Miracle-dz/UniFAR-FTUs huggingface dataset:
Bandwidth Partitioning and Downlink Analysis in mmWave Integrated Access and Backhaul for 5G

With the increasing network densification, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mmWave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mmWave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul. In this paper, we develop an analytical framework for IAB-enabled cellular network using which we provide an accurate characterization of its downlink rate coverage probability. Using this, we study the performance of three backhaul bandwidth (BW) partition strategies, (i) equal partition: when all SBSs obtain equal share of the backhaul BW, (ii) instantaneous load-based partition: when the backhaul BW share of an SBS is proportional to its instantaneous load, and (iii) average load-based partition: when the backhaul BW share of an SBS is proportional to its average load. Our analysis shows that depending on the choice of the partition strategy, there exists an optimal split of access and backhaul BW for which the rate coverage is maximized. Further, there exists a critical volume of cell-load (total number of users) beyond which the gains provided by the IAB-enabled network disappear and its performance converges to that of the traditional macro-only network with no SBSs.
Robust and Versatile Event Detection through Gradient-Based Scoring of HMM Models

Event detection is a critical feature across any data-driven system as it assists with the identification of nominal and anomalous behavior. Event detection is all the more relevant in robotics as robots are required to operate with greater autonomy and in increasingly unstructured environments. Recently supervised and unsupervised machine learning methods along with probabilistic models have played increasingly important roles in modeling and predicting events. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. Our work presents a theoretical proof that establishes the link between the derivative of the HMM log-likelihood and the latest emission probabilities. The result used a set of verifiable suppositions and insights aided by the log-sum-exp trick. This link established that the latest emission probabilities directly affected the log-likelihood gradient. The key insight was the inverse relationship: that from these gradients skills and anomalies could be identified. Our results showed better performance across all metrics than related state-of-the art results. The result is very significant for event detection problems as the gradient-based measure can be broadly utilized in any subject area (finance, bioinformatics, natural language processing, computer vision, etc) that involves HMM modeling, including its extensions: parametric, nonparametric, hierarchical, etc).
Toward Detecting Violations of Differential Privacy

The widespread acceptance of differential privacy has led to the publication of many sophisticated algorithms for protecting privacy. However, due to the subtle nature of this privacy definition, many such algorithms have bugs that make them violate their claimed privacy. In this paper, we consider the problem of producing counterexamples for such incorrect algorithms. The counterexamples are designed to be short and human-understandable so that the counterexample generator can be used in the development process – a developer could quickly explore variations of an algorithm and investigate where they break down. Our approach is statistical in nature. It runs a candidate algorithmmany times and uses statistical tests to try to detect violations of differential privacy. An evaluation on a variety of incorrect published algorithms validates the usefulness of our approach: it correctly rejects incorrect algorithms and provides counterexamples for them within a few seconds.
Unsupervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning

Separating audio mixtures into individual tracks has been a long standing challenging task. We introduce a novel unsupervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution distance between the separated sources and the real sources for each individual source. Moreover, a global regularization term is added to fulfill the spectrum energy preservation property regarding separation. Unlike state-of-the-art unsupervised models which often involve deliberately devised constraints or careful model selection, our approach need little prior model specification on the data, and can be straightforwardly learned in an end-to-end fashion. We show that the proposed method performs competitively on public benchmark against state-of-the-art unsupervised methods.
Self-healing Routing and Other Problems in Compact Memory

This paper looks at the question of designing distributed algorithms for the setting of compact memory i.e. sublinear (in $n$ - the number of nodes) bits for connected networks of arbitrary topologies. The nodes in our networks may have much lower internal memory (say, $O(poly\log n)$) as compared to the number of their possible neighbours. This implies that a node needs to run the algorithm and do computations without being even able to store the states or IDs of all its neighbours. Towards this end, we introduce the Compact Memory Passing model (CMP)- the standard message passing model at a finer granularity where a node can interleave reads and writes with internal computations. This is required for meaningful computations due to the low memory requirement and is somewhat akin to a distributed network with nodes executing streaming algorithms. We believe this model captures features of large networks of small memory devices (e.g. the Internet of Things (IOT)) and initiates research into theoretical foundations in this area. Compact Routing Messages in Self-Healing Trees (Distributed Computing 2017) introduced the compact self-healing routing algorithm CompactFTZ assuming `regular' memory for preprocessing and posed the problem of its compact preprocessing. We solve this problem and, hence, introduce the first fully compact self-healing routing algorithm. In the process, we also give independent fully compact algorithms for the Forgiving Tree [PODC 2008] Thorup-Zwick's tree based compact routing [SPAA 2001], and fundamental problems of leader election, tree constructions and traversals (BFS, DFS, spanning trees and convergecast). Our nodes have only $O(\log^2 n)$ local memory but the preprocessing can be accomplished using $O(\log n)$ bits sized messages (as in the CONGEST model). We also give a faster solution for $O(\log n)$ bits sized messages.
Use expert knowledge instead of data: generating hints for hour of code exercises

Within the field of on-line tutoring systems for learning programming, such as Code.org's Hour of code, there is a trend to use previous student data to give hints. This paper shows that it is better to use expert knowledge to provide hints in environments such as Code.org's Hour of code. We present a heuristic-based approach to generating next-step hints. We use pattern matching algorithms to identify heuristics and apply each identified heuristic to an input program. We generate a next-step hint by selecting the highest scoring heuristic using a scoring function. By comparing our results with results of a previous experiment on Hour of code we show that a heuristics-based approach to providing hints gives results that are impossible to further improve. These basic heuristics are sufficient to efficiently mimic experts' next-step hints.
DeepGauge: Comprehensive and Multi-Granularity Testing Criteria for Gauging the Robustness of Deep Learning Systems

Deep learning defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. Deep learning (DL) has been widely adopted in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the robustness of a DL system against adversarial attacks is usually measured by the accuracy of test data. Considering the limitation of accessible test data, good performance on test data can hardly guarantee the robustness and generality of DL systems. Different from traditional software systems which have clear and controllable logic and functionality, a DL system is trained with data and lacks thorough understanding. This makes it difficult for system analysis and defect detection, which could potentially hinder its real-world deployment without safety guarantees. In this paper, we propose DeepGauge, a comprehensive and multi-granularity testing criteria for DL systems, which renders a complete and multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, with four state-of-the-art adversarial data generation techniques. The effectiveness of DeepGauge sheds light on the construction of robust DL systems.
Delegated Pseudo-Secret Random Qubit Generator

We define the functionality of delegated pseudo-secret random qubit generator (PSRQG), where a classical client can instruct the preparation of a sequence of random qubits at some distant party. Their classical description is (computationally) unknown to any other party (including the distant party preparing them) but known to the client. We emphasize the unique feature that no quantum communication is required to implement PSRQG. This enables classical clients to perform a class of quantum communication protocols with only a public classical channel with a quantum server. A key such example is the delegated universal quantum computing. Using our functionality one could achieve for the first time a purely classical-client computational secure verifiable delegated universal quantum computing (also referred to as verifiable blind quantum computation). We give a concrete protocol (QFactory) implementing PSRQG, using the Learning-With-Errors problem to construct a trapdoor one-way function with certain desired properties (quantum-safe, two-regular, collision-resistant). We then prove the security in the Quantum-Honest-But-Curious setting and briefly discuss the extension to the malicious case.
Adaptive Spectral Graph Convolutional Networks for Skeleton-Based Action Recognition

Traditional deep methods for skeleton-based action recognition usually structure the skeleton as a coordinates sequence or a pseudo-image to feed to RNNs or CNNs, which cannot explicitly exploit the natural connectivity among the joints. Recently, graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, obtains remarkable performance for skeleton-based action recognition. In this work, we propose a novel adaptive spectral graph convolutional network which address the three major issues exposed in former GCN-based methods applied to action recognition: 1) The sampling function of graph convolutional operation applied in former methods is usually heuristically designed. We avoid the tricked definition of sampling function by transforming the GCN to frequency domain based on spectral graph theory. 2) The topology of the graph is set by hand and fixed over all layers, which may be not optimal for the action recognition task and the hierarchical CNN structures. In our model, the topology of the graph in each layer can be either uniformly or individually learned by BP algorithm, which can bring more flexibility and generality. 3) The first-order information (the coordinate of joints) is mainly used in former GCNs, while the second-order information (the length and direction of bones) is less exploited. A two-stream framework is proposed in this work to model both of the joints and bones information simultaneously. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate the performance of our model exceeds the state-of-the-art by a significant margin.
Multi-Player Bandits Models Revisited

Multi-player Multi-Armed Bandits (MAB) have been extensively studied in the literature, motivated by applications to Cognitive Radio systems. Driven by such applications as well, we motivate the introduction of several levels of feedback for multi-player MAB algorithms. Most existing work assume that sensing information is available to the algorithm. Under this assumption, we improve the state-of-the-art lower bound for the regret of any decentralized algorithms and introduce two algorithms, RandTopM and MCTopM, that are shown to empirically outperform existing algorithms. Moreover, we provide strong theoretical guarantees for these algorithms, including a notion of asymptotic optimality in terms of the number of selections of bad arms. We then introduce a promising heuristic, called Selfish, that can operate without sensing information, which is crucial for emerging applications to Internet of Things networks. We investigate the empirical performance of this algorithm and provide some first theoretical elements for the understanding of its behavior.
Compositionality in scenario-aware dataflow: a rendezvous perspective

Finite-state machine-based scenario-aware dataflow (FSM-SADF) is a dynamic dataflow model of computation that combines streaming data and finite-state control. For the most part, it preserves the determinism of its underlying synchronous dataflow (SDF) concurrency model and only when necessary introduces the non-deterministic variation in terms of scenarios that are represented by SDF graphs. This puts FSM-SADF in a sweet spot in the trade-off space between expressiveness and analyzability. However, FSM-SADF supports no notion of compositionality, which hampers its usability in modeling and consequent analysis of large systems. In this work we propose a compositional semantics for FSM-SADF that overcomes this problem. We base the semantics of the composition on standard composition of processes with rendezvous communication in the style of CCS or CSP at the control level and the parallel, serial and feedback composition of SDF graphs at the dataflow level. We evaluate the approach on a case study from the multimedia domain.
Using Sparse Digital Traces to Fill in Individual Level Mobility Timelines

Predicting individual-level mobility patterns is an imperative part of ubiquitous computing, in growing real-world applications like transport management and disease spread. While data sources such as GPS trackers or Call Data Records are temporally-rich, they are expensive, often not publicly available, or garnered only in select locations, restricting their wide use. Conversely, geo-located social media data are publicly and freely available, but present challenges due to their sparse nature. Further, much existing work has focused on predicting next location only, though knowledge of an entire movement timeline is relevant for emerging applications. Accordingly, we propose a stochastic framework, Intermediate Location Computing (ILC) which combines approaches from several existing mobility prediction methods, alongside community behavior, to predict every missing location from an individual’s social media timeline. We compare ILC with several state-of-the-art approaches. For three major cities, ILC predicts at 1 and 2-hour resolution with up to 86% accuracy, 19% better than compared methods. We show how amount of community data improves prediction, and that community movement improves prediction of an individual’s movement more on weekends versus weekdays. Overall this work presents a new algorithm to predict practical and continuous individual-level mobility patterns with sparse but readily available social media data.
Combinets: Learning New Classifiers via Recombination

Problems with few examples of a new class of objects prove challenging to most classifiers. One solution to is to reuse existing data through transfer methods such as one-shot learning or domain adaption. However these approaches require an explicit hand-authored or learned definition of how reuse can occur. We present an approach called conceptual expansion that learns how to reuse existing machine-learned knowledge when classifying new cases. We evaluate our approach by adding new classes of objects to the CIFAR-10 dataset and varying the number of available examples of these new classes.
Deep neural networks compiler for a trace-based accelerator (short WIP paper)

Deep Neural Networks (DNNs) are the algorithm of choice for image processing applications. DNNs present highly parallel workloads that lead to the emergence of custom hardware accelerators. Deep Learning (DL) models specialized in different tasks require a programmable custom hardware and a compiler/mapper to efficiently translate different DNNs into an efficient dataflow in the accelerator. The goal of this paper is to present a compiler for running DNNs on Snowflake, which is a programmable hardware accelerator that targets DNNs. The compiler correctly generates instructions for various DL models: AlexNet, VGG, ResNet and LightCNN9. Snowflake, with a varying number of processing units, was implemented on FPGA to measure the compiler and Snowflake performance properties upon scaling up. The system achieves 70 frames/s and 4.5 GB/s of off-chip memory bandwidth for AlexNet without linear layers on Xilinx’s Zynq-SoC XC7Z045 FPGA.
A Cascade of 2.5D CNN and LSTM Network for Mitotic Cell Detection in 4D Microscopy Image

In recent years, intravital skin imaging has been used in mammalian skin research for the investigation of cell behaviors. The mitotic cell (cell division) detection is a fundamental step of the investigation. Due to the complex backgrounds (normal cells), most of the existing methods bring a lot of false positives. In this paper, we proposed a 2.5 dimensional (2.5D) cascaded end-to-end network (CasDetNet_LSTM) for accurate automatic mitotic cell detection in 4D microscopic images with fewer training data. The CasDetNet_LSTM consists of two 2.5D networks. The first one is a 2.5D fast region-based convolutional neural network (2.5D Fast R-CNN), which is used for the detection of candidate cells with only volume information and the second one is a long short-term memory (LSTM) network embedded with temporal information, which is used for reduction of false positive and retrieving back those mitotic cells that missed in the first step. The experimental results shown that our CasDetNet_LSTM can achieve higher precision and recall comparing to other state-of-the-art methods.
FrauDroid: An Accurate and Scalable Approach to Automated Mobile Ad Fraud Detection

Previous studies on mobile ad fraud detection are limited to detecting certain types of ad fraud (e.g., placement fraud and click fraud). Dynamic interactive ad fraud has never been explored by previous work. In this paper, we propose an explorative study of ad fraud in Android apps. We first created a taxonomy of existing mobile ad fraud. Besides static placement fraud, we also created a new category called dynamic interactive fraud and found three new types of them. Then we propose FrauDroid, a new approach to detect ad fraud based on user interface (UI) state transition graph and networking traffic. To achieve accuracy and scalability, we use an optimized exploration strategy to traverse the UI states that contain ad views, and design a set of heuristic rules to identify kinds of ad fraud. Experiment results show that FrauDroid could achieve a precision of 80.9\% and it is capable of detecting all the 7 types of mobile ad fraud. We then apply FrauDroid to more than 80,000 apps from 21 app markets to study the prevalene of ad fraud. We find that 0.29\% of apps that use ad libraries are identified containing frauds, with 23 ad networks suffering from ad fraud. Our finding also suggests that Dynamic interactive fraud is more prevalent than static placement fraud in real world apps.
Discovering discussion topics about development of cross-platform mobile applications using a cross-compiler development framework

A cross-platform mobile application is an application that runs on multiple mobile platforms (Android or iOS platforms). One strategy for developing this kind of mobile applications involves to develop, using platform-related toolkits, a native application for each chosen platform. Several frameworks have been proposed to simplify the development of cross-platform mobile applications and, therefore, to reduce development and maintenance costs. Between them, the cross-compiler mobile development frameworks transform the application's code written in intermediate (aka non-native) language to native code for each platform. However, to our best knowledge, there is no much research about the advantages and disadvantages of the use of cross-compiler frameworks during the development and maintenance phases of mobile applications. This paper aims at contributing with one of the first bricks in that research direction. We study what mobile developers that use cross-compiler frameworks ask about when they develop and maintain cross-platform mobile applications. In particular, we focus on one framework: Xamarin from Microsoft. For that, we first created two datasets of questions and answers (QA) related to the development of mobile applications using Xamarin by mining two QA sites: Xamarin Forum and Stack Overflow. We analyzed and compared the number of questions, views and accepted. Then, we applied LDA on Xamarin-related questions to discover the main topics asked by developers that use Xamarin. Finally, we compared the discovered topics with those topics about mobile development. Our findings show that Xamarin Forum has a larger number of questions than Stack Overflow, however, the latter has more answers per question. Moreover, both sites share most of the main topics, which mainly discuss about user interface (UI), formatting, design and navigation.
Team based assignments in MOOCs: results and observations

Teamwork and collaborative learning are considered superior to learning individually by many instructors and didactical theories. Particularly, in the context of e-learning and Massive Open Online Courses (MOOCs) we see great benefits but also great challenges for both, learners and instructors. We discuss our experience with six team based assignments on the openHPI and openSAP1 MOOC platforms.