion components: based on a variance ratio, principal components, and Cholesky decomposition. The Cholesky decomposition based method allows the control of contributions. The ratio given by variance analysis of static and motion features match well with the experimental optimal ratio used in the Cholesky decomposition based method. The resulting activity recognition system is better or on par with existing state-of-the-art when tested with three popular datasets. The findings also enable us to characterize a dataset with respect to its richness in motion information.
The State of Speech in HCI: Trends, Themes and Challenges
Venue: arXiv cs.HC 2018
Year: 2018

Speech interfaces are growing in popularity. Through a review of 68 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in HCI. We find that most studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes, or developed systems by using self-report questionnaires to measure concepts like usability and user attitudes. A thematic analysis of the research found that speech HCI work focuses on nine key topics: system speech production, modality comparison, user speech production, assistive technology \& accessibility, design insight, experiences with interactive voice response (IVR) systems, using speech technology for development, people's experiences with intelligent personal assistants (IPAs) and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably the need to develop theories of speech interface interaction, grow critical mass in this domain, increase design work, and expand research from single to multiple user interaction contexts so as to reflect current use contexts. We also highlight the need to improve measure reliability, validity and consistency, in the wild deployment and reduce barriers to building fully functional speech interfaces for research.
Semitotal Domination: New hardness results and a polynomial-time algorithm for graphs of bounded mim-width
Venue: arXiv cs.CC 2018
Year: 2018

A semitotal dominating set of a graph $G$ with no isolated vertex is a dominating set $D$ of $G$ such that every vertex in $D$ is within distance two of another vertex in $D$. The minimum size $γ_{t2}(G)$ of a semitotal dominating set of $G$ is squeezed between the domination number $γ(G)$ and the total domination number $γ_{t}(G)$. \textsc{Semitotal Dominating Set} is the problem of finding, given a graph $G$, a semitotal dominating set of $G$ of size $γ_{t2}(G)$. In this paper, we continue the systematic study on the computational complexity of this problem when restricted to special graph classes. In particular, we show that it is solvable in polynomial time for the class of graphs with bounded mim-width by a reduction to \textsc{Total Dominating Set} and we provide several approximation lower bounds for subclasses of subcubic graphs. Moreover, we obtain complexity dichotomies in monogenic classes for the decision versions of \textsc{Semitotal Dominating Set} and \textsc{Total Dominating Set}. Finally, we show that it is $\mathsf{NP}$-complete to recognise the graphs such that $γ_{t2}(G) = γ_{t}(G)$ and those such that $γ(G) = γ_{t2}(G)$, even if restricted to be planar and with maximum degree at most $4$, and we provide forbidden induced subgraph characterisations for the graphs heriditarily satisfying either of these two equalities.
Feedforward Neural Networks for Caching: Enough or Too Much?
Venue: arXiv cs.NI 2018
Year: 2018

We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.
Simple game semantics and Day convolution
Venue: arXiv cs.LO 2018
Year: 2018

Game semantics has provided adequate models for a variety of programming languages, in which types are interpreted as two-player games and programs as strategies. Melliès (2018) suggested that such categories of games and strategies may be obtained as instances of a simple abstract construction on weak double categories. However, in the particular case of simple games, his construction slightly differs from the standard category. We refine the abstract construction using factorisation systems, and show that the new construction yields the standard category of simple games and strategies. Another perhaps surprising instance is Day's convolution monoidal structure on the category of presheaves over a strict monoidal category.
Recent Advances in Mobile Grid and Cloud Computing
Venue: arXiv cs.DC 2018
Year: 2018

Grid and cloud computing systems have been extensively used to solve large and complex problems in science and engineering areas. These systems include powerful computing resources connected through high-speed networks. Due to recent advances in mobile computing and networking technologies, it has become feasible to integrate various mobile devices such as robots, aerial vehicles, sensors, and smartphones with grid and cloud computing systems. This integration enables design and development of next generation of applications through sharing of resources in mobile environments and also introduces several challenges due to dynamic and unpredictable network. This paper discusses applications, research challenges involved in design and development of mobile grid and cloud computing systems, and recent advances in the field.
Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms
Venue: arXiv cs.NE 2018
Year: 2018

In this paper, we review the state-of-the-art results in evolutionary computation and observe that we do not evolve non trivial software from scratch and with no human intervention. A number of possible explanations are considered, but we conclude that computational complexity of the problem prevents it from being solved as currently attempted. A detailed analysis of necessary and available computational resources is provided to support our findings.
Distributed and adaptive location identification system for mobile devices
Venue: arXiv cs.NI 2018
Year: 2018

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments.
Robust Gesture-Based Communication for Underwater Human-Robot Interaction in the context of Search and Rescue Diver Missions
Venue: arXiv cs.RO 2018
Year: 2018

We propose a robust gesture-based communication pipeline for divers to instruct an Autonomous Underwater Vehicle (AUV) to assist them in performing high-risk tasks and helping in case of emergency. A gesture communication language (CADDIAN) is developed, based on consolidated and standardized diver gestures, including an alphabet, syntax and semantics, ensuring a logical consistency. A hierarchical classification approach is introduced for hand gesture recognition based on stereo imagery and multi-descriptor aggregation to specifically cope with underwater image artifacts, e.g. light backscatter or color attenuation. Once the classification task is finished, a syntax check is performed to filter out invalid command sequences sent by the diver or generated by errors in the classifier. Throughout this process, the diver receives constant feedback from an underwater tablet to acknowledge or abort the mission at any time. The objective is to prevent the AUV from executing unnecessary, infeasible or potentially harmful motions. Experimental results under different environmental conditions in archaeological exploration and bridge inspection applications show that the system performs well in the field.
Improving Data Quality through Deep Learning and Statistical Models
Venue: arXiv cs.AI 2018
Year: 2018

Traditional data quality control methods are based on users experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy. Utilizing deep learning, we can leverage computing resources and advanced techniques to overcome these challenges and provide greater value to users. In this paper, we, the authors, first review relevant works and discuss machine learning techniques, tools, and statistical quality models. Second, we offer a creative data quality framework based on deep learning and statistical model algorithm for identifying data quality. Third, we use data involving salary levels from an open dataset published by the state of Arkansas to demonstrate how to identify outlier data and how to improve data quality via deep learning. Finally, we discuss future work.
Exploring Modern GPU Memory System Design Challenges through Accurate Modeling
Venue: arXiv cs.AR 2018
Year: 2018

This paper explores the impact of simulator accuracy on architecture design decisions in the general-purpose graphics processing unit (GPGPU) space. We perform a detailed, quantitative analysis of the most popular publicly available GPU simulator, GPGPU-Sim, against our enhanced version of the simulator, updated to model the memory system of modern GPUs in more detail. Our enhanced GPU model is able to describe the NVIDIA Volta architecture in sufficient detail to reduce error in memory system even counters by as much as 66X. The reduced error in the memory system further reduces execution time error versus real hardware by 2.5X. To demonstrate the accuracy of our enhanced model against a real machine, we perform a counter-by-counter validation against an NVIDIA TITAN V Volta GPU, demonstrating the relative accuracy of the new simulator versus the publicly available model. We go on to demonstrate that the simpler model discounts the importance of advanced memory system designs such as out-of-order memory access scheduling, while overstating the impact of more heavily researched areas like L1 cache bypassing. Our results demonstrate that it is important for the academic community to enhance the level of detail in architecture simulators as system complexity continues to grow. As part of this detailed correlation and modeling effort, we developed a new Correlator toolset that includes a consolidation of applications from a variety of popular GPGPU benchmark suites, designed to run in reasonable simulation times. The Correlator also includes a database of hardware profiling results for all these applications on NVIDIA cards ranging from Fermi to Volta and a toolchain that enables users to gather correlation statistics and create detailed counter-by-counter hardware correlation plots with minimal effort.
Unambiguous Forest Factorization
Venue: arXiv cs.FL 2018
Year: 2018

In this paper, we look at an unambiguous version of Simon's forest factorization theorem, a very deep result which has wide connections in algebra, logic and automata. Given a morphism $φ$ from $Σ^+$ to a finite semigroup $S$, we construct a universal, unambiguous automaton A which is "good" for $φ$. The goodness of $\Aa$ gives a very easy proof for the forest factorization theorem, providing a Ramsey split for any word in $Σ^{\infty}$ such that the height of the Ramsey split is bounded by the number of states of A. An important application of synthesizing good automata from the morphim $φ$ is in the construction of regular transducer expressions (RTE) corresponding to deterministic two way transducers.
The Effect of Whole-Body Haptic Feedback on Driver's Perception in Negotiating a Curve
Venue: arXiv cs.HC 2018
Year: 2018

It remains uncertain regarding the safety of driving in autonomous vehicles that, after a long, passive control and inattention to the driving situation, how the drivers will be effectively informed to take-over the control in emergency. In particular, the active role of vehicle force feedback on the driver's risk perception on curves has not been fully explored. To investigate it, the current paper examined the driver's cognitive and visual responses to the whole-body haptic feedback during curve negotiations. The effects of force feedback on drivers' responses on curves were investigated in a high-fidelity driving simulator while measuring EEG and visual gaze over ten participants. The preliminary analyses of the first two participants revealed that pupil diameter and fixation time on the curves were significantly longer when the driver received whole-body feedback, compared to none. The findings suggest that whole-body feedback can be used as an effective "advance notification" of hazards.
Tracking Users across the Web via TLS Session Resumption
Venue: arXiv cs.CR 2018
Year: 2018

User tracking on the Internet can come in various forms, e.g., via cookies or by fingerprinting web browsers. A technique that got less attention so far is user tracking based on TLS and specifically based on the TLS session resumption mechanism. To the best of our knowledge, we are the first that investigate the applicability of TLS session resumption for user tracking. For that, we evaluated the configuration of 48 popular browsers and one million of the most popular websites. Moreover, we present a so-called prolongation attack, which allows extending the tracking period beyond the lifetime of the session resumption mechanism. To show that under the observed browser configurations tracking via TLS session resumptions is feasible, we also looked into DNS data to understand the longest consecutive tracking period for a user by a particular website. Our results indicate that with the standard setting of the session resumption lifetime in many current browsers, the average user can be tracked for up to eight days. With a session resumption lifetime of seven days, as recommended upper limit in the draft for TLS version 1.3, 65% of all users in our dataset can be tracked permanently.
A Scalable, Trustworthy Infrastructure for Collaborative Container Repositories
Venue: arXiv cs.CR 2018
Year: 2018

We present a scalable "Trustworthy Container Repository" (TCR) infrastructure for the storage of software container images, such as those used by Docker. Using an authenticated data structure based on index-ordered Merkle trees (IOMTs), TCR aims to provide assurances of 1) Integrity, 2) Availability, and 3) Confidentiality to its users, whose containers are stored in an untrusted environment. Trust within the TCR architecture is rooted in a low-complexity, tamper-resistant trusted module. The use of IOMTs allows such a module to efficiently track a virtually unlimited number of container images, and thus provide the desired assurances for the system's users. Using a simulated version of the proposed system, we demonstrate the scalability of platform by showing logarithmic time complexity up to $2^{25}$ (32 million) container images. This paper presents both algorithmic and proof-of-concept software implementations of the proposed TCR infrastructure.
Malware triage for early identification of Advanced Persistent Threat activities
Venue: arXiv cs.CR 2018
Year: 2018

In the last decade, a new class of cyber-threats has emerged. This new cybersecurity adversary is known with the name of "Advanced Persistent Threat" (APT) and is referred to different organizations that in the last years have been "in the center of the eye" due to multiple dangerous and effective attacks targeting financial and politic, news headlines, embassies, critical infrastructures, TV programs, etc. In order to early identify APT related malware, a semi-automatic approach for malware samples analysis is needed. In our previous work we introduced a "malware triage" step for a semi-automatic malware analysis architecture. This step has the duty to analyze as fast as possible new incoming samples and to immediately dispatch the ones that deserve a deeper analysis, among all the malware delivered per day in the cyber-space, the ones that really worth to be further examined by analysts. Our paper focuses on malware developed by APTs, and we build our knowledge base, used in the triage, on known APTs obtained from publicly available reports. In order to have the triage as fast as possible, we only rely on static malware features, that can be extracted with negligible delay, and use machine learning techniques for the identification. In this work we move from multiclass classification to a group of oneclass classifier, which simplify the training and allows higher modularity. The results of the proposed framework highlight high performances, reaching a precision of 100% and an accuracy over 95%