OpenRaiser/Intern-Atlas huggingface dataset:
Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think
Venue: arXiv cs.CV 2021
Year: 2021

Deep-learning based computer vision models have proved themselves to be ground-breaking approaches to human activity recognition (HAR). However, most existing works are dedicated to improve the prediction accuracy through either creating new model architectures, increasing model complexity, or refining model parameters by training on larger datasets. Here, we propose an alternative idea, differing from existing work, to increase model accuracy and also to shape model predictions to align with human understandings through automatically creating higher-level summarizing labels for similar groups of human activities. First, we argue the importance and feasibility of constructing a hierarchical labeling system for human activity recognition. Then, we utilize the predictions of a black box HAR model to identify similarities between different activities. Finally, we tailor hierarchical clustering methods to automatically generate hierarchical trees of activities and conduct experiments. In this system, the activity labels on the same level will have a designed magnitude of accuracy and reflect a specific amount of activity details. This strategy enables a trade-off between the extent of the details in the recognized activity and the user privacy by masking some sensitive predictions; and also provides possibilities for the use of formerly prohibited invasive models in privacy-concerned scenarios. Since the hierarchy is generated from the machine's perspective, the predictions at the upper levels provide better accuracy, which is especially useful when there are too detailed labels in the training set that are rather trivial to the final prediction goal. Moreover, the analysis of the structure of these trees can reveal the biases in the prediction model and guide future data collection strategies.
SEMULATOR: Emulating the Dynamics of Crossbar Array-based Analog Neural System with Regression Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

As deep neural networks require tremendous amount of computation and memory, analog computing with emerging memory devices is a promising alternative to digital computing for edge devices. However, because of the increasing simulation time for analog computing system, it has not been explored. To overcome this issue, analytically approximated simulators are developed, but these models are inaccurate and narrow down the options for peripheral circuits for multiply-accumulate operation (MAC). In this sense, we propose a methodology, SEMULATOR (SiMULATOR by Emulating the analog computing block) which uses a deep neural network to emulate the behavior of crossbar-based analog computing system. With the proposed neural architecture, we experimentally and theoretically shows that it emulates a MAC unit for neural computation. In addition, the simulation time is incomparably reduced when it compared to the circuit simulators such as SPICE.
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks
Venue: arXiv cs.LG 2021
Year: 2021

Generative Adversarial Networks (GANs) are capable of generating convincing imitations of elements from a training set, but the distribution of elements in the training set affects to difficulty of properly training the GAN and the quality of the outputs it produces. This paper looks at six different GANs trained on different subsets of data from the game Lode Runner. The quality diversity algorithm MAP-Elites was used to explore the set of quality levels that could be produced by each GAN, where quality was defined as being beatable and having the longest solution path possible. Interestingly, a GAN trained on only 20 levels generated the largest set of diverse beatable levels while a GAN trained on 150 levels generated the smallest set of diverse beatable levels, thus challenging the notion that more is always better when training GANs.
Flying Robots for Safe and Efficient Parcel Delivery Within the COVID-19 Pandemic
Venue: arXiv cs.NI 2021
Year: 2021

The integration of small-scale Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITSs) will empower novel smart-city applications and services. After the unforeseen outbreak of the COVID-19 pandemic, the public demand for delivery services has multiplied. Mobile robotic systems inherently offer the potential for minimizing the amount of direct human-to-human interactions with the parcel delivery process. The proposed system-of-systems consists of various complex aspects such as assigning and distributing delivery jobs, establishing and maintaining reliable communication links between the vehicles, as well as path planning and mobility control. In this paper, we apply a system-level perspective for identifying key challenges and promising solution approaches for modeling, analysis, and optimization of UAV-aided parcel delivery. We present a system-of-systems model for UAV-assisted parcel delivery to cope with higher capacity requirements induced by the COVID-19. To demonstrate the benefits of hybrid vehicular delivery, we present a case study focusing on the prioritization of time-critical deliveries such as medical goods. The results further confirm that the capacity of traditional delivery fleets can be upgraded with drone usage. Furthermore, we observe that the delay incurred by prioritizing time-critical deliveries can be compensated with drone deployment. Finally, centralized and decentralized communication approaches for data transmission inside hybrid delivery fleets are compared.
IntentNet: Learning to Predict Intention from Raw Sensor Data
Venue: arXiv cs.RO 2021
Year: 2021

In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high-level behaviors as well as continuous trajectories describing future motion. In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
Joint Inter-path and Intra-path Multiplexing for Terahertz Widely-spaced Multi-subarray Hybrid Beamforming Systems
Venue: arXiv cs.IT 2021
Year: 2021

Terahertz (THz) communications with multi-GHz bandwidth are envisioned as a key technology for 6G systems. Ultra-massive (UM) MIMO with hybrid beamforming architectures are widely investigated to provide a high array gain to overcome the huge propagation loss. However, most of the existing hybrid beamforming architectures can only utilize the multiplexing offered by the multipath components, i.e., inter-path multiplexing, which is very limited due to the spatially sparse THz channel. In this paper, a widely-spaced multi-subarray (WSMS) hybrid beamforming architecture is proposed, which improves the multiplexing gain by exploiting a new type of intra-path multiplexing provided by the spherical-wave propagation among k widely-spaced subarrays, in addition to the inter-path multiplexing. The resulting multiplexing gain of WSMS architecture is k times of the existing architectures. To harness WSMS hybrid beamforming, a novel design problem is formulated by optimizing the number of subarrays, subarray spacing, and hybrid beamforming matrices to maximize the spectral efficiency, which is decomposed into two subproblems. An optimal closed-form solution is derived for the first hybrid beamforming subproblem, while a dominant-line-of-sight-relaxation algorithm is proposed for the second array configuration subproblem. Extensive simulation results demonstrate that the WSMS architecture and proposed algorithms substantially enhance the spectral efficiency and energy efficiency.
Bias in ontologies -- a preliminary assessment
Venue: arXiv cs.AI 2021
Year: 2021

Logical theories in the form of ontologies and similar artefacts in computing and IT are used for structuring, annotating, and querying data, among others, and therewith influence data analytics regarding what is fed into the algorithms. Algorithmic bias is a well-known notion, but what does bias mean in the context of ontologies that provide a structuring mechanism for an algorithm's input? What are the sources of bias there and how would they manifest themselves in ontologies? We examine and enumerate types of bias relevant for ontologies, and whether they are explicit or implicit. These eight types are illustrated with examples from extant production-level ontologies and samples from the literature. We then assessed three concurrently developed COVID-19 ontologies on bias and detected different subsets of types of bias in each one, to a greater or lesser extent. This first characterisation aims contribute to a sensitisation of ethical aspects of ontologies primarily regarding representation of information and knowledge.
Automatic Differentiation via Effects and Handlers: An Implementation in Frank
Venue: arXiv cs.PL 2021
Year: 2021

Automatic differentiation (AD) is an important family of algorithms which enables derivative based optimization. We show that AD can be simply implemented with effects and handlers by doing so in the Frank language. By considering how our implementation behaves in Frank's operational semantics, we show how our code performs the dynamic creation of programs during evaluation.
Improving type information inferred by decompilers with supervised machine learning
Venue: arXiv cs.SE 2021
Year: 2021

In software reverse engineering, decompilation is the process of recovering source code from binary files. Decompilers are used when it is necessary to understand or analyze software for which the source code is not available. Although existing decompilers commonly obtain source code with the same behavior as the binaries, that source code is usually hard to interpret and certainly differs from the original code written by the programmer. Massive codebases could be used to build supervised machine learning models aimed at improving existing decompilers. In this article, we build different classification models capable of inferring the high-level type returned by functions, with significantly higher accuracy than existing decompilers. We automatically instrument C source code to allow the association of binary patterns with their corresponding high-level constructs. A dataset is created with a collection of real open-source applications plus a huge number of synthetic programs. Our system is able to predict function return types with a 79.1% F1-measure, whereas the best decompiler obtains a 30% F1-measure. Moreover, we document the binary patterns used by our classifier to allow their addition in the implementation of existing decompilers.
The (in)security of some recently proposed lightweight key distribution schemes
Venue: arXiv cs.CR 2021
Year: 2021

Two recently published papers propose some very simple key distribution schemes designed to enable two or more parties to establish a shared secret key with the aid of a third party. Unfortunately, as we show, most of the schemes are inherently insecure and all are incompletely specified - moreover, claims that the schemes are inherently lightweight are shown to be highly misleading. We also briefly critique a somewhat related very recent paper by the same authors that uses similar techniques to achieve what are claimed to be secure multiparty computations.
On the curvature extrema of special cubic Bézier curves
Venue: arXiv cs.CG 2021
Year: 2021

It is proved that special cubic Bézier curves, generated from quadratic curves by the use of a scalar parameter, have at most one local curvature extremum in the $(0,1)$ parameter interval.
Shielding Atari Games with Bounded Prescience
Venue: arXiv cs.AI 2021
Year: 2021

Deep reinforcement learning (DRL) is applied in safety-critical domains such as robotics and autonomous driving. It achieves superhuman abilities in many tasks, however whether DRL agents can be shown to act safely is an open problem. Atari games are a simple yet challenging exemplar for evaluating the safety of DRL agents and feature a diverse portfolio of game mechanics. The safety of neural agents has been studied before using methods that either require a model of the system dynamics or an abstraction; unfortunately, these are unsuitable to Atari games because their low-level dynamics are complex and hidden inside their emulator. We present the first exact method for analysing and ensuring the safety of DRL agents for Atari games. Our method only requires access to the emulator. First, we give a set of 43 properties that characterise "safe behaviour" for 30 games. Second, we develop a method for exploring all traces induced by an agent and a game and consider a variety of sources of game non-determinism. We observe that the best available DRL agents reliably satisfy only very few properties; several critical properties are violated by all agents. Finally, we propose a countermeasure that combines a bounded explicit-state exploration with shielding. We demonstrate that our method improves the safety of all agents over multiple properties.
Video Relation Detection with Trajectory-aware Multi-modal Features
Venue: arXiv cs.CV 2021
Year: 2021

Video relation detection problem refers to the detection of the relationship between different objects in videos, such as spatial relationship and action relationship. In this paper, we present video relation detection with trajectory-aware multi-modal features to solve this task. Considering the complexity of doing visual relation detection in videos, we decompose this task into three sub-tasks: object detection, trajectory proposal and relation prediction. We use the state-of-the-art object detection method to ensure the accuracy of object trajectory detection and multi-modal feature representation to help the prediction of relation between objects. Our method won the first place on the video relation detection task of Video Relation Understanding Grand Challenge in ACM Multimedia 2020 with 11.74\% mAP, which surpasses other methods by a large margin.
Onion under Microscope: An in-depth analysis of the Tor network
Venue: arXiv cs.SI 2021
Year: 2021

Tor is an anonymity network that allows offering and accessing various kinds of resources, known as hidden services, while guaranteeing sender and receiver anonymity. The Tor web is the set of web resources that exist on the Tor network, and Tor websites are part of the so-called dark web. Recent research works have evaluated Tor security, evolution over time, and thematic organization. Nevertheless, few information are available about the structure of the graph defined by the network of Tor websites. The limited number of Tor entry points that can be used to crawl the network renders the study of this graph far from being simple. In this paper we aim at better characterizing the Tor Web by analyzing three crawling datasets collected over a five-month time frame. On the one hand, we extensively study the global properties of the Tor Web, considering two different graph representations and verifying the impact of Tor's renowned volatility. We present an in depth investigation of the key features of the Tor Web graph showing what makes it different from the surface Web graph. On the other hand, we assess the relationship between contents and structural features. We analyse the local properties of the Tor Web to better characterize the role different services play in the network and to understand to which extent topological features are related to the contents of a service.
SAR and Optical data fusion based on Anisotropic Diffusion with PCA and Classification using Patch-based with LBP
Venue: arXiv cs.CV 2021
Year: 2021

SAR (VV and VH polarization) and optical data are widely used in image fusion to use the complimentary information of each other and to obtain the better-quality image (in terms of spatial and spectral features) for the improved classification results. This paper uses anisotropic diffusion with PCA for the fusion of SAR and optical data and patch-based SVM Classification with LBP (LBP-PSVM). Fusion results with VV polarization performed better than VH polarization using considered fusion method. For classification, the performance of LBP-PSVM using S1 (VV) with S2, S1 (VH) with S2 is compared with SVM classifier (without patch) and PSVM classifier (with patch), respectively. Classification results suggests that the LBP-PSVM classifier is more effective in comparison to SVM and PSVM classifiers for considered data.
Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches
Venue: arXiv cs.LG 2021
Year: 2021

The high penetration of volatile renewable energy sources such as solar make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open-source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14).
Non-Convex Compressed Sensing with Training Data
Venue: arXiv cs.LG 2021
Year: 2021

Efficient algorithms for the sparse solution of under-determined linear systems $Ax = b$ are known for matrices $A$ satisfying suitable assumptions like the restricted isometry property (RIP). Without such assumptions little is known and without any assumptions on $A$ the problem is $NP$-hard. A common approach is to replace $\ell_1$ by $\ell_p$ minimization for $0 < p < 1$, which is no longer convex and typically requires some form of local initial values for provably convergent algorithms. In this paper, we consider an alternative, where instead of suitable initial values we are provided with extra training problems $Ax = B_l$, $l=1, \dots, p$ that are related to our compressed sensing problem. They allow us to find the solution of the original problem $Ax = b$ with high probability in the range of a one layer linear neural network with comparatively few assumptions on the matrix $A$.
Communication Aid for Non-English Speaking Newcomers
Venue: arXiv cs.HC 2021
Year: 2021

This research work is intended to assess the usability of Pictogram symbols and other visual symbols in an audio-visual strategy to facilitate and enhance the use and learning of English as an additional language for Arabic-speaking Syrian refugees, with a potential for generalizing the process to speakers from other linguistic backgrounds. The adopted software for the project is PICTOPAGES, a versatile tool with 2,200 symbols, 78 animated symbols, and the potential for customization with photographs, thus augmenting its capability for personalization and relevance. While PICTOPAGES is the intended basis for this research, the concept and software will be adapted and modified as may be required. PICTOPAGES includes text, recorded speech, and symbols and is currently available for iPad. In the future, it may be adapted for use on iPhone. A preliminary design using PICTOPAGES has been created for this research. The focus group includes, but is not limited to, newcomers who may have limited to no English skills, limited resources, limited education, and potentially limited literacy in their native language, and perhaps high levels of distraction and frustration related to their recent experiences. Enhanced communication capability and confidence should enhance the participants employment potential. Extensive interaction with respect to communication requirements, selection or development of readily understandable symbols, and real-world testing would be undertaken with an intended user group. A potential subset of the focus group could involve members of the refugee community that, in addition to English language limitations, also have developmental or acquired disabilities that affect their ability to communicate verbally (per the original intent of the software).
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
Venue: arXiv cs.CL 2021
Year: 2021

Pretrained multilingual text encoders based on neural Transformer architectures, such as multilingual BERT (mBERT) and XLM, have achieved strong performance on a myriad of language understanding tasks. Consequently, they have been adopted as a go-to paradigm for multilingual and cross-lingual representation learning and transfer, rendering cross-lingual word embeddings (CLWEs) effectively obsolete. However, questions remain to which extent this finding generalizes 1) to unsupervised settings and 2) for ad-hoc cross-lingual IR (CLIR) tasks. Therefore, in this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a large number of language pairs. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR -- a setup with no relevance judgments for IR-specific fine-tuning -- pretrained encoders fail to significantly outperform models based on CLWEs. For sentence-level CLIR, we demonstrate that state-of-the-art performance can be achieved. However, the peak performance is not met using the general-purpose multilingual text encoders `off-the-shelf', but rather relying on their variants that have been further specialized for sentence understanding tasks.
Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing
Venue: arXiv cs.HC 2021
Year: 2021

The recent growth of digital interventions for mental well-being prompts a call-to-arms to explore the delivery of personalised recommendations from a user's perspective. In a randomised placebo study with a two-way factorial design, we analysed the difference between an autonomous user experience as opposed to personalised guidance, with respect to both users' preference and their actual usage of a mental well-being app. Furthermore, we explored users' preference in sharing their data for receiving personalised recommendations, by juxtaposing questionnaires and mobile sensor data. Interestingly, self-reported results indicate the preference for personalised guidance, whereas behavioural data suggests that a blend of autonomous choice and recommended activities results in higher engagement. Additionally, although users reported a strong preference of filling out questionnaires instead of sharing their mobile data, the data source did not have any impact on the actual app use. We discuss the implications of our findings and provide takeaways for designers of mental well-being applications.