OpenRaiser/Intern-Atlas huggingface dataset:
Generating Human-Like Movement: A Comparison Between Two Approaches Based on Environmental Features
Venue: arXiv cs.AI 2020
Year: 2020

Modelling realistic human behaviours in simulation is an ongoing challenge that resides between several fields like social sciences, philosophy, and artificial intelligence. Human movement is a special type of behaviour driven by intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Services available online and offline do not normally consider the environment when planning a path, which is decisive especially on a leisure trip. Two novel algorithms have been presented to generate human-like trajectories based on environmental features. The Attraction-Based A* algorithm includes in its computation information from the environmental features meanwhile, the Feature-Based A* algorithm also injects information from the real trajectories in its computation. The human-likeness aspect has been tested by a human expert judging the final generated trajectories as realistic. This paper presents a comparison between the two approaches in some key metrics like efficiency, efficacy, and hyper-parameters sensitivity. We show how, despite generating trajectories that are closer to the real one according to our predefined metrics, the Feature-Based A* algorithm fall short in time efficiency compared to the Attraction-Based A* algorithm, hindering the usability of the model in the real world.
Betrayed by the Guardian: Security and Privacy Risks of Parental Control Solutions
Venue: arXiv cs.CR 2020
Year: 2020

For parents of young children and adolescents, the digital age has introduced many new challenges, including excessive screen time, inappropriate online content, cyber predators, and cyberbullying. To address these challenges, many parents rely on numerous parental control solutions on different platforms, including parental control network devices (e.g., WiFi routers) and software applications on mobile devices and laptops. While these parental control solutions may help digital parenting, they may also introduce serious security and privacy risks to children and parents, due to their elevated privileges and having access to a significant amount of privacy-sensitive data. In this paper, we present an experimental framework for systematically evaluating security and privacy issues in parental control software and hardware solutions. Using the developed framework, we provide the first comprehensive study of parental control tools on multiple platforms including network devices, Windows applications, Chrome extensions and Android apps. Our analysis uncovers pervasive security and privacy issues that can lead to leakage of private information, and/or allow an adversary to fully control the parental control solution, and thereby may directly aid cyberbullying and cyber predators.
Trembling-Hand Perfection and Correlation in Sequential Games
Venue: arXiv cs.GT 2020
Year: 2020

We initiate the study of trembling-hand perfection in sequential (i.e., extensive-form) games with correlation. We introduce the extensive-form perfect correlated equilibrium (EFPCE) as a refinement of the classical extensive-form correlated equilibrium (EFCE) that amends its weaknesses off the equilibrium path. This is achieved by accounting for the possibility that players may make mistakes while following recommendations independently at each information set of the game. After providing an axiomatic definition of EFPCE, we show that one always exists since any perfect (Nash) equilibrium constitutes an EFPCE, and that it is a refinement of EFCE, as any EFPCE is also an EFCE. Then, we prove that, surprisingly, computing an EFPCE is not harder than finding an EFCE, since the problem can be solved in polynomial time for general n-player extensive-form games (also with chance). This is achieved by formulating the problem as that of finding a limit solution (as $ε\rightarrow 0$) to a suitably defined trembling LP parametrized by $ε$, featuring exponentially many variables and polynomially many constraints. To this end, we show how a recently developed polynomial-time algorithm for trembling LPs can be adapted to deal with problems having an exponential number of variables. This calls for the solution of a sequence of (non-trembling) LPs with exponentially many variables and polynomially many constraints, which is possible in polynomial time by applying an ellipsoid against hope approach.
TEEMon: A continuous performance monitoring framework for TEEs
Venue: arXiv cs.CR 2020
Year: 2020

Trusted Execution Environments (TEEs), such as Intel Software Guard eXtensions (SGX), are considered as a promising approach to resolve security challenges in clouds. TEEs protect the confidentiality and integrity of application code and data even against privileged attackers with root and physical access by providing an isolated secure memory area, i.e., enclaves. The security guarantees are provided by the CPU, thus even if system software is compromised, the attacker can never access the enclave's content. While this approach ensures strong security guarantees for applications, it also introduces a considerable runtime overhead in part by the limited availability of protected memory (enclave page cache). Currently, only a limited number of performance measurement tools for TEE-based applications exist and none offer performance monitoring and analysis during runtime. This paper presents TEEMon, the first continuous performance monitoring and analysis tool for TEE-based applications. TEEMon provides not only fine-grained performance metrics during runtime, but also assists the analysis of identifying causes of performance bottlenecks, e.g., excessive system calls. Our approach smoothly integrates with existing open-source tools (e.g., Prometheus or Grafana) towards a holistic monitoring solution, particularly optimized for systems deployed through Docker containers or Kubernetes and offers several dedicated metrics and visualizations. Our evaluation shows that TEEMon's overhead ranges from 5% to 17%.
Learning from User Interactions with Rankings: A Unification of the Field
Venue: arXiv cs.IR 2020
Year: 2020

Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings they produce should place the most relevant or preferred items at the top of the ranking. Learning to rank is a field within machine learning that covers methods which optimize ranking systems w.r.t. this goal. Traditional supervised learning to rank methods utilize expert-judgements to evaluate and learn, however, in many situations such judgements are impossible or infeasible to obtain. As a solution, methods have been introduced that perform learning to rank based on user clicks instead. The difficulty with clicks is that they are not only affected by user preferences, but also by what rankings were displayed. Therefore, these methods have to prevent being biased by other factors than user preference. This thesis concerns learning to rank methods based on user clicks and specifically aims to unify the different families of these methods. As a whole, the second part of this thesis proposes a framework that bridges many gaps between areas of online, counterfactual, and supervised learning to rank. It has taken approaches, previously considered independent, and unified them into a single methodology for widely applicable and effective learning to rank from user clicks.
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
Venue: arXiv cs.LG 2020
Year: 2020

We demonstrate the ability of hybrid regularization methods to automatically avoid the double descent phenomenon arising in the training of random feature models (RFM). The hallmark feature of the double descent phenomenon is a spike in the regularization gap at the interpolation threshold, i.e. when the number of features in the RFM equals the number of training samples. To close this gap, the hybrid method considered in our paper combines the respective strengths of the two most common forms of regularization: early stopping and weight decay. The scheme does not require hyperparameter tuning as it automatically selects the stopping iteration and weight decay hyperparameter by using generalized cross-validation (GCV). This also avoids the necessity of a dedicated validation set. While the benefits of hybrid methods have been well-documented for ill-posed inverse problems, our work presents the first use case in machine learning. To expose the need for regularization and motivate hybrid methods, we perform detailed numerical experiments inspired by image classification. In those examples, the hybrid scheme successfully avoids the double descent phenomenon and yields RFMs whose generalization is comparable with classical regularization approaches whose hyperparameters are tuned optimally using the test data. We provide our MATLAB codes for implementing the numerical experiments in this paper at https://github.com/EmoryMLIP/HybridRFM.
Approximate Trace Reconstruction
Venue: arXiv cs.DS 2020
Year: 2020

In the usual trace reconstruction problem, the goal is to exactly reconstruct an unknown string of length $n$ after it passes through a deletion channel many times independently, producing a set of traces (i.e., random subsequences of the string). We consider the relaxed problem of approximate reconstruction. Here, the goal is to output a string that is close to the original one in edit distance while using much fewer traces than is needed for exact reconstruction. We present several algorithms that can approximately reconstruct strings that belong to certain classes, where the estimate is within $n/\mathrm{polylog}(n)$ edit distance, and where we only use $\mathrm{polylog}(n)$ traces (or sometimes just a single trace). These classes contain strings that require a linear number of traces for exact reconstruction and which are quite different from a typical random string. From a technical point of view, our algorithms approximately reconstruct consecutive substrings of the unknown string by aligning dense regions of traces and using a run of a suitable length to approximate each region. To complement our algorithms, we present a general black-box lower bound for approximate reconstruction, building on a lower bound for distinguishing between two candidate input strings in the worst case. In particular, this shows that approximating to within $n^{1/3 - δ}$ edit distance requires $n^{1 + 3δ/2}/\mathrm{polylog}(n)$ traces for $0< δ< 1/3$ in the worst case.
Towards Neurohaptics: Brain-Computer Interfaces for Decoding Intuitive Sense of Touch
Venue: arXiv cs.HC 2020
Year: 2020

Noninvasive brain-computer interface (BCI) is widely used to recognize users' intentions. Especially, BCI related to tactile and sensation decoding could provide various effects on many industrial fields such as manufacturing advanced touch displays, controlling robotic devices, and more immersive virtual reality or augmented reality. In this paper, we introduce haptic and sensory perception-based BCI systems called neurohaptics. It is a preliminary study for a variety of scenarios using actual touch and touch imagery paradigms. We designed a novel experimental environment and a device that could acquire brain signals under touching designated materials to generate natural touch and texture sensations. Through the experiment, we collected the electroencephalogram (EEG) signals with respect to four different texture objects. Seven subjects were recruited for the experiment and evaluated classification performances using machine learning and deep learning approaches. Hence, we could confirm the feasibility of decoding actual touch and touch imagery on EEG signals to develop practical neurohaptics.
SenSeNet: Neural Keyphrase Generation with Document Structure
Venue: arXiv cs.CL 2020
Year: 2020

Keyphrase Generation (KG) is the task of generating central topics from a given document or literary work, which captures the crucial information necessary to understand the content. Documents such as scientific literature contain rich meta-sentence information, which represents the logical-semantic structure of the documents. However, previous approaches ignore the constraints of document logical structure, and hence they mistakenly generate keyphrases from unimportant sentences. To address this problem, we propose a new method called Sentence Selective Network (SenSeNet) to incorporate the meta-sentence inductive bias into KG. In SenSeNet, we use a straight-through estimator for end-to-end training and incorporate weak supervision in the training of the sentence selection module. Experimental results show that SenSeNet can consistently improve the performance of major KG models based on seq2seq framework, which demonstrate the effectiveness of capturing structural information and distinguishing the significance of sentences in KG task.
Delay Differential Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Neural ordinary differential equations (NODEs) treat computation of intermediate feature vectors as trajectories of ordinary differential equation parameterized by a neural network. In this paper, we propose a novel model, delay differential neural networks (DDNN), inspired by delay differential equations (DDEs). The proposed model considers the derivative of the hidden feature vector as a function of the current feature vector and past feature vectors (history). The function is modelled as a neural network and consequently, it leads to continuous depth alternatives to many recent ResNet variants. We propose two different DDNN architectures, depending on the way current and past feature vectors are considered. For training DDNNs, we provide a memory-efficient adjoint method for computing gradients and back-propagate through the network. DDNN improves the data efficiency of NODE by further reducing the number of parameters without affecting the generalization performance. Experiments conducted on synthetic and real-world image classification datasets such as Cifar10 and Cifar100 show the effectiveness of the proposed models.
VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge
Venue: arXiv cs.SD 2020
Year: 2020

We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020. The goal of this challenge was to assess how well current speaker recognition technology is able to diarise and recognize speakers in unconstrained or `in the wild' data. It consisted of: (i) a publicly available speaker recognition and diarisation dataset from YouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a virtual public challenge and workshop held at Interspeech 2020. This paper outlines the challenge, and describes the baselines, methods used, and results. We conclude with a discussion of the progress over the first installment of the challenge.
AIR-FI: Generating Covert Wi-Fi Signals from Air-Gapped Computers
Venue: arXiv cs.CR 2020
Year: 2020

In this paper, we show that attackers can exfiltrate data from air-gapped computers via Wi-Fi signals. Malware in a compromised air-gapped computer can generate signals in the Wi-Fi frequency bands. The signals are generated through the memory buses - no special hardware is required. Sensitive data can be modulated and secretly exfiltrated on top of the signals. We show that nearby Wi-Fi capable devices (e.g., smartphones, laptops, IoT devices) can intercept these signals, decode them, and send them to the attacker over the Internet. To extract the signals, we utilize the physical layer information exposed by the Wi-Fi chips. We implement the transmitter and receiver and discuss design considerations and implementation details. We evaluate this covert channel in terms of bandwidth and distance and present a set of countermeasures. Our evaluation shows that data can be exfiltrated from air-gapped computers to nearby Wi-Fi receivers located a distance of several meters away.
GAN-based Recommendation with Positive-Unlabeled Sampling
Venue: arXiv cs.IR 2020
Year: 2020

Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products. In this work, we propose a Generative Adversarial Network based recommendation framework using a positive-unlabeled sampling strategy. Specifically, we utilize the generator to learn the continuous distribution of user-item tuples and design the discriminator to be a binary classifier that outputs the relevance score between each user and each item. Meanwhile, positive-unlabeled sampling is applied in the learning procedure of the discriminator. Theoretical bounds regarding positive-unlabeled sampling and optimalities of convergence for the discriminators and the generators are provided. We show the effectiveness and efficiency of our framework on three publicly accessible data sets with eight ranking-based evaluation metrics in comparison with thirteen popular baselines.
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach. To prevent from updating the parameters for an abnormally high number of clicks over some targeted items (mainly due to bots), we introduce an upper and a lower threshold on the number of updates for each user. These thresholds are estimated over the distribution of the number of blocks in the training set. The thresholds affect the decision of RS and imply a shift over the distribution of items that are shown to the users. Furthermore, we provide a convergence analysis of both algorithms and demonstrate their practical efficiency over six large-scale collections, both regarding different ranking measures and computational time.
NP-ODE: Neural Process Aided Ordinary Differential Equations for Uncertainty Quantification of Finite Element Analysis
Venue: arXiv cs.LG 2020
Year: 2020

Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems. Despite its strength and accuracy, the limitations of FEA can be summarized into two aspects: a) running high-fidelity FEA often requires significant computational cost and consumes a large amount of time; b) FEA is a deterministic method that is insufficient for uncertainty quantification (UQ) when modeling complex systems with various types of uncertainties. In this paper, a physics-informed data-driven surrogate model, named Neural Process Aided Ordinary Differential Equation (NP-ODE), is proposed to model the FEA simulations and capture both input and output uncertainties. To validate the advantages of the proposed NP-ODE, we conduct experiments on both the simulation data generated from a given ordinary differential equation and the data collected from a real FEA platform for tribocorrosion. The performances of the proposed NP-ODE and several benchmark methods are compared. The results show that the proposed NP-ODE outperforms benchmark methods. The NP-ODE method realizes the smallest predictive error as well as generates the most reasonable confidence interval having the best coverage on testing data points.
Discriminative Pre-training for Low Resource Title Compression in Conversational Grocery
Venue: arXiv cs.CL 2020
Year: 2020

The ubiquity of smart voice assistants has made conversational shopping commonplace. This is especially true for low consideration segments like grocery. A central problem in conversational grocery is the automatic generation of short product titles that can be read out fast during a conversation. Several supervised models have been proposed in the literature that leverage manually labeled datasets and additional product features to generate short titles automatically. However, obtaining large amounts of labeled data is expensive and most grocery item pages are not as feature-rich as other categories. To address this problem we propose a pre-training based solution that makes use of unlabeled data to learn contextual product representations which can then be fine-tuned to obtain better title compression even in a low resource setting. We use a self-attentive BiLSTM encoder network with a time distributed softmax layer for the title compression task. We overcome the vocabulary mismatch problem by using a hybrid embedding layer that combines pre-trained word embeddings with trainable character level convolutions. We pre-train this network as a discriminator on a replaced-token detection task over a large number of unlabeled grocery product titles. Finally, we fine tune this network, without any modifications, with a small labeled dataset for the title compression task. Experiments on Walmart's online grocery catalog show our model achieves performance comparable to state-of-the-art models like BERT and XLNet. When fine tuned on all of the available training data our model attains an F1 score of 0.8558 which lags the best performing model, BERT-Base, by 2.78% and XLNet by 0.28% only, while using 55 times lesser parameters than both. Further, when allowed to fine tune on 5% of the training data only, our model outperforms BERT-Base by 24.3% in F1 score.
Estimating Spread of Contact-Based Contagions in a Population Through Sub-Sampling
Venue: arXiv cs.SI 2020
Year: 2020

Physical contacts result in the spread of various phenomena such as viruses, gossips, ideas, packages and marketing pamphlets across a population. The spread depends on how people move and co-locate with each other, or their mobility patterns. How far such phenomena spread has significance for both policy making and personal decision making, e.g., studying the spread of COVID-19 under different intervention strategies such as wearing a mask. In practice, mobility patterns of an entire population is never available, and we usually have access to location data of a subset of individuals. In this paper, we formalize and study the problem of estimating the spread of a phenomena in a population, given that we only have access to sub-samples of location visits of some individuals in the population. We show that simple solutions such as estimating the spread in the sub-sample and scaling it to the population, or more sophisticated solutions that rely on modeling location visits of individuals do not perform well in practice, the former because it ignores contacts between unobserved individuals and sampled ones and the latter because it yields inaccurate modeling of co-locations. Instead, we directly model the co-locations between the individuals. We introduce PollSpreader and PollSusceptible, two novel approaches that model the co-locations between individuals using a contact network, and infer the properties of the contact network using the subsample to estimate the spread of the phenomena in the entire population. We show that our estimates provide an upper bound and a lower bound on the spread of the disease in expectation. Finally, using a large high-resolution real-world mobility dataset, we experimentally show that our estimates are accurate, while other methods that do not correctly account for co-locations between individuals result in wrong observations (e.g, premature herd-immunity).