OpenRaiser/Intern-Atlas huggingface dataset:
Towards Automatic Identification and Blocking of Non-Critical IoT Traffic Destinations
Venue: arXiv cs.NI 2020
Year: 2020

The consumer Internet of Things (IoT) space has experienced a significant rise in popularity in the recent years. From smart speakers, to baby monitors, and smart kettles and TVs, these devices are increasingly found in households around the world while users may be unaware of the risks associated with owning these devices. Previous work showed that these devices can threaten individuals' privacy and security by exposing information online to a large number of service providers and third party analytics services. Our analysis shows that many of these Internet connections (and the information they expose) are neither critical, nor even essential to the operation of these devices. However, automatically separating out critical from non-critical network traffic for an IoT device is nontrivial, and requires expert analysis based on manual experimentation in a controlled setting. In this paper, we investigate whether it is possible to automatically classify network traffic destinations as either critical (essential for devices to function properly) or not, hence allowing the home gateway to act as a selective firewall to block undesired, non-critical destinations. Our initial results demonstrate that some IoT devices contact destinations that are not critical to their operation, and there is no impact on device functionality if these destinations are blocked. We take the first steps towards designing and evaluating IoTrimmer, a framework for automated testing and analysis of various destinations contacted by devices, and selectively blocking the ones that do not impact device functionality.
Inference and Influence of Large-Scale Social Networks Using Snapshot Population Behaviour without Network Data
Venue: arXiv cs.SI 2020
Year: 2020

Population behaviours, such as voting and vaccination, depend on social networks. Social networks can differ depending on behaviour type and are typically hidden. However, we do often have large-scale behavioural data, albeit only snapshots taken at one timepoint. We present a method that jointly infers large-scale network structure and a networked model of human behaviour using only snapshot population behavioural data. This exploits the simplicity of a few parameter, geometric socio-demographic network model and a spin based model of behaviour. We illustrate, for the EU Referendum and two London Mayoral elections, how the model offers both prediction and the interpretation of our homophilic inclinations. Beyond offering the extraction of behaviour specific network structure from large-scale behavioural datasets, our approach yields a crude calculus linking inequalities and social preferences to behavioural outcomes. We give examples of potential network sensitive policies: how changes to income inequality, a social temperature and homophilic preferences might have reduced polarisation in a recent election.
Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world
Venue: arXiv cs.AI 2020
Year: 2020

In solving real-world problems like changing healthcare-seeking behaviors, designing interventions to improve downstream outcomes requires an understanding of the causal links within the system. Causal Bayesian Networks (BN) have been proposed as one such powerful method. In real-world applications, however, confidence in the results of BNs are often moderate at best. This is due in part to the inability to validate against some ground truth, as the DAG is not available. This is especially problematic if the learned DAG conflicts with pre-existing domain doctrine. At the policy level, one must justify insights generated by such analysis, preferably accompanying them with uncertainty estimation. Here we propose a causal extension to the datasheet concept proposed by Gebru et al (2018) to include approximate BN performance expectations for any given dataset. To generate the results for a prototype Causal Datasheet, we constructed over 30,000 synthetic datasets with properties mirroring characteristics of real data. We then recorded the results given by state-of-the-art structure learning algorithms. These results were used to populate the Causal Datasheet, and recommendations were automatically generated dependent on expected performance. As a proof of concept, we used our Causal Datasheet Generation Tool (CDG-T) to assign expected performance expectations to a maternal health survey we conducted in Uttar Pradesh, India.
On the parameterized complexity of 2-partitions
Venue: arXiv cs.CC 2020
Year: 2020

We give an FPT algorithm for deciding whether the vertex set a digraph $D$ can be partitioned into two disjoint sets $V_1,V_2$ such that the digraph $D[V_1]$ induced by $V_1$ has a vertex that can reach all other vertices by directed paths, the digraph $D[V_2]$ has no vertex of in-degree zero and $|V_i|\geq k_i$, where $k_1,k_2$ are part of the input. This settles an open problem from[1,4].
Passlab: A Password Security Tool for the Blue Team
Venue: arXiv cs.CR 2020
Year: 2020

If we wish to compromise some password-protected system as an attacker (i.e. a member of the red team), we have a large number of popular and actively-maintained tools to choose from in helping us to realise our goal. Password hash cracking hardware and software, online guessing tools, exploit frameworks, and a wealth of tools for helping us to perform reconnaissance on the target system are widely available. By comparison, if we wish to defend a password-protected system against such an attack (i.e. as a member of the blue team), we have comparatively few tools to choose from. In this research abstract, we present our work to date on Passlab, a password security tool designed to help system administrators take advantage of formal methods in order to make sensible and evidence-based security decisions using a clean and intuitive user interface.
Motion Control Simulations for a Magnetically-Coupled Bacterium and Robotic Arm
Venue: arXiv cs.RO 2020
Year: 2020

The demonstrations of micro-robotic systems in minimally invasive medicine include an individual or a swarm of microswimmer of various origin, artificial or biohybrid, often with an external computer-controlled electromagnetic field. There are several in vivo and in vitro control performances with artificial microswimmers but control of a bio-hybrid microswimmer using an open kinematic chain remains untouched. In this work, non-contact maneuvering control of a single magnetotactic bacterium cell is simulated. The results show that the proposed system is capable of adjusting the heading of the microswimmer moving at proximity to a 2D boundary under the guidance of the set-point tracking scheme. The performance of the coupled model and the sensitivity to control parameters are demonstrated with the help of a time-dependent error to the yaw-angle reference under the influence of PID with adaptive integral gain.
Dash: Scalable Hashing on Persistent Memory
Venue: arXiv cs.DB 2020
Year: 2020

Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules (DCPMM) further accelerates this trend. Many new hash table designs have been proposed, but most of them were based on emulation and perform sub-optimally on real PM. They were also piece-wise and partial solutions that side-step many important properties, in particular good scalability, high load factor and instant recovery. We present Dash, a holistic approach to building dynamic and scalable hash tables on real PM hardware with all the aforementioned properties. Based on Dash, we adapted two popular dynamic hashing schemes (extendible hashing and linear hashing). On a 24-core machine with Intel Optane DCPMM, we show that compared to state-of-the-art, Dash-enabled hash tables can achieve up to ~3.9X higher performance with up to over 90% load factor and an instant recovery time of 57ms regardless of data size.
A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication
Venue: arXiv cs.IT 2020
Year: 2020

Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.
Learning Shape Representations for Clothing Variations in Person Re-Identification
Venue: arXiv cs.CV 2020
Year: 2020

Person re-identification (re-ID) aims to recognize instances of the same person contained in multiple images taken across different cameras. Existing methods for re-ID tend to rely heavily on the assumption that both query and gallery images of the same person have the same clothing. Unfortunately, this assumption may not hold for datasets captured over long periods of time (e.g., weeks, months or years). To tackle the re-ID problem in the context of clothing changes, we propose a novel representation learning model which is able to generate a body shape feature representation without being affected by clothing color or patterns. We call our model the Color Agnostic Shape Extraction Network (CASE-Net). CASE-Net learns a representation of identity that depends only on body shape via adversarial learning and feature disentanglement. Due to the lack of large-scale re-ID datasets which contain clothing changes for the same person, we propose two synthetic datasets for evaluation. We create a rendered dataset SMPL-reID with different clothes patterns and a synthesized dataset Div-Market with different clothing color to simulate two types of clothing changes. The quantitative and qualitative results across 5 datasets (SMPL-reID, Div-Market, two benchmark re-ID datasets, a cross-modality re-ID dataset) confirm the robustness and superiority of our approach against several state-of-the-art approaches
Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes
Venue: arXiv cs.CV 2020
Year: 2020

We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments. The proposed method incorporates a two-stage approach where the initial stage clusters an unordered point cloud representation of the scene into room instances and wall instances using a deep neural network based voting approach. The subsequent stage estimates a closed perimeter, parameterized by a simple polygon, for each individual room by finding the shortest path along the predicted room and wall keypoints. The final floorplan is simply an assembly of all such room perimeters in the global co-ordinate system. The Scan2Plan pipeline produces accurate floorplans for complex layouts, is highly parallelizable and extremely efficient compared to existing methods. The voting module is trained only on synthetic data and evaluated on publicly available Structured3D and BKE datasets to demonstrate excellent qualitative and quantitative results outperforming state-of-the-art techniques.
Lazy Product Discovery in Huge Configuration Spaces
Venue: arXiv cs.SE 2020
Year: 2020

Highly-configurable software systems can have thousands of interdependent configuration options across different subsystems. In the resulting configuration space, discovering a valid product configuration for some selected options can be complex and error prone. The configuration space can be organized using a feature model, fragmented into smaller interdependent feature models reflecting the configuration options of each subsystem. We propose a method for lazy product discovery in large fragmented feature models with interdependent features. We formalize the method and prove its soundness and completeness. The evaluation explores an industrial-size configuration space. The results show that lazy product discovery has significant performance benefits compared to standard product discovery, which in contrast to our method requires all fragments to be composed to analyze the feature model. Furthermore, the method succeeds when more efficient, heuristics-based engines fail to find a valid configuration.
Developing a Multilingual Annotated Corpus of Misogyny and Aggression
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we discuss the development of a multilingual annotated corpus of misogyny and aggression in Indian English, Hindi, and Indian Bangla as part of a project on studying and automatically identifying misogyny and communalism on social media (the ComMA Project). The dataset is collected from comments on YouTube videos and currently contains a total of over 20,000 comments. The comments are annotated at two levels - aggression (overtly aggressive, covertly aggressive, and non-aggressive) and misogyny (gendered and non-gendered). We describe the process of data collection, the tagset used for annotation, and issues and challenges faced during the process of annotation. Finally, we discuss the results of the baseline experiments conducted to develop a classifier for misogyny in the three languages.
Hihooi: A Database Replication Middleware for Scaling Transactional Databases Consistently
Venue: arXiv cs.DB 2020
Year: 2020

With the advent of the Internet and Internet-connected devices, modern business applications can experience rapid increases as well as variability in transactional workloads. Database replication has been employed to scale performance and improve availability of relational databases but past approaches have suffered from various issues including limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. This paper presents Hihooi, a replication-based middleware system that is able to achieve workload scalability, strong consistency guarantees, and elasticity for existing transactional databases at a low cost. A novel replication algorithm enables Hihooi to propagate database modifications asynchronously to all replicas at high speeds, while ensuring that all replicas are consistent. At the same time, a fine-grained routing algorithm is used to load balance incoming transactions to available replicas in a consistent way. Our thorough experimental evaluation with several well-established benchmarks shows how Hihooi is able to achieve almost linear workload scalability for transactional databases.
A Numerical Transform of Random Forest Regressors corrects Systematically-Biased Predictions
Venue: arXiv cs.LG 2020
Year: 2020

Over the past decade, random forest models have become widely used as a robust method for high-dimensional data regression tasks. In part, the popularity of these models arises from the fact that they require little hyperparameter tuning and are not very susceptible to overfitting. Random forest regression models are comprised of an ensemble of decision trees that independently predict the value of a (continuous) dependent variable; predictions from each of the trees are ultimately averaged to yield an overall predicted value from the forest. Using a suite of representative real-world datasets, we find a systematic bias in predictions from random forest models. We find that this bias is recapitulated in simple synthetic datasets, regardless of whether or not they include irreducible error (noise) in the data, but that models employing boosting do not exhibit this bias. Here we demonstrate the basis for this problem, and we use the training data to define a numerical transformation that fully corrects it. Application of this transformation yields improved predictions in every one of the real-world and synthetic datasets evaluated in our study.
Recent Advances and Challenges in Task-oriented Dialog System
Venue: arXiv cs.CL 2020
Year: 2020

Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities. In this paper, we survey recent advances and challenges in task-oriented dialog systems. We also discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning to achieve better task-completion performance, and (3) integrating domain ontology knowledge into the dialog model. Besides, we review the recent progresses in dialog evaluation and some widely-used corpora. We believe that this survey, though incomplete, can shed a light on future research in task-oriented dialog systems.
On beta-Plurality Points in Spatial Voting Games
Venue: arXiv cs.CG 2020
Year: 2020

Let $V$ be a set of $n$ points in $\mathbb{R}^d$, called voters. A point $p\in \mathbb{R}^d$ is a plurality point for $V$ when the following holds: for every $q\in\mathbb{R}^d$ the number of voters closer to $p$ than to $q$ is at least the number of voters closer to $q$ than to $p$. Thus, in a vote where each $v\in V$ votes for the nearest proposal (and voters for which the proposals are at equal distance abstain), proposal $p$ will not lose against any alternative proposal $q$. For most voter sets a plurality point does not exist. We therefore introduce the concept of $β$-plurality points, which are defined similarly to regular plurality points except that the distance of each voter to $p$ (but not to $q$) is scaled by a factor $β$, for some constant $0<β\leq 1$. We investigate the existence and computation of $β$-plurality points, and obtain the following. * Define $β^*_d := \sup \{ β: \text{any finite multiset $V$ in $\mathbb{R}^d$ admits a $β$-plurality point} \}$. We prove that $β^*_2 = \sqrt{3}/2$, and that $1/\sqrt{d} \leq β^*_d \leq \sqrt{3}/2$ for all $d\geq 3$. * Define $β(p, V) := \sup \{ β: \text{$p$ is a $β$-plurality point for $V$}\}$. Given a voter set $V \in \mathbb{R}^2$, we provide an algorithm that runs in $O(n \log n)$ time and computes a point $p$ such that $β(p, V) \geq β^*_2$. Moreover, for $d\geq 2$ we can compute a point $p$ with $β(p,V) \geq 1/\sqrt{d}$ in $O(n)$ time. * Define $β(V) := \sup \{ β: \text{$V$ admits a $β$-plurality point}\}$. We present an algorithm that, given a voter set $V$ in $\mathbb{R}^d$, computes an $(1-\varepsilon)\cdot β(V)$ plurality point in time $O(\frac{n^2}{\varepsilon^{3d-2}} \cdot \log \frac{n}{\varepsilon^{d-1}} \cdot \log^2 \frac {1}{\varepsilon})$.
Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition
Venue: arXiv cs.CV 2020
Year: 2020

In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have achieved remarkable performance recently. However, current state-of-the-art methods for skeleton-based action recognition usually work on the assumption that the completely observed skeletons will be provided. This may be problematic to apply this assumption in real scenarios since there is always a possibility that captured skeletons are incomplete or noisy. In this work, we propose a skeleton-based action recognition method which is robust to noise information of given skeleton features. The key insight of our approach is to train a model by maximizing the mutual information between normal and noisy skeletons using a predictive coding manner. We have conducted comprehensive experiments about skeleton-based action recognition with defected skeletons using NTU-RGB+D and Kinetics-Skeleton datasets. The experimental results demonstrate that our approach achieves outstanding performance when skeleton samples are noised compared with existing state-of-the-art methods.
FakeYou! -- A Gamified Approach for Building and Evaluating Resilience Against Fake News
Venue: arXiv cs.CY 2020
Year: 2020

Nowadays fake news are heavily discussed in public and political debates. Even though the phenomenon of intended false information is rather old, misinformation reaches a new level with the rise of the internet and participatory platforms. Due to Facebook and Co., purposeful false information - often called fake news - can be easily spread by everyone. Because of a high data volatility and variety in content types (text, images,...) debunking of fake news is a complex challenge. This is especially true for automated approaches, which are prone to fail validating the veracity of the information. This work focuses on an a gamified approach to strengthen the resilience of consumers towards fake news. The game FakeYou motivates its players to critically analyze headlines regarding their trustworthiness. Further, the game follows a "learning by doing strategy": by generating own fake headlines, users should experience the concepts of convincing fake headline formulations. We introduce the game itself, as well as the underlying technical infrastructure. A first evaluation study shows, that users tend to use specific stylistic devices to generate fake news. Further, the results indicate, that creating good fakes and identifying correct headlines are challenging and hard to learn.
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.