OpenRaiser/Intern-Atlas huggingface dataset:
Validation of Abstract Side-Channel Models for Computer Architectures
Venue: arXiv cs.CR 2020
Year: 2020

Observational models make tractable the analysis of information flow properties by providing an abstraction of side channels. We introduce a methodology and a tool, Scam-V, to validate observational models for modern computer architectures. We combine symbolic execution, relational analysis, and different program generation techniques to generate experiments and validate the models. An experiment consists of a randomly generated program together with two inputs that are observationally equivalent according to the model under the test. Validation is done by checking indistinguishability of the two inputs on real hardware by executing the program and analyzing the side channel. We have evaluated our framework by validating models that abstract the data-cache side channel of a Raspberry Pi 3 board with a processor implementing the ARMv8-A architecture. Our results show that Scam-V can identify bugs in the implementation of the models and generate test programs which invalidate the models due to hidden microarchitectural behavior.
A modular extension for a computer algebra system
Venue: arXiv cs.MS 2020
Year: 2020

Computer algebra systems are complex software systems that cover a wide range of scientific and practical problems. However, the absolute coverage cannot be achieved. Often, it is required to create a user extension for an existing computer algebra system. In this case, the extensibility of the system should be taken into account. In this paper, we consider a technology for extending the SymPy computer algebra system with a low-level module that implements a random number generator.
Federated Learning and Wireless Communications
Venue: arXiv cs.IT 2020
Year: 2020

Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. In contrast to other machine learning tools that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a machine learning model. Therefore, how to efficiently assign limited communication resources to train a federated learning model becomes critical to performance optimization. On the other hand, federated learning, as a brand new tool, can potentially enhance the intelligence of wireless networks. In this article, we provide a comprehensive overview on the relationship between federated learning and wireless communications, including basic principle of federated learning, efficient communications for training a federated learning model, and federated learning for intelligent wireless applications. We also identify some future research challenges and directions at the end of this article.
A Game Theoretic Approach for Parking Spot Search with Limited Parking Lot Information
Venue: arXiv cs.RO 2020
Year: 2020

We propose a game theoretic approach to address the problem of searching for available parking spots in a parking lot and picking the ``optimal'' one to park. The approach exploits limited information provided by the parking lot, i.e., its layout and the current number of cars in it. Considering the fact that such information is or can be easily made available for many structured parking lots, the proposed approach can be applicable without requiring major updates to existing parking facilities. For large parking lots, a sampling-based strategy is integrated with the proposed approach to overcome the associated computational challenge. The proposed approach is compared against a state-of-the-art heuristic-based parking spot search strategy in the literature through simulation studies and demonstrates its advantage in terms of achieving lower cost function values.
Identifying Mechanical Models through Differentiable Simulations
Venue: arXiv cs.RO 2020
Year: 2020

This paper proposes a new method for manipulating unknown objects through a sequence of non-prehensile actions that displace an object from its initial configuration to a given goal configuration on a flat surface. The proposed method leverages recent progress in differentiable physics models to identify unknown mechanical properties of manipulated objects, such as inertia matrix, friction coefficients and external forces acting on the object. To this end, a recently proposed differentiable physics engine for two-dimensional objects is adopted in this work and extended to deal forces in the three-dimensional space. The proposed model identification technique analytically computes the gradient of the distance between forecasted poses of objects and their actual observed poses and utilizes that gradient to search for values of the mechanical properties that reduce the reality gap. Experiments with real objects using a real robot to gather data show that the proposed approach can identify the mechanical properties of heterogeneous objects on the fly.
Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search
Venue: arXiv cs.CV 2020
Year: 2020

Automating the classification of camera-obtained microscopic images of White Blood Cells (WBCs) and related cell subtypes has assumed importance since it aids the laborious manual process of review and diagnosis. Several State-Of-The-Art (SOTA) methods developed using Deep Convolutional Neural Networks suffer from the problem of domain shift - severe performance degradation when they are tested on data (target) obtained in a setting different from that of the training (source). The change in the target data might be caused by factors such as differences in camera/microscope types, lenses, lighting-conditions etc. This problem can potentially be solved using Unsupervised Domain Adaptation (UDA) techniques albeit standard algorithms presuppose the existence of a sufficient amount of unlabelled target data which is not always the case with medical images. In this paper, we propose a method for UDA that is devoid of the need for target data. Given a test image from the target data, we obtain its 'closest-clone' from the source data that is used as a proxy in the classifier. We prove the existence of such a clone given that infinite number of data points can be sampled from the source distribution. We propose a method in which a latent-variable generative model based on variational inference is used to simultaneously sample and find the 'closest-clone' from the source distribution through an optimization procedure in the latent space. We demonstrate the efficacy of the proposed method over several SOTA UDA methods for WBC classification on datasets captured using different imaging modalities under multiple settings.
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments
Venue: arXiv cs.LG 2020
Year: 2020

Action and observation delays exist prevalently in the real-world cyber-physical systems which may pose challenges in reinforcement learning design. It is particularly an arduous task when handling multi-agent systems where the delay of one agent could spread to other agents. To resolve this problem, this paper proposes a novel framework to deal with delays as well as the non-stationary training issue of multi-agent tasks with model-free deep reinforcement learning. We formally define the Delay-Aware Markov Game that incorporates the delays of all agents in the environment. To solve Delay-Aware Markov Games, we apply centralized training and decentralized execution that allows agents to use extra information to ease the non-stationarity issue of the multi-agent systems during training, without the need of a centralized controller during execution. Experiments are conducted in multi-agent particle environments including cooperative communication, cooperative navigation, and competitive experiments. We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness. Results show that the proposed delay-aware multi-agent reinforcement learning algorithm greatly alleviates the performance degradation introduced by delay. Codes and demo videos are available at: https://github.com/baimingc/delay-aware-MARL.
Luganda Text-to-Speech Machine
Venue: arXiv cs.CL 2020
Year: 2020

In Uganda, Luganda is the most spoken native language. It is used for communication in informal as well as formal business transactions. The development of technology startups globally related to TTS has mainly been with languages like English, French, etc. These are added in TTS engines by Google, Microsoft among others, allowing developers in these regions to innovate TTS products. Luganda is not supported because the language is not built and trained on these engines. In this study, we analyzed the Luganda language structure and constructions and then proposed and developed a Luganda TTS. The system was built and trained using locally sourced Luganda language text and audio. The engine is now able to capture text and reads it aloud. We tested the accuracy using MRT and MOS. MRT and MOS tests results are quite good with MRT having better results. The results general score was 71%. This study will enhance previous solutions to NLP gaps in Uganda, as well as provide raw data such that other research in this area can take place.
Performance Analysis and Optimization of Cache-Assisted CoMP for Clustered D2D Networks
Venue: arXiv cs.IT 2020
Year: 2020

Caching at mobile devices and leveraging cooperative device-to-device (D2D) communications are two promising approaches to support massive content delivery over wireless networks while mitigating the effects of interference. To show the impact of cooperative communication on the performance of cache-enabled D2D networks, the notion of device clustering must be factored in to convey a realistic description of the network performance. In this regard, this paper develops a novel mathematical model, based on stochastic geometry and an optimization framework for cache-assisted coordinated multi-point (CoMP) transmissions with clustered devices. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory to cache files from a known library, following a random probabilistic caching scheme. Desired contents that are not self-cached can be obtained via D2D CoMP transmissions from neighboring devices or, as a last resort, from the network. For this model, we analytically characterize the offloading gain and rate coverage probability as functions of the system parameters. An optimal caching strategy is then defined as the content placement scheme that maximizes the offloading gain. For a tractable optimization framework, we pursue two separate approaches to obtain a lower bound and a provably accurate approximation of the offloading gain, which allows us to obtain optimized caching strategies.
Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon
Venue: arXiv cs.LG 2020
Year: 2020

This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem. The latter refers to a particular type of robust fitting characterisation, popular in Computer Vision (MaxCon). Indeed, this is our main motivation but we believe the results of the study of these connections are more widely applicable to LP-type problems (at least 'thresholded versions', as we describe), and perhaps even more widely. We illustrate, with examples from Computer Vision, how the resulting perspectives suggest new algorithms. Indeed, we focus, in the experimental part, on how the Influence (a property of Boolean Functions that takes on a special form if the function is Monotone) can guide a search for the MaxCon solution.
PSDet: Efficient and Universal Parking Slot Detection
Venue: arXiv cs.CV 2020
Year: 2020

While real-time parking slot detection plays a critical role in valet parking systems, existing methods have limited success in real-world applications. We argue two reasons accounting for the unsatisfactory performance: \romannumeral1, The available datasets have limited diversity, which causes the low generalization ability. \romannumeral2, Expert knowledge for parking slot detection is under-estimated. Thus, we annotate a large-scale benchmark for training the network and release it for the benefit of community. Driven by the observation of various parking lots in our benchmark, we propose the circular descriptor to regress the coordinates of parking slot vertexes and accordingly localize slots accurately. To further boost the performance, we develop a two-stage deep architecture to localize vertexes in the coarse-to-fine manner. In our benchmark and other datasets, it achieves the state-of-the-art accuracy while being real-time in practice. Benchmark is available at: https://github.com/wuzzh/Parking-slot-dataset
Age-Energy Tradeoff in Fading Channels with Packet-Based Transmissions
Venue: arXiv cs.IT 2020
Year: 2020

The optimal transmission strategy to minimize the weighted combination of age of information (AoI) and total energy consumption is studied in this paper. It is assumed that the status update information is obtained and transmitted at fixed rate over a Rayleigh fading channel in a packet-based wireless communication system. A maximum transmission round on each packet is enforced to guarantee certain reliability of the update packets. Given fixed average transmission power, the age-energy tradeoff can be formulated as a constrained Markov decision process (CMDP) problem considering the sensing power consumption as well. Employing the Lagrangian relaxation, the CMDP problem is transformed into a Markov decision process (MDP) problem. An algorithm is proposed to obtain the optimal power allocation policy. Through simulation results, it is shown that both age and energy efficiency can be improved by the proposed optimal policy compared with two benchmark schemes. Also, age can be effectively reduced at the expense of higher energy cost, and more emphasis on energy consumption leads to higher average age at the same energy efficiency. Overall, the tradeoff between average age and energy efficiency is identified.
Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking
Venue: arXiv cs.HC 2020
Year: 2020

Case records on victims of human trafficking are highly sensitive, yet the ability to share such data is critical to evidence-based practice and policy development across government, business, and civil society. We present new methods to anonymize, publish, and explore such data, implemented as a pipeline generating three artifacts: (1) synthetic data mitigating the privacy risk that published attribute combinations might be linked to known individuals or groups; (2) aggregate data mitigating the utility risk that synthetic data might misrepresent statistics needed for official reporting; and (3) visual analytics interfaces to both datasets mitigating the accessibility risk that privacy mechanisms or analysis tools might not be understandable and usable by all stakeholders. We present our work as a design study motivated by the goal of transforming how the world's largest database of identified victims is made available for global collaboration against human trafficking.
RetinotopicNet: An Iterative Attention Mechanism Using Local Descriptors with Global Context
Venue: arXiv cs.CV 2020
Year: 2020

Convolutional Neural Networks (CNNs) were the driving force behind many advancements in Computer Vision research in recent years. This progress has spawned many practical applications and we see an increased need to efficiently move CNNs to embedded systems today. However traditional CNNs lack the property of scale and rotation invariance: two of the most frequently encountered transformations in natural images. As a consequence CNNs have to learn different features for same objects at different scales. This redundancy is the main reason why CNNs need to be very deep in order to achieve the desired accuracy. In this paper we develop an efficient solution by reproducing how nature has solved the problem in the human brain. To this end we let our CNN operate on small patches extracted using the log-polar transform, which is known to be scale and rotation equivariant. Patches extracted in this way have the nice property of magnifying the central field and compressing the periphery. Hence we obtain local descriptors with global context information. However the processing of a single patch is usually not sufficient to achieve high accuracies in e.g. classification tasks. We therefore successively jump to several different locations, called saccades, thus building an understanding of the whole image. Since log-polar patches contain global context information, we can efficiently calculate following saccades using only the small patches. Saccades efficiently compensate for the lack of translation equivariance of the log-polar transform.
Fostering Event Compression using Gated Surprise
Venue: arXiv cs.LG 2020
Year: 2020

Our brain receives a dynamically changing stream of sensorimotor data. Yet, we perceive a rather organized world, which we segment into and perceive as events. Computational theories of cognitive science on event-predictive cognition suggest that our brain forms generative, event-predictive models by segmenting sensorimotor data into suitable chunks of contextual experiences. Here, we introduce a hierarchical, surprise-gated recurrent neural network architecture, which models this process and develops compact compressions of distinct event-like contexts. The architecture contains a contextual LSTM layer, which develops generative compressions of ongoing and subsequent contexts. These compressions are passed into a GRU-like layer, which uses surprise signals to update its recurrent latent state. The latent state is passed forward into another LSTM layer, which processes actual dynamic sensory flow in the light of the provided latent, contextual compression signals. Our model shows to develop distinct event compressions and achieves the best performance on multiple event processing tasks. The architecture may be very useful for the further development of resource-efficient learning, hierarchical model-based reinforcement learning, as well as the development of artificial event-predictive cognition and intelligence.
Automatic clustering of Celtic coins based on 3D point cloud pattern analysis
Venue: arXiv cs.CV 2020
Year: 2020

The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is very tedious. In this paper, we propose a method to automatically cluster dies, based on 3D scans of coins. It is based on three steps: registration, comparison and graph-based clustering. Experimental results on 90 coins coming from a Celtic treasury from the II-Ith century BC show a clustering quality equivalent to expert's work.
Preference Elicitation in Assumption-Based Argumentation
Venue: arXiv cs.AI 2020
Year: 2020

Various structured argumentation frameworks utilize preferences as part of their standard inference procedure to enable reasoning with preferences. In this paper, we consider an inverse of the standard reasoning problem, seeking to identify what preferences over assumptions could lead to a given set of conclusions being drawn. We ground our work in the Assumption-Based Argumentation (ABA) framework, and present an algorithm which computes and enumerates all possible sets of preferences over the assumptions in the system from which a desired conflict free set of conclusions can be obtained under a given semantic. After describing our algorithm, we establish its soundness, completeness and complexity.
Skeleton-Aware Networks for Deep Motion Retargeting
Venue: arXiv cs.CV 2020
Year: 2020

We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Importantly, our approach learns how to retarget without requiring any explicit pairing between the motions in the training set. We leverage the fact that different homeomorphic skeletons may be reduced to a common primal skeleton by a sequence of edge merging operations, which we refer to as skeletal pooling. Thus, our main technical contribution is the introduction of novel differentiable convolution, pooling, and unpooling operators. These operators are skeleton-aware, meaning that they explicitly account for the skeleton's hierarchical structure and joint adjacency, and together they serve to transform the original motion into a collection of deep temporal features associated with the joints of the primal skeleton. In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons. Thus, retargeting can be achieved simply by encoding to, and decoding from this latent space. Our experiments show the effectiveness of our framework for motion retargeting, as well as motion processing in general, compared to existing approaches. Our approach is also quantitatively evaluated on a synthetic dataset that contains pairs of motions applied to different skeletons. To the best of our knowledge, our method is the first to perform retargeting between skeletons with differently sampled kinematic chains, without any paired examples.
Human-Robot Collaboration in Microgravity: the Object Handover Problem
Venue: arXiv cs.RO 2020
Year: 2020

Collaborative space robots are an emerging technology with high impact as robots facilitate servicing functions in collaboration with astronauts with higher precision during lengthy tasks, under tight operational schedules, with less risk and costs, making them more efficient and economically more viable. However, human-robot collaboration in space is still a challenge concerning key issues in human-robot interaction, including mobility and collaborative manipulation of objects on a microgravity environment. In this paper we formulate an algorithm that enables a free-flyer robot, equipped with a manipulator, to perform an object handover between a human and a free-flyer robot, in a microgravity environment. To validate and evaluate this algorithm, we present a systematic user study with the goal of understanding the subjective outcome effects of a rigid and compliant impedance robot behavior during the interaction. The results showed that the rigid behavior was overall more preferable and registered higher transfer success during the tasks.
Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019
Venue: arXiv cs.CL 2020
Year: 2020

We reassess the claims of human parity and super-human performance made at the news shared task of WMT 2019 for three translation directions: English-to-German, English-to-Russian and German-to-English. First we identify three potential issues in the human evaluation of that shared task: (i) the limited amount of intersentential context available, (ii) the limited translation proficiency of the evaluators and (iii) the use of a reference translation. We then conduct a modified evaluation taking these issues into account. Our results indicate that all the claims of human parity and super-human performance made at WMT 2019 should be refuted, except the claim of human parity for English-to-German. Based on our findings, we put forward a set of recommendations and open questions for future assessments of human parity in machine translation.