OpenRaiser/Intern-Atlas huggingface dataset:
ANDRUSPEX : Leveraging Graph Representation Learning to Predict Harmful App Installations on Mobile Devices
Venue: arXiv cs.CR 2021
Year: 2021

Android's security model severely limits the capabilities of anti-malware software. Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sandboxed applications without root privileges and are limited by Android's permission system. As such, PHAs on Android are usually willingly installed by victims, as they come disguised as useful applications with hidden malicious functionality, and are encountered on mobile app stores as suggestions based on the apps that a user previously installed. Users with similar interests and app installation history are likely to be exposed and to decide to install the same PHA. This observation gives us the opportunity to develop predictive approaches that can warn the user about which PHAs they will encounter and potentially be tempted to install in the near future. These approaches could then be used to complement commodity anti-malware solutions, which are focused on post-fact detection, closing the window of opportunity that existing solutions suffer from. In this paper we develop Andruspex, a system based on graph representation learning, allowing us to learn latent relationships between user devices and PHAs and leverage them for prediction. We test Andruspex on a real world dataset of PHA installations collected by a security company, and show that our approach achieves very high prediction results (up to 0.994 TPR at 0.0001 FPR), while at the same time outperforming alternative baseline methods. We also demonstrate that Andruspex is robust and its runtime performance is acceptable for a real world deployment.
Weight-Reducing Turing Machines
Venue: arXiv cs.FL 2021
Year: 2021

It is well-known that one-tape Turing machines working in linear time are no more powerful than finite automata, namely they recognize exactly the class of regular languages. We prove that it is not decidable if a one-tape machine works in linear time, even if it is deterministic and restricted to use only the portion of the tape which initially contains the input. This motivates the introduction of a constructive variant of one-tape machines, called weight-reducing machine, and the investigation of its properties. We focus on the deterministic case. In particular, we show that, paying a polynomial size increase only, each weight-reducing machine can be turned into a halting one that works in linear time. Furthermore each weight-reducing machine can be converted into equivalent nondeterministic and deterministic finite automata by paying exponential and doubly-exponential increase in size, respectively. These costs cannot be reduced in general.
How to integrate with real cars -- minimizing lead time at Volkswagen
Venue: arXiv cs.SE 2021
Year: 2021

The most successful tech companies of the world release new software versions to production multiple times a day. Thereby, they are able to quickly fix emerging bugs and rapidly deliver new features to their customers. This leads to short development cycles, minimal lead times and a high customer-centricity. Short development cycles are easy to achieve if you start a software project on a green field. Nevertheless, this does not apply to brown field environments which are usually found in big corporates such as traditional car manufacturers. For instance, if you want to integrate with real cars you have to interface legacy systems with development cycles of up to several months. We present a solution, which worked for one of the world's largest car manufacturer, leveraging in-house core development teams, dynamic stages and feature-toggles to overcome a brown field environment, allow for short development cycles and minimize the lead time.
Development of a VR tool to study pedestrian route and exit choice behaviour in a multi-story building
Venue: arXiv cs.HC 2021
Year: 2021

Although route and exit choice in complex buildings are important aspects of pedestrian behaviour, studies predominantly investigated pedestrian movement in a single level. This paper presents an innovative VR tool that was designed to investigate pedestrian route and exit choice in a multi-story building. This tool supports free navigation and collects pedestrian walking trajectories, head movements and gaze points automatically. An experiment was conducted to evaluate the VR tool from objective standpoints (i.e., pedestrian behaviour) and subjective standpoints (i.e., the feeling of presence, system usability, simulation sickness). The results show that the VR tool allows for accurate collection of pedestrian behavioural data in the complex building. Moreover, the results of the questionnaire report high realism of the virtual environment, high immersive feeling, high usability, and low simulator sickness. This paper contributes by showcasing an innovative approach of applying VR technologies to study pedestrian behaviour in complex and realistic environments.
Did Chatbots Miss Their 'Apollo Moment'? A Survey of the Potential, Gaps and Lessons from Using Collaboration Assistants During COVID-19
Venue: arXiv cs.HC 2021
Year: 2021

Artificial Intelligence (AI) technologies have long been positioned as a tool to provide crucial data-driven decision support to people. In this survey paper, we look at how AI in general, and collaboration assistants (CAs or chatbots for short) in particular, have been used during a true global exigency - the COVID-19 pandemic. The key observation is that chatbots missed their "Apollo moment" when they could have really provided contextual, personalized, reliable decision support at scale that the state-of-the-art makes possible. We review the existing capabilities that are feasible and methods, identify the potential that chatbots could have met, the use-cases they were deployed on, the challenges they faced and gaps that persisted, and draw lessons that, if implemented, would make them more relevant in future health emergencies.
MirrorME: Implementation of an IoT based Smart Mirror through Facial Recognition and Personalized Information Recommendation Algorithm
Venue: arXiv cs.HC 2021
Year: 2021

We are living in the era of the fourth industrial revolution, which also treated as 4IR or Industry 4.0. Generally, 4IR considered as the mixture of robotics, artificial intelligence (AI), quantum computing, the Internet of Things (IoT) and other frontier technologies. It is obvious that nowadays a plethora of smart devices is providing services to make the daily life of human easier. However, in the morning most people around the globe use a traditional mirror while preparing themselves for daily task. The aim is to build a low-cost intelligent mirror system that can display a variety of details based on user recommendations. Therefore, in this article, Internet of Things (IoT) and AI-based smart mirror is introduced that will support the users to receive the necessary daily update of weather information, date, time, calendar, to-do list, updated news headlines, traffic updates, COVID-19 cases status and so on. Moreover, a face detection method also implemented with the smart mirror to construct the architecture more secure. Our proposed MirrorME application provides a success rate of nearly 87% in interacting with the features of face recognition and voice input. The mirror is capable of delivering multimedia facilities while maintaining high levels of security within the device.
Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization
Venue: arXiv cs.CV 2021
Year: 2021

Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort. This work enables the training of semantic segmentation networks on images with only a single point for training per instance, an extreme case of weak supervision which drastically reduces the burden of annotation. Our approach has two key aspects: (1) we construct a graph-theoretic soft-segmentation using individual seeds to be used within a regularizer during training and (2) we use an objective function that enables learning from the constructed soft-labels. We achieve competitive results against the state-of-the-art in point-supervised semantic segmentation on challenging datasets in digital pathology. Finally, we scale our methodology to point-supervised segmentation in 3D fluorescence microscopy volumes, obviating the need for arduous manual volumetric delineation. Our code is freely available.
Improved upper bounds for the rigidity of Kronecker products
Venue: arXiv cs.DS 2021
Year: 2021

The rigidity of a matrix $A$ for target rank $r$ is the minimum number of entries of $A$ that need to be changed in order to obtain a matrix of rank at most $r$. At MFCS'77, Valiant introduced matrix rigidity as a tool to prove circuit lower bounds for linear functions and since then this notion received much attention and found applications in other areas of complexity theory. The problem of constructing an explicit family of matrices that are sufficiently rigid for Valiant's reduction (Valiant-rigid) still remains open. Moreover, since 2017 most of the long-studied candidates have been shown not to be Valiant-rigid. Some of those former candidates for rigidity are Kronecker products of small matrices. In a recent paper (STOC'21), Alman gave a general non-rigidity result for such matrices: he showed that if an $n\times n$ matrix $A$ (over any field) is a Kronecker product of $d\times d$ matrices $M_1,\dots, M_k$ (so $n=d^k$) $(d\ge 2)$ then changing only $n^{1+\varepsilon}$ entries of $A$ one can reduce its rank to $\le n^{1-γ}$, where $1/γ$ is roughly $2^d/\varepsilon^2$. In this note we improve this result in two directions. First, we do not require the matrices $M_i$ to have equal size. Second, we reduce $1/γ$ from exponential in $d$ to roughly $d^{3/2}/\varepsilon^2$ (where $d$ is the maximum size of the matrices $M_i$), and to nearly linear (roughly $d/\varepsilon^2$) for matrices $M_i$ of sizes within a constant factor of each other. As an application of our results we significantly expand the class of Hadamard matrices that are known not to be Valiant-rigid; these now include the Kronecker products of Paley-Hadamard matrices and Hadamard matrices of bounded size.
Proof-of-Learning: Definitions and Practice
Venue: arXiv cs.LG 2021
Year: 2021

Training machine learning (ML) models typically involves expensive iterative optimization. Once the model's final parameters are released, there is currently no mechanism for the entity which trained the model to prove that these parameters were indeed the result of this optimization procedure. Such a mechanism would support security of ML applications in several ways. For instance, it would simplify ownership resolution when multiple parties contest ownership of a specific model. It would also facilitate the distributed training across untrusted workers where Byzantine workers might otherwise mount a denial-of-service by returning incorrect model updates. In this paper, we remediate this problem by introducing the concept of proof-of-learning in ML. Inspired by research on both proof-of-work and verified computations, we observe how a seminal training algorithm, stochastic gradient descent, accumulates secret information due to its stochasticity. This produces a natural construction for a proof-of-learning which demonstrates that a party has expended the compute require to obtain a set of model parameters correctly. In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform *at least* as much work than is needed for gradient descent itself. We also instantiate a concrete proof-of-learning mechanism in both of the scenarios described above. In model ownership resolution, it protects the intellectual property of models released publicly. In distributed training, it preserves availability of the training procedure. Our empirical evaluation validates that our proof-of-learning mechanism is robust to variance induced by the hardware (ML accelerators) and software stacks.
Self-Supervision by Prediction for Object Discovery in Videos
Venue: arXiv cs.CV 2021
Year: 2021

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data. On the other hand, unsupervised settings pose many challenges, especially about determining the right inductive bias in diverse scenarios. One scalable solution is to make the model generate the supervision for itself by leveraging some part of the input data, which is known as self-supervised learning. In this paper, we use the prediction task as self-supervision and build a novel object-centric model for image sequence representation. In addition to disentangling the notion of objects and the motion dynamics, our compositional structure explicitly handles occlusion and inpaints inferred objects and background for the composition of the predicted frame. With the aid of auxiliary loss functions that promote spatially and temporally consistent object representations, our self-supervised framework can be trained without the help of any manual annotation or pretrained network. Initial experiments confirm that the proposed pipeline is a promising step towards object-centric video prediction.
Pixel-wise Distance Regression for Glacier Calving Front Detection and Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Glacier calving front position (CFP) is an important glaciological variable. Traditionally, delineating the CFPs has been carried out manually, which was subjective, tedious and expensive. Automating this process is crucial for continuously monitoring the evolution and status of glaciers. Recently, deep learning approaches have been investigated for this application. However, the current methods get challenged by a severe class-imbalance problem. In this work, we propose to mitigate the class-imbalance between the calving front class and the non-calving front class by reformulating the segmentation problem into a pixel-wise regression task. A Convolutional Neural Network gets optimized to predict the distance values to the glacier front for each pixel in the image. The resulting distance map localizes the CFP and is further post-processed to extract the calving front line. We propose three post-processing methods, one method based on statistical thresholding, a second method based on conditional random fields (CRF), and finally the use of a second U-Net. The experimental results confirm that our approach significantly outperforms the state-of-the-art methods and produces accurate delineation. The Second U-Net obtains the best performance results, resulting in an average improvement of about 21% dice coefficient enhancement.
Spheroidal Ambisonics: a Spatial Audio Framework Using Spheroidal Bases
Venue: arXiv cs.SD 2021
Year: 2021

Ambisonics is an established framework to capture, process, and reproduce spatial sound fields based on its spherical harmonics representation. We propose a generalization of conventional spherical ambisonics to the spheroidal coordinate system and spheroidal microphone arrays, which represent sound fields by means of spheroidal wave functions. This framework is referred to as spheroidal ambisonics and a formulation for the case of prolate spheroidal coordinates is presented. Spheroidal ambisonics allows analytical encoding of sound fields using spheroidal microphone arrays. In addition, an analytical conversion formula from spheroidal ambisonics to spherical ambisonics is derived in order to ensure compatibility with the existing ecosystem of spherical ambisonics. Numerical experiments are performed to verify spheroidal ambisonic encoding and transcoding when used for spatial sound field recording. It is found that the sound field reconstructed from the transcoded coefficients has a zone of accurate reconstruction which is prolonged towards the long axis of a prolate spheroidal microphone array.
A Multi-resolution Approach to Expression Recognition in the Wild
Venue: arXiv cs.CV 2021
Year: 2021

Facial expressions play a fundamental role in human communication. Indeed, they typically reveal the real emotional status of people beyond the spoken language. Moreover, the comprehension of human affect based on visual patterns is a key ingredient for any human-machine interaction system and, for such reasons, the task of Facial Expression Recognition (FER) draws both scientific and industrial interest. In the recent years, Deep Learning techniques reached very high performance on FER by exploiting different architectures and learning paradigms. In such a context, we propose a multi-resolution approach to solve the FER task. We ground our intuition on the observation that often faces images are acquired at different resolutions. Thus, directly considering such property while training a model can help achieve higher performance on recognizing facial expressions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset. Not being available a test set, we conduct tests and models selection by employing the validation set only on which we achieve more than 90\% accuracy on classifying the seven expressions that the dataset comprises.
Deep graph convolution neural network with non-negative matrix factorization for community discovery
Venue: arXiv cs.SI 2021
Year: 2021

Community discovery is an important task for graph mining. Owing to the nonstructure, the high dimensionality, and the sparsity of graph data, it is not easy to obtain an appropriate community partition. In this paper, a deep graph convolution neural network with non-negative matrix factorization (DGCN-NMF) is proposed for community discovery. DGCN-NMF employs multiple graph convolution layers to obtain the low dimensional embedding. In each layer, the last output and the non-negative matrix factorization's results of the previous outputs are inputted into the current layer. The community partition of the inputted graph can be outputted by an end-to-end approach. The proposed algorithm and the comparison algorithms are conducted on the experimental data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the experimental data sets. The experimental results demonstrate that DGCN-NMF is an effective algorithm for community discovery.
A principled approach for weighted multilayer network aggregation
Venue: arXiv cs.SI 2021
Year: 2021

A multilayer network depicts different types of interactions among the same set of nodes. For example, protease networks consist of five to seven layers, where different layers represent distinct types of experimentally confirmed molecule interactions among proteins. In a multilayer protease network, the co-expression layer is obtained through the meta-analysis of transcriptomic data from various sources and platforms. While in some researches the co-expression layer is in turn represented as a multilayered network, a fundamental problem is how to obtain a single-layer network from the corresponding multilayered network. This process is called multilayer network aggregation. In this work, we propose a maximum a posteriori estimation-based algorithm for multilayer network aggregation. The method allows to aggregate a weighted multilayer network while conserving the core information of the layers. We evaluate the method through an unweighted friendship network and a multilayer gene co-expression network. We compare the aggregated gene co-expression network with a network obtained from conflated datasets and a network obtained from averaged weights. The Von Neumann entropy is adopted to compare the mixedness of the three networks, and, together with other network measurements, shows the effectiveness of the proposes method.
Natural Hoare Logic: Towards formal verification of programs from logical forms of natural language specifications
Venue: arXiv cs.PL 2021
Year: 2021

Formal verification provides strong guarantees of correctness of software, which are especially important in safety or security critical systems. Hoare logic is a widely used formalism for rigorous verification of software against specifications in the form of pre-condition/post-condition assertions. The advancement of semantic parsing techniques and higher computational capabilities enable us to extract semantic content from natural language text as formal logical forms, with increasing accuracy and coverage. This paper proposes a formal framework for Hoare logic-based formal verification of imperative programs using logical forms generated from compositional semantic parsing of natural language assertions. We call our reasoning approach Natural Hoare Logic. This enables formal verification of software directly against safety requirements specified by a domain expert in natural language. We consider both declarative assertions of program invariants and state change as well as imperative assertions that specify commands which alter the program state. We discuss how the reasoning approach can be extended using domain knowledge and a practical approach for guarding against semantic parser errors.
Fast and flexible: Human program induction in abstract reasoning tasks
Venue: arXiv cs.HC 2021
Year: 2021

The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collected from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). Although this subset of tasks contains considerable variation, our results showed that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 80% of tasks solved per participant, and with 65% of tasks being solved by more than 80% of participants. Additionally, we find interesting patterns of behavioral consistency and variability within the action sequences during the generation process, the natural language descriptions to describe the transformations for each task, and the errors people made. Our findings suggest that people can quickly and reliably determine the relevant features and properties of a task to compose a correct solution. Future modeling work could incorporate these findings, potentially by connecting the natural language descriptions we collected here to the underlying semantics of ARC.
Learning to compose 6-DoF omnidirectional videos using multi-sphere images
Venue: arXiv cs.CV 2021
Year: 2021

Omnidirectional video is an essential component of Virtual Reality. Although various methods have been proposed to generate content that can be viewed with six degrees of freedom (6-DoF), existing systems usually involve complex depth estimation, image in-painting or stitching pre-processing. In this paper, we propose a system that uses a 3D ConvNet to generate a multi-sphere images (MSI) representation that can be experienced in 6-DoF VR. The system utilizes conventional omnidirectional VR camera footage directly without the need for a depth map or segmentation mask, thereby significantly simplifying the overall complexity of the 6-DoF omnidirectional video composition. By using a newly designed weighted sphere sweep volume (WSSV) fusing technique, our approach is compatible with most panoramic VR camera setups. A ground truth generation approach for high-quality artifact-free 6-DoF contents is proposed and can be used by the research and development community for 6-DoF content generation.