OpenRaiser/Intern-Atlas huggingface dataset:
Core-Periphery Structure in Directed Networks
Venue: arXiv cs.SI 2019
Year: 2019

While studies of meso-scale structures in networks often focus on community structure, core--periphery structures can reveal new insights. This structure typically consists of a well-connected core and a periphery that is well connected to the core but sparsely connected internally. Most studies of core--periphery structure focus on undirected networks. We propose a generalisation of core-periphery structure to directed networks. Our approach yields a family of core-periphery block model formulations in which core and periphery sets are edge-direction dependent. We mainly focus on a particular core--periphery structure consisting of two core sets and two periphery sets which we motivate empirically. To detect this directed core-periphery structure we propose four different methods, with different trade-offs between computational complexity and accuracy. We assess these methods on three benchmarks and compare to four standard methods. On simulated data, the proposed methods match or outperform the standard methods. Applying our methods to three empirical networks -- a political blogs networks, a faculty hiring network, and a trade network -- illustrates that this directed core--periphery structure can offer novel insights about the underlying dataset.
Deep-Learning-Aided Successive-Cancellation Decoding of Polar Codes
Venue: arXiv cs.IT 2019
Year: 2019

A deep-learning-aided successive-cancellation list (DL-SCL) decoding algorithm for polar codes is introduced with deep-learning-aided successive-cancellation (DL-SC) decoding being a specific case of it. The DL-SCL decoder works by allowing additional rounds of SCL decoding when the first SCL decoding attempt fails, using a novel bit-flipping metric. The proposed bit-flipping metric exploits the inherent relations between the information bits in polar codes that are represented by a correlation matrix. The correlation matrix is then optimized using emerging deep-learning techniques. Performance results on a polar code of length 128 with 64 information bits concatenated with a 24-bit cyclic redundancy check show that the proposed bit-flipping metric in the proposed DL-SCL decoder requires up to 66% fewer multiplications and up to 36% fewer additions, without any need to perform transcendental functions, and by providing almost the same error-correction performance in comparison with the state of the art.
Unsupervised Inflection Generation Using Neural Language Modeling
Venue: arXiv cs.CL 2019
Year: 2019

The use of Deep Neural Network architectures for Language Modeling has recently seen a tremendous increase in interest in the field of NLP with the advent of transfer learning and the shift in focus from rule-based and predictive models (supervised learning) to generative or unsupervised models to solve the long-standing problems in NLP like Information Extraction or Question Answering. While this shift has worked greatly for languages lacking in inflectional morphology, such as English, challenges still arise when trying to build similar systems for morphologically-rich languages, since their individual words shift forms in context more often. In this paper we investigate the extent to which these new unsupervised or generative techniques can serve to alleviate the type-token ratio disparity in morphologically rich languages. We apply an off-the-shelf neural language modeling library to the newly introduced task of unsupervised inflection generation in the nominal domain of three morphologically rich languages: Romanian, German, and Finnish. We show that this neural language model architecture can successfully generate the full inflection table of nouns without needing any pre-training on large, wikipedia-sized corpora, as long as the model is shown enough inflection examples. In fact, our experiments show that pre-training hinders the generation performance.
SAIS: Single-stage Anchor-free Instance Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a simple yet efficientinstance segmentation approach based on the single-stage anchor-free detector, termed SAIS. In our approach, the instancesegmentation task consists of two parallel subtasks which re-spectively predict the mask coefficients and the mask prototypes.Then, instance masks are generated by linearly combining theprototypes with the mask coefficients. To enhance the quality ofinstance mask, the information from regression and classificationis fused to predict the mask coefficients. In addition, center-aware target is designed to preserve the center coordination ofeach instance, which achieves a stable improvement in instancesegmentation. The experiment on MS COCO shows that SAISachieves the performance of the exiting state-of-the-art single-stage methods with a much less memory footpr
Approximating Star Cover Problems
Venue: arXiv cs.DS 2019
Year: 2019

Given a metric space $(F \cup C, d)$, we consider star covers of $C$ with balanced loads. A star is a pair $(f, C_f)$ where $f \in F$ and $C_f \subseteq C$, and the load of a star is $\sum_{c \in C_f} d(f, c)$. In minimum load $k$-star cover problem $(\mathrm{MLkSC})$, one tries to cover the set of clients $C$ using $k$ stars that minimize the maximum load of a star, and in minimum size star cover $(\mathrm{MSSC})$ one aims to find the minimum number of stars of load at most $T$ needed to cover $C$, where $T$ is a given parameter. We obtain new bicriteria approximations for the two problems using novel rounding algorithms for their standard LP relaxations. For $\mathrm{MLkSC}$, we find a star cover with $(1+\varepsilon)k$ stars and $O(1/\varepsilon^2)\mathrm{OPT}_{\mathrm{MLk}}$ load where $\mathrm{OPT}_{\mathrm{MLk}}$ is the optimum load. For $\mathrm{MSSC}$, we find a star cover with $O(1/\varepsilon^2) \mathrm{OPT}_{\mathrm{MS}}$ stars of load at most $(2 + \varepsilon) T$ where $\mathrm{OPT}_{\mathrm{MS}}$ is the optimal number of stars for the problem. Previously, non-trivial bicriteria approximations were known only when $F = C$.
Deep Probabilistic Models to Detect Data Poisoning Attacks
Venue: arXiv cs.LG 2019
Year: 2019

Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time. In this work, we investigate backdoor data poisoning attack on deep neural networks (DNNs) by inserting a backdoor pattern in the training images. The resulting attack will misclassify poisoned test samples while maintaining high accuracies for the clean test-set. We present two approaches for detection of such poisoned samples by quantifying the uncertainty estimates associated with the trained models. In the first approach, we model the outputs of the various layers (deep features) with parametric probability distributions learnt from the clean held-out dataset. At inference, the likelihoods of deep features w.r.t these distributions are calculated to derive uncertainty estimates. In the second approach, we use Bayesian deep neural networks trained with mean-field variational inference to estimate model uncertainty associated with the predictions. The uncertainty estimates from these methods are used to discriminate clean from the poisoned samples.
Evaluation of Smartphone IMUs for Small Mobile Search and Rescue Robots
Venue: arXiv cs.RO 2019
Year: 2019

Small mobile robots are an important class of Search and Rescue Robots. Integrating all required components into such small robots is a difficult engineering task. Smartphones have already been made small, lightweight and cheap by the industry and are thus an excellent candidate as main controller for such robots. In this paper we outline how ROS can be used on Android devices and then evaluate one sensor which is very important for mobile robots: the Inertial Measurement Unit (IMU). Experiments are performed under static and dynamic conditions to measure the error of the IMUs of three smartphones and three professional IMUs. In the experiments we make use of a tracking system and an autonomous mobile robot.
Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian
Venue: arXiv cs.CY 2019
Year: 2019

News website comment sections are spaces where potentially conflicting opinions and beliefs are voiced. Addressing questions of how to study such cultural and societal conflicts through technological means, the present article critically examines possibilities and limitations of machine-guided exploration and potential facilitation of on-line opinion dynamics. These investigations are guided by a discussion of an experimental observatory for mining and analyzing opinions from climate change-related user comments on news articles from the TheGuardian.com. This observatory combines causal mapping methods with computational text analysis in order to mine beliefs and visualize opinion landscapes based on expressions of causation. By (1) introducing digital methods and open infrastructures for data exploration and analysis and (2) engaging in debates about the implications of such methods and infrastructures, notably in terms of the leap from opinion observation to debate facilitation, the article aims to make a practical and theoretical contribution to the study of opinion dynamics and conflict in new media environments.
A Formal Approach to the Engineering of Domain-Specific Distributed Systems
Venue: arXiv cs.PL 2019
Year: 2019

We review some results regarding specification, programming and verification of different classes of distributed systems which stemmed from the research of the Concurrency and Mobility Group at University of Firenze. More specifically, we examine the distinguishing features of network-aware programming, service-oriented computing, autonomic computing, and collective adaptive systems programming. We then present an overview of four different languages, namely Klaim, Cows, Scel and AbC. For each language, we discuss design choices, present syntax and semantics, show how the different formalisms can be used to model and program a travel booking scenario, and describe programming environments and verification techniques.
Radial-Layer Jamming Mechanism for String Configuration
Venue: arXiv cs.RO 2019
Year: 2019

Grippers can be attached to objects in a rigid mode, and they are therefore used in various applications, for example granular jamming gripper. This paper introduces a cutting-edge radial layer jamming mechanism with is tunable stiffness, which is critical for the development of grippers. The layer jamming mechanism generates friction between the layers of multi cylindrical walls by pulling wire. This paper describes the principles of three types of proposed tendon-driven jamming mechanism, in addition to their prototypes of string configuration and the experiments conducted on the holding torques of their joints. Due to the string configuration, the surface and three-dimensional (3D) shape. This mechanism can be implemented in various applications.
Internally-Balanced Magnetic Mechanisms Using Magnetic Spring for Producing Large Amplified Clamping Force
Venue: arXiv cs.RO 2019
Year: 2019

To detach a permanent magnet with a controlled force much smaller than its original attractive force, the Internally-Balanced Magnetic Unit (IB Magnet) was invented and has been applied to magnetic devices such as wall-climbing robots, ceil-dangling drones, and modular swarm robots. In contrast to its drastic reduction rate on the control force, the IB Magnet has two major problems on its nonlinear spring which cancels out the internal force on the magnet: complicated design procedure and trade-off relationship between balancing precision and mechanism volume. This paper proposes a principle of a new balancing method for the IB Magnet which uses a like-pole pair of magnets as a magnetic spring, whose repulsive force ideally equals the attractive force of an unlike-pole pair exactly. To verify the proposed principle, the authors realized a prototype model of the IB Magnet using magnetic spring and verified through experiments its reduction rate is comparable to those of conventional IB Magnets. Moreover, the authors discussed and realized a robotic clamp as an application example containing proposed IB Magnets as its internal mechanism.
Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape and Diffuse Reflectance
Venue: arXiv cs.CV 2019
Year: 2019

Face recognition is widely employed in Automated Border Control (ABC) gates, which verify the face image on passport or electronic Machine Readable Travel Document (eMTRD) against the captured image to confirm the identity of the passport holder. In this paper, we present a robust morph detection algorithm that is based on differential morph detection. The proposed method decomposes the bona fide image captured from the ABC gate and the digital face image extracted from the eMRTD into the diffuse reconstructed image and a quantized normal map. The extracted features are further used to learn a linear classifier (SVM) to detect a morphing attack based on the assessment of differences between the bona fide image from the ABC gate and the digital face image extracted from the passport. Owing to the availability of multiple cameras within an ABC gate, we extend the proposed method to fuse the classification scores to generate the final decision on morph-attack-detection. To validate our proposed algorithm, we create a morph attack database with overall 588 images, where bona fide are captured in an indoor lighting environment with a Canon DSLR Camera with one sample per subject and correspondingly images from ABC gates. We benchmark our proposed method with the existing state-of-the-art and can state that the new approach significantly outperforms previous approaches in the ABC gate scenario.
Facial Expression Representation Learning by Synthesizing Expression Images
Venue: arXiv cs.CV 2019
Year: 2019

Representations used for Facial Expression Recognition (FER) usually contain expression information along with identity features. In this paper, we propose a novel Disentangled Expression learning-Generative Adversarial Network (DE-GAN) which combines the concept of disentangled representation learning with residue learning to explicitly disentangle facial expression representation from identity information. In this method the facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. Unlike previous works using only expression residual learning for facial expression recognition, our method learns the disentangled expression representation along with the expressive component recorded by the encoder of DE-GAN. In order to improve the quality of synthesized expression images and the effectiveness of the learned disentangled expression representation, expression and identity classification is performed by the discriminator of DE-GAN. Experiments performed on widely used datasets (CK+, MMI, Oulu-CASIA) show that the proposed technique produces comparable or better results than state-of-the-art methods.
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
Venue: arXiv cs.CR 2019
Year: 2019

Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state or classifying it to the correct APT family is challenging for several reasons. First, each nation-state has more than a single cyber unit that develops such malware, rendering traditional authorship attribution algorithms useless. Furthermore, the dataset of such available APTs is still extremely small. Finally, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. In this paper, we use a deep neural network (DNN) as a classifier for nation-state APT attribution. We record the dynamic behavior of the APT when run in a sandbox and use it as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. We also use the same raw features for APT family classification. Finally, we use the feature abstractions learned by the APT family classifier to solve the attribution problem. Using a test set of 1000 Chinese and Russian developed APTs, we achieved an accuracy rate of 98.6%.
Knowledge-Enriched Visual Storytelling
Venue: arXiv cs.CL 2019
Year: 2019

Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST.
Nonintrusive Load Monitoring for Machines used in Manufacturing
Venue: arXiv cs.OH 2019
Year: 2019

In order to increase the electric energy efficiency of production machines, it is necessary to determine the energy demand of the constituent electric loads. Therefore, a new measurement system based on nonintrusive load monitoring is proposed in this paper. It only measures the voltage and current of the aggregate load and then uses automatic disaggregation methods to estimate the energy demand of the constituent loads. In two case studies, the energy demand of most loads could be determined with an accuracy of 85~\% or more in this way.
RESCUE: Interdependent Challenges of Reliability, Security and Quality in Nanoelectronic Systems
Venue: arXiv cs.CR 2019
Year: 2019

The recent trends for nanoelectronic computing systems include machine-to-machine communication in the era of Internet-of-Things (IoT) and autonomous systems, complex safety-critical applications, extreme miniaturization of implementation technologies and intensive interaction with the physical world. These set tough requirements on mutually dependent extra-functional design aspects. The H2020 MSCA ITN project RESCUE is focused on key challenges for reliability, security and quality, as well as related electronic design automation tools and methodologies. The objectives include both research advancements and cross-sectoral training of a new generation of interdisciplinary researchers. Notable interdisciplinary collaborative research results for the first half-period include novel approaches for test generation, soft-error and transient faults vulnerability analysis, cross-layer fault-tolerance and error-resilience, functional safety validation, reliability assessment and run-time management, HW security enhancement and initial implementation of these into holistic EDA tools.
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization
Venue: arXiv cs.LG 2019
Year: 2019

The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the-art results on two Thai social text categorization tasks can be realized by pretraining a language model on a large noisy Thai social media corpus of over 1.26 billion tokens and later fine-tuned on the downstream classification tasks. Due to the linguistically noisy and domain-specific nature of the content, our unique data preprocessing steps designed for Thai social media were utilized to ease the training comprehension of the model. We compared four modern language models: ULMFiT, ELMo with biLSTM, OpenAI GPT, and BERT. We systematically compared the models across different dimensions including speed of pretraining and fine-tuning, perplexity, downstream classification benchmarks, and performance in limited pretraining data.
Towards an Integrative Educational Recommender for Lifelong Learners
Venue: arXiv cs.IR 2019
Year: 2019

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.
A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques
Venue: arXiv cs.MA 2019
Year: 2019

This paper attempts to design an intelligent simulation model for pedestrian crowd evacuation. For this purpose, the cellular automata(CA) was fully integrated with fuzzy logic, the kth nearest neighbors (KNN), and some statistical equations. In this model, each pedestrian was assigned a specific speed, according to his/her physical, biological and emotional features. The emergency behavior and evacuation efficiency of each pedestrian were evaluated by coupling his or her speed with various elements, such as environment, pedestrian distribution and familiarity with the exits. These elements all have great impacts on the evacuation process. Several experiments were carried out to verify the performance of the model in different emergency scenarios. The results show that the proposed model can predict the evacuation time and emergency behavior in various types of building interiors and pedestrian distributions. The research provides a good reference to the design of building evacuation systems.
Integrating Motion into Vision Models for Better Visual Prediction
Venue: arXiv cs.CV 2019
Year: 2019

We demonstrate an improved vision system that learns a model of its environment using a self-supervised, predictive learning method. The system includes a pan-tilt camera, a foveated visual input, a saccading reflex to servo the foveated region to areas high prediction error, input frame transformation synced to the camera motion, and a recursive, hierachical machine learning technique based on the Predictive Vision Model. In earlier work, which did not integrate camera motion into the vision model, prediction was impaired and camera movement suffered from undesired feedback effects. Here we detail the integration of camera motion into the predictive learning system and show improved visual prediction and saccadic behavior. From these experiences, we speculate on the integration of additional sensory and motor systems into self-supervised, predictive learning models.