OpenRaiser/Intern-Atlas huggingface dataset:
An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments
Venue: arXiv cs.HC 2021
Year: 2021

This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human-computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes.
Domain adaptation for person re-identification on new unlabeled data using AlignedReID++
Venue: arXiv cs.CV 2021
Year: 2021

In the world where big data reigns and there is plenty of hardware prepared to gather a huge amount of non structured data, data acquisition is no longer a problem. Surveillance cameras are ubiquitous and they capture huge numbers of people walking across different scenes. However, extracting value from this data is challenging, specially for tasks that involve human images, such as face recognition and person re-identification. Annotation of this kind of data is a challenging and expensive task. In this work we propose a domain adaptation workflow to allow CNNs that were trained in one domain to be applied to another domain without the need for new annotation of the target data. Our method uses AlignedReID++ as the baseline, trained using a Triplet loss with batch hard. Domain adaptation is done by using pseudo-labels generated using an unsupervised learning strategy. Our results show that domain adaptation techniques really improve the performance of the CNN when applied in the target domain.
Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events
Venue: arXiv cs.RO 2021
Year: 2021

This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic to perceptually degraded situations such as dark or foggy scenes. While some robots are equipped with dedicated physical sensors to detect necessary contact data for state estimation, some robots do not have dedicated contact sensors, and the addition of such sensors is non-trivial without redesigning the hardware. The trained network can estimate contact events on different terrains. The experiments show that a contact-aided invariant extended Kalman filter can generate accurate odometry trajectories compared to a state-of-the-art visual SLAM system, enabling robust proprioceptive odometry.
HetEng: An Improved Distributed Energy Efficient Clustering Scheme for Heterogeneous IoT Networks
Venue: arXiv cs.NI 2021
Year: 2021

Network lifetime is always a challenging issue in battery-powered networks due to the difficulty of recharging or replacing nodes in some scenarios. Clustering methods are a promising approach to tackle this challenge and prolong lifetime by efficiently distributing tasks among nodes in the cluster. The present study aimed to improve energy consumption in heterogeneous IoT devices using an energy-aware clustering method. In a heterogeneous IoT network, nodes (i.e., battery-powered IoT devices) can have a variety of energy profiles and communication capabilities. Most of the existing clustering algorithms have neglected the heterogeneity of energy capacity among nodes and assumed that they are of the same energy level. In this work, we present HetEng, a Cluster Head (CH) selection process that extended an existing clustering algorithm, named Smart-BEEM. To this end, we proposed a statistical approach that distributes energy consumption among highly energetic nodes in the network topology by constantly changing the CH role between the nodes based on their real energy levels (in joules). Experimental results showed that HetEng resulted in a 6.6% increase of alive nodes and 3% improvement in residual energy among the nodes in comparison with SmartBEEM. Moreover, our method reduced the total number of iterations by 1% on average.
Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge
Venue: arXiv cs.IT 2021
Year: 2021

By exploiting the superiority of non-orthogonal multiple access (NOMA), NOMA-aided mobile edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of wireless networks and sophisticated signal processing of NOMA, it is critical but challenging to design an efficient task offloading algorithm for NOMA-aided MEC, especially under a large number of devices. This paper presents an online algorithm that jointly optimizes offloading decisions and resource allocation to maximize the long-term system utility (i.e., a measure of throughput and fairness). Since the optimization variables are temporary coupled, we first apply Lyapunov technique to decouple the long-term stochastic optimization into a series of per-slot deterministic subproblems, which does not require any prior knowledge of network dynamics. Second, we propose to transform the non-convex per-slot subproblem of optimizing NOMA power allocation equivalently to a convex form by introducing a set of auxiliary variables, whereby the time-complexity is reduced from the exponential complexity to $\mathcal{O} (M^{3/2})$. The proposed algorithm is proved to be asymptotically optimal, even under partial knowledge of the device states at the base station. Simulation results validate the superiority of the proposed algorithm in terms of system utility, stability improvement, and the overhead reduction.
Pilot Assignment for Joint Uplink-Downlink Spectral Efficiency Enhancement in Massive MIMO Systems with Spatial Correlation
Venue: arXiv cs.IT 2021
Year: 2021

This paper proposes a flexible pilot assignment method to jointly optimize the uplink and downlink data transmission in multi-cell Massive multiple input multiple output (MIMO) systems with correlated Rayleigh fading channels. By utilizing a closed-form expression of the ergodic spectral efficiency (SE) achieved with maximum ratio processing, we formulate an optimization problem for maximizing the minimum weighted sum of the uplink and downlink SEs subject to the transmit powers and pilot assignment sets. This combinatiorial optimization problem is solved by two sequential algorithms: a heuristic pilot assignment is first proposed to obtain a good pilot reuse set and the data power control is then implemented. Numerical results manifest that the proposed algorithm converges fast to a better minimum sum SE per user than the algorithms in previous works.
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks
Venue: arXiv cs.CR 2021
Year: 2021

Machine Learning (ML) models are susceptible to evasion attacks. Evasion accuracy is typically assessed using aggregate evasion rate, and it is an open question whether aggregate evasion rate enables feature-level diagnosis on the effect of adversarial perturbations on evasive predictions. In this paper, we introduce a novel framework that harnesses explainable ML methods to guide high-fidelity assessment of ML evasion attacks. Our framework enables explanation-guided correlation analysis between pre-evasion perturbations and post-evasion explanations. Towards systematic assessment of ML evasion attacks, we propose and evaluate a novel suite of model-agnostic metrics for sample-level and dataset-level correlation analysis. Using malware and image classifiers, we conduct comprehensive evaluations across diverse model architectures and complementary feature representations. Our explanation-guided correlation analysis reveals correlation gaps between adversarial samples and the corresponding perturbations performed on them. Using a case study on explanation-guided evasion, we show the broader usage of our methodology for assessing robustness of ML models.
O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification
Venue: arXiv cs.CL 2021
Year: 2021

The PAN 2021 authorship verification (AV) challenge is part of a three-year strategy, moving from a cross-topic/closed-set AV task to a cross-topic/open-set AV task over a collection of fanfiction texts. In this work, we present a novel hybrid neural-probabilistic framework that is designed to tackle the challenges of the 2021 task. Our system is based on our 2020 winning submission, with updates to significantly reduce sensitivities to topical variations and to further improve the system's calibration by means of an uncertainty-adaptation layer. Our framework additionally includes an out-of-distribution detector (O2D2) for defining non-responses. Our proposed system outperformed all other systems that participated in the PAN 2021 AV task.
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
Venue: arXiv cs.LG 2021
Year: 2021

Although machine learning models typically experience a drop in performance on out-of-distribution data, accuracies on in- versus out-of-distribution data are widely observed to follow a single linear trend when evaluated across a testbed of models. Models that are more accurate on the out-of-distribution data relative to this baseline exhibit "effective robustness" and are exceedingly rare. Identifying such models, and understanding their properties, is key to improving out-of-distribution performance. We conduct a thorough empirical investigation of effective robustness during fine-tuning and surprisingly find that models pre-trained on larger datasets exhibit effective robustness during training that vanishes at convergence. We study how properties of the data influence effective robustness, and we show that it increases with the larger size, more diversity, and higher example difficulty of the dataset. We also find that models that display effective robustness are able to correctly classify 10% of the examples that no other current testbed model gets correct. Finally, we discuss several strategies for scaling effective robustness to the high-accuracy regime to improve the out-of-distribution accuracy of state-of-the-art models.
CCID5: An implementation of the BBR Congestion Control algorithm for DCCP and its impact over multi-path scenarios
Venue: arXiv cs.NI 2021
Year: 2021

Providing multi-connectivity services is an important goal for next generation wireless networks, where multiple access networks are available and need to be integrated into a coherent solution that efficiently supports both reliable and non reliable traffic. Based on virtual network interfaces and per path congestion controlled tunnels, the MP-DCCP based multiaccess aggregation framework presents as a novel solution that flexibly supports different path schedulers and congestion control algorithms as well as reordering modules. The framework has been implemented within the Linux kernel space and has been tested over different prototypes. Experimental results have shown that the overall performance strongly depends upon the congestion control algorithm used on the individual DCCP tunnels, denoted as CCID. In this paper, we present an implementation of the BBR (Bottleneck Bandwidth Round Trip propagation time) congestion control algorithm for DCCP in the Linux kernel. We show how BBR is integrated into the MP-DCCP multi-access framework and evaluate its performance over both single and multi-path environments. Our evaluation results show that BBR improves the performance compared to CCID2 for multi-path scenarios due to the faster response to changes in the available bandwidth, which reduces latency and increases performance, especially for unreliable traffic. the MP-DCCP framework code, including the new CCID5 is available as OpenSource.
Finite-image property of weighted tree automata over past-finite monotonic strong bimonoids
Venue: arXiv cs.FL 2021
Year: 2021

We consider weighted tree automata over strong bimonoids (for short: wta). A wta $\mathcal{A}$ has the finite-image property if its recognized weighted tree language $[\![\mathcal{A}]\!]$ has finite image; moreover, $\mathcal{A}$ has the preimage property if the preimage under $[\![\mathcal{A}]\!]$ of each element of the underlying strong bimonoid is a recognizable tree language. For each wta $\mathcal{A}$ over a past-finite monotonic strong bimonoid we prove the following results. In terms of $\mathcal{A}$'s structural properties, we characterize whether it has the finite-image property. We characterize those past-finite monotonic strong bimonoids such that for each wta $\mathcal{A}$ it is decidable whether $\mathcal{A}$ has the finite-image property. In particular, the finite-image property is decidable for wta over past-finite monotonic semirings. Moreover, we prove that $\mathcal{A}$ has the preimage property. All our results also hold for weighted string automata.
Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents
Venue: arXiv cs.RO 2021
Year: 2021

Navigating the world is a fundamental ability for any living entity. Accomplishing the same degree of freedom in technology has proven to be difficult. The brain is the only known mechanism capable of voluntary navigation, making neuroscience our best source of inspiration toward autonomy. Assuming that state representation is key, we explore the difference in how the brain and the machine represent the navigational state. Where Reinforcement Learning (RL) requires a monolithic state representation in accordance with the Markov property, Neural Representation of Euclidean Space (NRES) reflects navigational state via distributed activation patterns. We show how NRES-Oriented RL (neoRL) agents are possible before verifying our theoretical findings by experiments. Ultimately, neoRL agents are capable of behavior synthesis across state spaces -- allowing for decomposition of the problem into smaller spaces, alleviating the curse of dimensionality.
Optimally rescheduling jobs with a LIFO buffer
Venue: arXiv cs.DS 2021
Year: 2021

This paper considers single-machine scheduling problems in which a given solution, i.e. an ordered set of jobs, has to be improved as much as possible by re-sequencing the jobs. The need for rescheduling may arise in different contexts, e.g. due to changes in the job data or because of the local objective in a stage of a supply chain \red{that is} not aligned with the given sequence. A common production setting entails the movement of jobs (or parts) on a conveyor. This is reflected in our model by facilitating the re-sequencing of jobs via a buffer of limited capacity accessible by a LIFO policy. We consider the classical objective functions of total weighted completion time, maximum lateness and (weighted) number of late jobs and study their complexity. For three of these problems we present strictly polynomial-time dynamic programming algorithms, while for the case of minimizing the weighted number of late jobs NP-hardness is proven and a pseudo-polynomial algorithm is given.
Communication conditions in virtual acoustic scenes in an underground station
Venue: arXiv cs.SD 2021
Year: 2021

Underground stations are a common communication situation in towns: we talk with friends or colleagues, listen to announcements or shop for titbits while background noise and reverberation are challenging communication. Here, we perform an acoustical analysis of two communication scenes in an underground station in Munich and test speech intelligibility. The acoustical conditions were measured in the station and are compared to simulations in the real-time Simulated Open Field Environment (rtSOFE). We compare binaural room impulse responses measured with an artificial head in the station to modeled impulse responses for free-field auralization via 60 loudspeakers in the rtSOFE. We used the image source method to model early reflections and a set of multi-microphone recordings to model late reverberation. The first communication scene consists of 12 equidistant (1.6 m) horizontally spaced source positions around a listener, simulating different direction-dependent spatial unmasking conditions. The second scene mimics an approaching speaker across six radially spaced source positions (from 1 m to 10 m) with varying direct sound level and thus direct-to-reverberant energy. The acoustic parameters of the underground station show a moderate amount of reverberation (T30 in octave bands was between 2.3 s and 0.6 s and early-decay times between 1.46 s and 0.46 s). The binaural and energetic parameters of the auralization were in a close match to the measurement. Measured speech reception thresholds were within the error of the speech test, letting us to conclude that the auralized simulation reproduces acoustic and perceptually relevant parameters for speech intelligibility with high accuracy.
A preliminary approach to knowledge integrity risk assessment in Wikipedia projects
Venue: arXiv cs.CY 2021
Year: 2021

Wikipedia is one of the main repositories of free knowledge available today, with a central role in the Web ecosystem. For this reason, it can also be a battleground for actors trying to impose specific points of view or even spreading disinformation online. There is a growing need to monitor its "health" but this is not an easy task. Wikipedia exists in over 300 language editions and each project is maintained by a different community, with their own strengths, weaknesses and limitations. In this paper, we introduce a taxonomy of knowledge integrity risks across Wikipedia projects and a first set of indicators to assess internal risks related to community and content issues, as well as external threats such as the geopolitical and media landscape. On top of this taxonomy, we offer a preliminary analysis illustrating how the lack of editors' geographical diversity might represent a knowledge integrity risk. These are the first steps of a research project to build a Wikipedia Knowledge Integrity Risk Observatory.
eXtended Reality for Autism Interventions: The importance of Mediation and Sensory-Based Approaches
Venue: arXiv cs.HC 2021
Year: 2021

eXtended Reality (XR) autism research, ranging from Augmented Reality to Virtual Reality, focuses on socio-emotional abilities and high-functioning autism. However common autism interventions address the entire spectrum over social, sensory and mediation issues. To bridge the gap between autism research and real interventions, we compared existing literature on XR and autism with stakeholders' needs obtained by interviewing 34 skateholders, mainly practitioners. It allow us first to suggest XR use cases that could better support practitioners' interventions, and second to derive design guidelines accordingly. Findings demonstrate that collaborative XR sensory-based and mediation approaches would benefit the entire spectrum, and encourage to consider the overall intervention context when designing XR protocols.
Context-Aware Attention-Based Data Augmentation for POI Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorize historical patterns through user's trajectories for recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences the model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each check-in sequence and the decoder predicts the possible missing check-ins based on the encoded information. In order to learn time-aware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two real-world check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.
Close relatives (of Feedback Vertex Set), revisited
Venue: arXiv cs.DM 2021
Year: 2021

At IPEC 2020, Bergougnoux, Bonnet, Brettell, and Kwon showed that a number of problems related to the classic Feedback Vertex Set (FVS) problem do not admit a $2^{o(k \log k)} \cdot n^{\mathcal{O}(1)}$-time algorithm on graphs of treewidth at most $k$, assuming the Exponential Time Hypothesis. This contrasts with the $3^{k} \cdot k^{\mathcal{O}(1)} \cdot n$-time algorithm for FVS using the Cut&Count technique. During their live talk at IPEC 2020, Bergougnoux et al.~posed a number of open questions, which we answer in this work. - Subset Even Cycle Transversal, Subset Odd Cycle Transversal, Subset Feedback Vertex Set can be solved in time $2^{\mathcal{O}(k \log k)} \cdot n$ in graphs of treewidth at most $k$. This matches a lower bound for Even Cycle Transversal of Bergougnoux et al.~and improves the polynomial factor in some of their upper bounds. - Subset Feedback Vertex Set and Node Multiway Cut can be solved in time $2^{\mathcal{O}(k \log k)} \cdot n$, if the input graph is given as a clique-width expression of size $n$ and width $k$. - Odd Cycle Transversal can be solved in time $4^k \cdot k^{\mathcal{O}(1)} \cdot n$ if the input graph is given as a clique-width expression of size $n$ and width $k$. Furthermore, the existence of a constant $\varepsilon > 0$ and an algorithm performing this task in time $(4-\varepsilon)^k \cdot n^{\mathcal{O}(1)}$ would contradict the Strong Exponential Time Hypothesis.
AutoLAW: Augmented Legal Reasoning through Legal Precedent Prediction
Venue: arXiv cs.CL 2021
Year: 2021

This paper demonstrate how NLP can be used to address an unmet need of the legal community and increase access to justice. The paper introduces Legal Precedent Prediction (LPP), the task of predicting relevant passages from precedential court decisions given the context of a legal argument. To this end, the paper showcases a BERT model, trained on 530,000 examples of legal arguments made by U.S. federal judges, to predict relevant passages from precedential court decisions given the context of a legal argument. In 96% of unseen test examples the correct target passage is among the top-10 predicted passages. The same model is able to predict relevant precedent given a short summary of a complex and unseen legal brief, predicting the precedent that was actually cited by the brief's co-author, former U.S. Solicitor General and current U.S. Supreme Court Justice Elena Kagan.