OpenRaiser/Intern-Atlas huggingface dataset:
Multiple criteria decision analysis with the SRF-II method to compare hypotheses of adaptive reuse for an iconic historical building
Venue: arXiv cs.CY 2020
Year: 2020

The paper shows how Multiple Criteria Decision Aiding (MCDA) tools can support the analyses of six hypotheses of adaptive reuse of an iconic historical building in Turin, Italy (called Stock Exchange) to identify the preferred alternative. In the last two years, the debate around the requalification of the building has been huge for several reasons: it is perceived as a "monument" by citizens; it shows architectural and typological values nationally recognized; it involves public and private interests. In this context, interacting with experts involved in the ongoing discussion, we consider a recently proposed conjunction of four MCDA methods, namely: Multiple Criteria Hierarchy Process (MCHP), permitting to consider structural relationships between criteria; ELECTRE III, considering three types of interaction effects between criteria (strengthening, weakening and antagonistic effects); the imprecise SRF method, supplying an easily understandable approach to collect information from the DM; and Stochastic Multicriteria Acceptability Analysis (SMAA), providing robust recommendation, in terms of rankings and relations of preference, indifference and incomparability between project alternatives, at each level of the hierarchy. We propose a modification of the SRF methodology, called SFR-II, to increase the reliability of the decision aid procedure, which could constitute a significant advance for the same SRF method.
Sharing gNB components in RAN slicing: A perspective from 3GPP/NFV standards
Venue: arXiv cs.NI 2020
Year: 2020

To implement the next Generation NodeBs (gNBs) that are present in every Radio Access Network (RAN) slice subnet, Network Function Virtualization (NFV) enables the deployment of some of the gNB components as Virtual Networks Functions (VNFs). Deploying individual VNF instances for these components could guarantee the customization of each RAN slice subnet. However, due to the multiplicity of VNFs, the required amount of virtual resources will be greater compared to the case where a single VNF instance carries the aggregated traffic of all the RAN slice subnets. Sharing gNB components between RAN slice subnets could optimize the trade-off between customization, isolation and resource utilization. In this article, we shed light on the key aspects in the Third Generation Partnership Project (3GPP)/NFV standards for sharing gNB components. First, we identify four possible scenarios for sharing gNB components. Then, we analyze the impact of sharing on the customization level of each RAN slice subnet. Later, we determine the main factors that enable isolation between RAN slice subnets. Finally, we propose a 3GPP/NFV-based description model to define the lifecycle management of shared gNB components
Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Venue: arXiv cs.CV 2020
Year: 2020

Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of using bones as anatomical landmarks in surgical navigation. We herein propose a method to encode the physics of ultrasound propagation into a factor graph formulation for the purpose of bone surface delineation. In this graph structure, unary node potentials encode the local likelihood for being a soft tissue or acoustic-shadow (behind bone surface) region, both learned through image descriptors. Pair-wise edge potentials encode ultrasound propagation constraints of bone surfaces given their large acoustic-impedance difference. We evaluate the proposed method in comparison with four earlier approaches, on in-vivo ultrasound images collected from dorsal and volar views of the forearm. The proposed method achieves an average root-mean-square error and symmetric Hausdorff distance of 0.28mm and 1.78mm, respectively. It detects 99.9% of the annotated bone surfaces with a mean scanline error (distance to annotations) of 0.39mm.
Retentive Lenses
Venue: arXiv cs.PL 2020
Year: 2020

Based on Foster et al.'s lenses, various bidirectional programming languages and systems have been developed for helping the user to write correct data synchronisers. The two well-behavedness laws of lenses, namely Correctness and Hippocraticness, are usually adopted as the guarantee of these systems. While lenses are designed to retain information in the source when the view is modified, well-behavedness says very little about the retaining of information: Hippocraticness only requires that the source be unchanged if the view is not modified, and nothing about information retention is guaranteed when the view is changed. To address the problem, we propose an extension of the original lenses, called retentive lenses, which satisfy a new Retentiveness law guaranteeing that if parts of the view are unchanged, then the corresponding parts of the source are retained as well. As a concrete example of retentive lenses, we present a domain-specific language for writing tree transformations; we prove that the pair of get and put functions generated from a program in our DSL forms a retentive lens. We demonstrate the practical use of retentive lenses and the DSL by presenting case studies on code refactoring, Pombrio and Krishnamurthi's resugaring, and XML synchronisation.
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
Venue: arXiv cs.CV 2020
Year: 2020

Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we propose a learning-based approach for frame-to-frame monocular visual odometry estimation. The proposed network is only learned by disparity maps for not only covering the environment changes but also solving the scale problem. Furthermore, attention block and skip-ordering scheme are introduced to achieve robust performance in various driving environment. Our network is compared with the conventional methods which use common domain such as color or optical flow. Experimental results confirm that the proposed network shows better performance than other approaches with higher and more stable results.
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network
Venue: arXiv cs.CL 2020
Year: 2020

Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein-protein pairs with memory networks. The proposed MNM captures important context clues related to knowledge representations learned from knowledge bases. Both entity embeddings and relation embeddings of prior knowledge are effective in improving the PPI extraction model, leading to a new state-of-the-art performance on the BioCreative VI PPI dataset. The paper also shows that multiple computational layers over an external memory are superior to long short-term memory networks with the local memories.
Equivalence Classes and Conditional Hardness in Massively Parallel Computations
Venue: arXiv cs.DC 2020
Year: 2020

The Massively Parallel Computation (MPC) model serves as a common abstraction of many modern large-scale data processing frameworks, and has been receiving increasingly more attention over the past few years, especially in the context of classical graph problems. So far, the only way to argue lower bounds for this model is to condition on conjectures about the hardness of some specific problems, such as graph connectivity on promise graphs that are either one cycle or two cycles, usually called the one cycle vs. two cycles problem. This is unlike the traditional arguments based on conjectures about complexity classes (e.g., $\textsf{P} \neq \textsf{NP}$), which are often more robust in the sense that refuting them would lead to groundbreaking algorithms for a whole bunch of problems. In this paper we present connections between problems and classes of problems that allow the latter type of arguments. These connections concern the class of problems solvable in a sublogarithmic amount of rounds in the MPC model, denoted by $\textsf{MPC}(o(\log N))$, and some standard classes concerning space complexity, namely $\textsf{L}$ and $\textsf{NL}$, and suggest conjectures that are robust in the sense that refuting them would lead to many surprisingly fast new algorithms in the MPC model. We also obtain new conditional lower bounds, and prove new reductions and equivalences between problems in the MPC model.
Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted via social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this paper we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms eight state-of-the-art recommendation models, achieving at least 3~5.3% improvement.
A Prevention and a Traction System for Ransomware Attacks
Venue: arXiv cs.CR 2020
Year: 2020

Over the past three years, especially following WannaCry malware, ransomware has become one of the biggest concerns for private businesses, state, and local government agencies. According to Homeland Security statistics, 1.5 million ransomware attacks have occurred per year since 2016. Cybercriminals often use creative methods to inject their malware into the target machines and use sophisticated cryptographic techniques to hold hostage victims' files and programs unless a certain amount of equivalent Bitcoin is paid. The return to the cybercriminals is so high (estimated \$1 billion in 2019) without any cost because of the advanced anonymity provided by cryptocurrencies, especially Bitcoin \cite{Paquet-Clouston2019}. Given this context, this study first discusses the current state of ransomware, detection, and prevention systems. Second, we propose a global ransomware center to better manage our concerted efforts against cybercriminals. The policy implications of the proposed study are discussed in the conclusion section.
Incremental Monoidal Grammars
Venue: arXiv cs.FL 2020
Year: 2020

In this work we define formal grammars in terms of free monoidal categories, along with a functor from the category of formal grammars to the category of automata. Generalising from the Booleans to arbitrary semirings, we extend our construction to weighted formal grammars and weighted automata. This allows us to link the categorical viewpoint on natural language to the standard machine learning notion of probabilistic language model.
Egoistic Incentives Based on Zero-Determinant Alliances for Large-Scale Systems
Venue: arXiv cs.GT 2020
Year: 2020

Social dilemmas exist in various fields and give rise to the so-called free-riding problem, leading to collective fiascos. The difficulty of tracking individual behaviors makes egoistic incentives in large-scale systems a challenging task. However, the state-of-the-art mechanisms are either individual-based or state-dependent, resulting in low efficiency in large-scale networks. In this paper, we propose an egoistic incentive mechanism from a connected (network) perspective rather than an isolated (individual) perspective by taking advantage of the social nature of people. We make use of a zero-determinant (ZD) strategy for rewarding cooperation and sanctioning defection. After proving cooperation is the dominant strategy for ZD players, we optimize their deployment to facilitate cooperation over the whole system. To further speed up cooperation, we derive a ZD alliance strategy for sequential multiple-player repeated games to empower ZD players with higher controllable leverage, which undoubtedly enriches the theoretical system of ZD strategies and broadens their application domain. Our approach is stateless and stable, which contributes to its scalability. Extensive simulations based on a real world trace data as well as synthetic data demonstrate the effectiveness of our proposed egoistic incentive approach under different networking scenarios.
The Past and Present of Imitation Learning: A Citation Chain Study
Venue: arXiv cs.LG 2020
Year: 2020

Imitation Learning is a promising area of active research. Over the last 30 years, Imitation Learning has advanced significantly and been used to solve difficult tasks ranging from Autonomous Driving to playing Atari games. In the course of this development, different methods for performing Imitation Learning have fallen into and out of favor. In this paper, I explore the development of these different methods and attempt to examine how the field has progressed. I focus my analysis on surveying 4 landmark papers that sequentially build upon each other to develop increasingly impressive Imitation Learning methods.
Online Joint Placement and Allocation of Virtual Network Functions with Heterogeneous Servers
Venue: arXiv cs.NI 2020
Year: 2020

Network Function Virtualization (NFV) is a promising virtualization technology that has the potential to significantly reduce the expenses and improve the service agility. NFV makes it possible for Internet Service Providers (ISPs) to employ various Virtual Network Functions (VNFs) without installing new equipments. One of the most attractive approaches in NFV technology is a so-called Joint Placement and Allocation of Virtual Network Functions (JPA-VNF) which considers the balance between VNF investment with Quality of Services (QoS). We introduce a novel capability function to measure the potential of locating VNF instances for each server in the proposed OJPA-HS model. This model allows the servers in the network to be heterogeneous, at the same time combines and generalizes many classical JPA-VNF models. Despite its NP-hardness, we present a provable best-possible deterministic online algorithm based on dynamic programming (DP). To conquer the high complexity of DP, we propose two additional randomized heuristics, the Las Vegas (LV) and Monte Carlo (MC) randomized algorithms, which performs even as good as DP with much smaller complexity. Besides, MC is a promising heuristic in practice as it has the advantage to deal with big data environment. Extensive numerical experiments are constructed for the proposed algorithms in the paper.
Training Progressively Binarizing Deep Networks Using FPGAs
Venue: arXiv cs.CV 2020
Year: 2020

While hardware implementations of inference routines for Binarized Neural Networks (BNNs) are plentiful, current realizations of efficient BNN hardware training accelerators, suitable for Internet of Things (IoT) edge devices, leave much to be desired. Conventional BNN hardware training accelerators perform forward and backward propagations with parameters adopting binary representations, and optimization using parameters adopting floating or fixed-point real-valued representations--requiring two distinct sets of network parameters. In this paper, we propose a hardware-friendly training method that, contrary to conventional methods, progressively binarizes a singular set of fixed-point network parameters, yielding notable reductions in power and resource utilizations. We use the Intel FPGA SDK for OpenCL development environment to train our progressively binarizing DNNs on an OpenVINO FPGA. We benchmark our training approach on both GPUs and FPGAs using CIFAR-10 and compare it to conventional BNNs.
An improved online learning algorithm for general fuzzy min-max neural network
Venue: arXiv cs.LG 2020
Year: 2020

This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new on-line learning algorithm, a simple ensemble method is also proposed.
Improving BLE Beacon Proximity Estimation Accuracy through Bayesian Filtering
Venue: arXiv cs.NI 2020
Year: 2020

The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application such as Proximity-Based Services (PBS), and Location-Based Services (LBS). For these systems to perform, it is essential to have reliable hardware and predict a user's position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize Received Signal Strength Indicators (RSSI) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this work, Bluetooth Low Energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a Non-parametric Information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30 % in comparison with traditional filtering, when the beacon and the receiver are within 3 m.
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning
Venue: arXiv cs.LG 2020
Year: 2020

Fatigue is the most vital factor of road fatalities and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to analyze an electroencephalogram (EEG) dataset captured during a simulated endurance driving test. By measuring the correlation between drowsiness and driving performance, this experiment represents an important brain-computer interface (BCI) paradigm especially from an application perspective. We adapt the terminologies in the driving test to fit the reinforcement learning framework, thus formulate the drowsiness estimation problem as an optimization of a Q-learning task. By referring to the latest deep Q-Learning technologies and attending to the characteristics of EEG data, we tailor a deep Q-network for action proposition that can indirectly estimate drowsiness. Our results show that the trained model can trace the variations of mind state in a satisfactory way against the testing EEG data, which demonstrates the feasibility and practicability of this new computation paradigm. We also show that our method outperforms the supervised learning counterpart and is superior for real applications. To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can be potentially generalized to other BCI cases.
Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
Venue: arXiv cs.HC 2020
Year: 2020

New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet the minimal requirements that we set based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22--23 percentage points; and providing more granular controls on the first page decreases consent by 8--20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance.
LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition
Venue: arXiv cs.CL 2020
Year: 2020

In recent years, deep learning has achieved great success in many natural language processing tasks including named entity recognition. The shortcoming is that a large amount of manually-annotated data is usually required. Previous studies have demonstrated that active learning could elaborately reduce the cost of data annotation, but there is still plenty of room for improvement. In real applications we found existing uncertainty-based active learning strategies have two shortcomings. Firstly, these strategies prefer to choose long sequence explicitly or implicitly, which increase the annotation burden of annotators. Secondly, some strategies need to invade the model and modify to generate some additional information for sample selection, which will increase the workload of the developer and increase the training/prediction time of the model. In this paper, we first examine traditional active learning strategies in a specific case of BiLstm-CRF that has widely used in named entity recognition on several typical datasets. Then we propose an uncertainty-based active learning strategy called Lowest Token Probability (LTP) which combines the input and output of CRF to select informative instance. LTP is simple and powerful strategy that does not favor long sequences and does not need to invade the model. We test LTP on multiple datasets, and the experiments show that LTP performs slightly better than traditional strategies with obviously less annotation tokens on both sentence-level accuracy and entity-level F1-score. Related code have been release on https://github.com/HIT-ICES/AL-NER