OpenRaiser/Intern-Atlas huggingface dataset:
Physical Primitive Decomposition
Venue: arXiv cs.CV 2018
Year: 2018

Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition---understanding an object through its components, each with physical and geometric attributes. As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object's appearance and its behaviors in physical events. Our model performs well on block towers and tools in both synthetic and real scenarios; we also demonstrate that visual and physical observations often provide complementary signals. We further present ablation and behavioral studies to better understand our model and contrast it with human performance.
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural language applications. Designing optimal network architectures from a principled or rational approach however has been less than successful, with the best successful approaches utilizing an additional machine learning algorithm to tune the network hyperparameters. However, in many technical fields, there exist established domain knowledge and understanding about the subject matter. In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network. We demonstrate proof-of-concept by developing IL-Net, a furcated network for predicting the properties of ionic liquids, which is a class of complex multi-chemicals entities. Compared to existing state-of-the-art approaches, we show that furcated networks can improve model accuracy by approximately 20-35%, without using additional labeled data. Lastly, we distill two key design principles for furcated networks that can be adapted to other domains.
Balance in signed networks
Venue: arXiv cs.SI 2018
Year: 2018

We consider signed networks in which connections or edges can be either positive (friendship, trust, alliance) or negative (dislike, distrust, conflict). Early literature in graph theory theorized that such networks should display "structural balance," meaning that certain configurations of positive and negative edges are favored and others are disfavored. Here we propose two measures of balance in signed networks based on the established notions of weak and strong balance, and compare their performance on a range of tasks with each other and with previously proposed measures. In particular, we ask whether real-world signed networks are significantly balanced by these measures compared to an appropriate null model, finding that indeed they are, by all the measures studied. We also test our ability to predict unknown signs in otherwise known networks by maximizing balance. In a series of cross-validation tests we find that our measures are able to predict signs substantially better than chance.
A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University
Venue: arXiv cs.LG 2018
Year: 2018

The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestation as control inputs to a building. Furthermore, the occupants of a building typically lack the independent motivation necessary to contribute to and play a key role in the control of smart building infrastructure. Moreover, true human actions and their integration with sensing/actuation platforms remains unknown to the decision maker tasked with improving operational efficiency. By modeling user interaction as a sequential discrete game between non-cooperative players, we introduce a gamification approach for supporting user engagement and integration in a human-centric cyber-physical system. We propose the design and implementation of a large-scale network game with the goal of improving the energy efficiency of a building through the utilization of cutting-edge Internet of Things (IoT) sensors and cyber-physical systems sensing/actuation platforms. A benchmark utility learning framework that employs robust estimations for classical discrete choice models provided for the derived high dimensional imbalanced data. To improve forecasting performance, we extend the benchmark utility learning scheme by leveraging Deep Learning end-to-end training with Deep bi-directional Recurrent Neural Networks. We apply the proposed methods to high dimensional data from a social game experiment designed to encourage energy efficient behavior among smart building occupants in Nanyang Technological University (NTU) residential housing. Using occupant-retrieved actions for resources such as lighting and A/C, we simulate the game defined by the estimated utility functions.
Organic Computing as Chance for Interwoven Systems
Venue: arXiv cs.SE 2018
Year: 2018

Systems are growing into more complex ones for developing and maintaining. Existing systems which do not have much in common on the first look are connected, due to the technical progress, even if it was never intended that way. It is an upcoming challenge to handle these large-scale and complex systems. A solution must be found to manage these "Interwoven Systems". Therefore it is discussed where approaches of "Organic Computing" can help, to handle some of these upcoming challenges.
Do Your Cores Play Nicely? A Portable Framework for Multi-core Interference Tuning and Analysis
Venue: arXiv cs.DC 2018
Year: 2018

Multi-core architectures can be leveraged to allow independent processes to run in parallel. However, due to resources shared across cores, such as caches, distinct processes may interfere with one another, e.g. affecting execution time. Analysing the extent of this interference is difficult due to: (1) the diversity of modern architectures, which may contain different implementations of shared resources, and (2) the complex nature of modern processors, in which interference might arise due to subtle interactions. To address this, we propose a black-box auto-tuning approach that searches for processes that are effective at causing slowdowns for a program when executed in parallel. Such slowdowns provide lower bounds on worst-case execution time; an important metric in systems with real-time constraints. Our approach considers a set of parameterised "enemy" processes and "victim" programs, each targeting a shared resource. The autotuner searches for enemy process parameters that are effective at causing slowdowns in the victim programs. The idea is that victim programs behave as a proxy for shared resource usage of arbitrary programs. We evaluate our approach on: 5 different chips; 3 resources (cache, memory bus, and main memory); and consider several search strategies and slowdown metrics. Using enemy processes tuned per chip, we evaluate the slowdowns on the autobench and coremark benchmark suites and show that our method is able to achieve slowdowns in 98% of benchmark/chip combinations and provide similar results to manually written enemy processes.
A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors
Venue: arXiv cs.CV 2018
Year: 2018

Tumor detection in biomedical imaging is a time-consuming process for medical professionals and is not without errors. Thus in recent decades, researchers have developed algorithmic techniques for image processing using a wide variety of mathematical methods, such as statistical modeling, variational techniques, and machine learning. In this paper, we propose a semi-automatic method for liver segmentation of 2D CT scans into three labels denoting healthy, vessel, or tumor tissue based on graph cuts. First, we create a feature vector for each pixel in a novel way that consists of the 59 intensity values in the time series data and propose a simplified perimeter cost term in the energy functional. We normalize the data and perimeter terms in the functional to expedite the graph cut without having to optimize the scaling parameter $λ$. In place of a training process, predetermined tissue means are computed based on sample regions identified by expert radiologists. The proposed method also has the advantage of being relatively simple to implement computationally. It was evaluated against the ground truth on a clinical CT dataset of 10 tumors and yielded segmentations with a mean Dice similarity coefficient (DSC) of .77 and mean volume overlap error (VOE) of 36.7%. The average processing time was 1.25 minutes per slice.
On Evaluating Perceptual Quality of Online User-Generated Videos
Venue: arXiv cs.MM 2018
Year: 2018

This paper deals with the issue of the perceptual quality evaluation of user-generated videos shared online, which is an important step toward designing video-sharing services that maximize users' satisfaction in terms of quality. We first analyze viewers' quality perception patterns by applying graph analysis techniques to subjective rating data. We then examine the performance of existing state-of-the-art objective metrics for the quality estimation of user-generated videos. In addition, we investigate the feasibility of metadata accompanied with videos in online video-sharing services for quality estimation. Finally, various issues in the quality assessment of online user-generated videos are discussed, including difficulties and opportunities.
Future developments in cyber risk assessment for the internet of things
Venue: arXiv cs.CY 2018
Year: 2018

This article is focused on the economic impact assessment of Internet of Things (IoT) and its associated cyber risks vectors and vertices - a reinterpretation of IoT verticals. We adapt to IoT both the Cyber Value at Risk model, a well-established model for measuring the maximum possible loss over a given time period, and the MicroMort model, a widely used model for predicting uncertainty through units of mortality risk. The resulting new IoT MicroMort for calculating IoT risk is tested and validated with real data from the BullGuard's IoT Scanner - over 310,000 scans - and the Garner report on IoT connected devices. Two calculations are developed, the current state of IoT cyber risk and the future forecasts of IoT cyber risk. Our work therefore advances the efforts of integrating cyber risk impact assessments and offer a better understanding of economic impact assessment for IoT cyber risk.
Learning to Fingerprint the Latent Structure in Question Articulation
Venue: arXiv cs.AI 2018
Year: 2018

Abstract Machine understanding of questions is tightly related to recognition of articulation in the context of the computational capabilities of an underlying processing algorithm. In this paper a mathematical model to capture and distinguish the latent structure in the articulation of questions is presented. We propose an objective-driven approach to represent this latent structure and show that such an approach is beneficial when examples of complementary objectives are not available. We show that the latent structure can be represented as a system that maximizes a cost function related to the underlying objective. Further, we show that the optimization formulation can be approximated to building a memory of patterns represented as a trained neural auto-encoder. Experimental evaluation using many clusters of questions, each related to an objective, shows 80% recognition accuracy and negligible false positive across these clusters of questions. We then extend the same memory to a related task where the goal is to iteratively refine a dataset of questions based on the latent articulation. We also demonstrate a refinement scheme called K-fingerprints, that achieves nearly 100% recognition with negligible false positive across the different clusters of questions.
Keypoint Based Weakly Supervised Human Parsing
Venue: arXiv cs.CV 2018
Year: 2018

Fully convolutional networks (FCN) have achieved great success in human parsing in recent years. In conventional human parsing tasks, pixel-level labeling is required for guiding the training, which usually involves enormous human labeling efforts. To ease the labeling efforts, we propose a novel weakly supervised human parsing method which only requires simple object keypoint annotations for learning. We develop an iterative learning method to generate pseudo part segmentation masks from keypoint labels. With these pseudo masks, we train an FCN network to output pixel-level human parsing predictions. Furthermore, we develop a correlation network to perform joint prediction of part and object segmentation masks and improve the segmentation performance. The experiment results show that our weakly supervised method is able to achieve very competitive human parsing results. Despite our method only uses simple keypoint annotations for learning, we are able to achieve comparable performance with fully supervised methods which use the expensive pixel-level annotations.
Spatial Configuration of Agile Wireless Networks with Drone-BSs and User-in-the-loop
Venue: arXiv cs.NI 2018
Year: 2018

Agile networking can reduce over-engineering, costs, and energy waste. Towards that end, it is vital to exploit all degrees of freedom of wireless networks efficiently, so that service quality is not sacrificed. In order to reap the benefits of flexible networking, we propose a spatial network configuration scheme (SNC), which can result in efficient networking; both from the perspective of network capacity, and profitability. First, SNC utilizes the drone-base-stations (drone-BSs) to configure access points. Drone-BSs are shifting paradigms of heterogeneous wireless networks by providing radically flexible deployment opportunities. On the other hand, their limited endurance and potential high cost increase the importance of utilizing drone-BSs efficiently. Therefore, secondly, user mobility is exploited via user-in-the-loop (UIL), which aims at influencing users' mobility by offering incentives. The proposed uncoordinated SNC is a computationally efficient method, yet, it may be insufficient to exploit the synergy between drone-BSs and UIL. Hence, we propose joint SNC, which increases the performance gain along with the computational cost. Finally, semi-joint SNC combines benefits of joint SNC, with computational efficiency. Numerical results show that semi-joint SNC is two orders of magnitude times faster than joint SNC, and more than 15 percent profit can be obtained compared to conventional systems.
Motion Planning in Irreducible Path Spaces
Venue: arXiv cs.RO 2018
Year: 2018

The motion of a mechanical system can be defined as a path through its configuration space. Computing such a path has a computational complexity scaling exponentially with the dimensionality of the configuration space. We propose to reduce the dimensionality of the configuration space by introducing the irreducible path --- a path having a minimal swept volume. The paper consists of three parts: In part I, we define the space of all irreducible paths and show that planning a path in the irreducible path space preserves completeness of any motion planning algorithm. In part II, we construct an approximation to the irreducible path space of a serial kinematic chain under certain assumptions. In part III, we conduct motion planning using the irreducible path space for a mechanical snake in a turbine environment, for a mechanical octopus with eight arms in a pipe system and for the sideways motion of a humanoid robot moving through a room with doors and through a hole in a wall. We demonstrate that the concept of an irreducible path can be applied to any motion planning algorithm taking curvature constraints into account.
Put a Ring on It: Text Entry Performance on a Grip Ring Attached Smartphone
Venue: arXiv cs.HC 2018
Year: 2018

This paper presents results of a study investing effects of grip rings on text entry. Results revealed that grip rings do not affect text entry performance in terms of speed, accuracy, or keystrokes per character. It then reflects on future research directions based on the results and observations from the study. The purpose of this work is to stress the necessity of classifying and evaluating low-cost mobile phone accessories.
Reclaiming Data: Overcoming app identification barriers for exercising data protection rights
Venue: arXiv cs.CY 2018
Year: 2018

Data protection regulations generally afford individuals certain rights over their personal data, including the rights to access, rectify, and delete the data held on them. Exercising such rights naturally requires those with data management obligations (service providers) to be able to match an individual with their data. However, many mobile apps collect personal data, without requiring user registration or collecting details of a user's identity (email address, names, phone number, and so forth). As a result, a user's ability to exercise their rights will be hindered without means for an individual to link themselves with this 'nameless' data. Current approaches often involve those seeking to exercise their legal rights having to give the app's provider more personal information, or even to register for a service; both of which seem contrary to the spirit of data protection law. This paper explores these concerns, and indicates simple means for facilitating data subject rights through both application and mobile platform (OS) design.
Transferring Category-based Functional Grasping Skills by Latent Space Non-Rigid Registration
Venue: arXiv cs.RO 2018
Year: 2018

Objects within a category are often similar in their shape and usage. When we---as humans---want to grasp something, we transfer our knowledge from past experiences and adapt it to novel objects. In this paper, we propose a new approach for transferring grasping skills that accumulates grasping knowledge into a category-level canonical model. Grasping motions for novel instances of the category are inferred from geometric deformations between the observed instance and the canonical shape. Correspondences between the shapes are established by means of a non-rigid registration method that combines the Coherent Point Drift approach with subspace methods. By incorporating category-level information into the registration, we avoid unlikely shapes and focus on deformations actually observed within the category. Control poses for generating grasping motions are accumulated in the canonical model from grasping definitions of known objects. According to the estimated shape parameters of a novel instance, the control poses are transformed towards it. The category-level model makes our method particularly relevant for on-line grasping, where fully-observed objects are not easily available. This is demonstrated through experiments in which objects with occluded handles are successfully grasped.
SCORES: Shape Composition with Recursive Substructure Priors
Venue: arXiv cs.GR 2018
Year: 2018

We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and plausible 3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a recursive autoencoder network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical substructure shape prior based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss.We showresults of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling.
An FPGA Implementation of a Time Delay Reservoir Using Stochastic Logic
Venue: arXiv cs.NE 2018
Year: 2018

This paper presents and demonstrates a stochastic logic time delay reservoir design in FPGA hardware. The reservoir network approach is analyzed using a number of metrics, such as kernel quality, generalization rank, performance on simple benchmarks, and is also compared to a deterministic design. A novel re-seeding method is introduced to reduce the adverse effects of stochastic noise, which may also be implemented in other stochastic logic reservoir computing designs, such as echo state networks. Benchmark results indicate that the proposed design performs well on noise-tolerant classification problems, but more work needs to be done to improve the stochastic logic time delay reservoirs robustness for regression problems. In addition, we show that the stochastic design can significantly reduce area cost if the conversion between binary and stochastic representations implemented efficiently.
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
Venue: arXiv cs.AI 2018
Year: 2018

The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.
Blameworthiness in Strategic Games
Venue: arXiv cs.AI 2018
Year: 2018

There are multiple notions of coalitional responsibility. The focus of this paper is on the blameworthiness defined through the principle of alternative possibilities: a coalition is blamable for a statement if the statement is true, but the coalition had a strategy to prevent it. The main technical result is a sound and complete bimodal logical system that describes properties of blameworthiness in one-shot games.