OpenRaiser/Intern-Atlas huggingface dataset:
An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks
Venue: arXiv cs.IT 2022
Year: 2022

In wireless network, the optimization problems generally have complex constraints, and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of network environments. In this paper, to overcome these shortcomings, an unsupervised deep unrolling framework based on projection gradient descent, i.e., unrolled PGD network (UPGDNet), is designed to solve a family of constrained optimization problems. The set of constraints is divided into two categories according to the coupling relations among optimization variables and the convexity of constraints. One category of constraints includes convex constraints with decoupling among optimization variables, and the other category of constraints includes non-convex or convex constraints with coupling among optimization variables. Then, the first category of constraints is directly projected onto the feasible region, while the second category of constraints is projected onto the feasible region using neural network. Finally, an unrolled sum rate maximization network (USRMNet) is designed based on UPGDNet to solve the weighted SR maximization problem for the multiuser ultra-reliable low latency communication system. Numerical results show that USRMNet has a comparable performance with low computational complexity and an acceptable generalization ability in terms of the user distribution.
On the Privacy of Mental Health Apps: An Empirical Investigation and its Implications for Apps Development
Venue: arXiv cs.CR 2022
Year: 2022

An increasing number of mental health services are offered through mobile systems, a paradigm called mHealth. Although there is an unprecedented growth in the adoption of mHealth systems, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps. We analyzed 27 top-ranked mental health apps from Google Play Store. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests. There is also a high risk of user profiling as the apps' development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among third parties and advertisers in the current apps' ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. [...]
SL Sensor: An Open-Source, ROS-Based, Real-Time Structured Light Sensor for High Accuracy Construction Robotic Applications
Venue: arXiv cs.RO 2022
Year: 2022

High accuracy 3D surface information is required for many construction robotics tasks such as automated cement polishing or robotic plaster spraying. However, consumer-grade depth cameras currently found in the market are not accurate enough for these tasks where millimeter (mm)-level accuracy is required. This paper presents SL Sensor, a structured light sensing solution capable of producing high fidelity point clouds at 5 Hz by leveraging on phase shifting profilometry (PSP) codification techniques. The SL Sensor was compared with to two commercial depth cameras - the Azure Kinect and RealSense L515. Experiments showed that the SL Sensor surpasses the two devices in both precision and accuracy for indoor surface reconstruction applications. Furthermore, to demonstrate SL Sensor's ability to be a structured light sensing research platform for robotic applications, a motion compensation strategy was developed that allows the SL Sensor to operate during linear motion when traditional PSP methods only work when the sensor is static. Field experiments show that the SL Sensor is able to produce highly detailed reconstructions of spray plastered surfaces. The robot operating system (ROS)-based software and a sample hardware build of the SL Sensor are made open-source with the objective to make structured light sensing more accessible to the construction robotics community. All documentation and code is available at https://github.com/ethz-asl/sl_sensor/ .
Manifold Optimization Based Multi-user Rate Maximization Aided by Intelligent Reflecting Surface
Venue: arXiv cs.IT 2022
Year: 2022

In this work, two problems associated with a downlink multi-user system are considered with the aid of intelligent reflecting surface (IRS): weighted sum-rate maximization and weighted minimal-rate maximization. For the first problem, a novel DOuble Manifold ALternating Optimization (DOMALO) algorithm is proposed by exploiting the matrix manifold theory and introducing the beamforming matrix and reflection vector using complex sphere manifold and complex oblique manifold, respectively, which incorporate the inherent geometrical structure and the required constraint. A smooth double manifold alternating optimization (S-DOMALO) algorithm is then developed based on the Dinkelbach-type algorithm and smooth exponential penalty function for the second problem. Finally, possible cooperative beamforming gain between IRSs and the IRS phase shift with limited resolution is studied, providing a reference for practical implementation. Numerical results show that our proposed algorithms can significantly outperform the benchmark schemes.
Strong Sleptsov Net is Turing-Complete
Venue: arXiv cs.CC 2022
Year: 2022

It is known that a Sleptsov net, with multiple firing a transition at a step, runs exponentially faster than a Petri net opening prospects for its application as a graphical language of concurrent programming. We provide classification of place-transition nets based on firability rules considering general definitions and their strong and weak variants. We introduce and study a strong Sleptsov net, where a transition with the maximal firing multiplicity fires at a step, and prove that it is Turing-complete. We follow the proof pattern of Peterson applied to prove that an inhibitor Petri net is Turing-complete simulating a Shepherdson and Sturgis register machine. The central construct of our proof is a strong Sleptsov net that checks whether a register value (place marking) equals zero.
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Venue: arXiv cs.CV 2022
Year: 2022

Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results. However, there is a clinical need for estimation of the uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian deep learning methods have been proposed for the task but the studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results by systematically investigating a clinical dataset and a clinically relevant 5-class classification scheme, in addition to benchmark datasets and the binary classification scheme. Moreover, we derive a connection between uncertainty measures and classifier risk, from which we develop a new uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure generalizes to the clinical dataset on the binary classification scheme but not on the 5-class scheme, whereas our new uncertainty measure generalizes to the latter case.
Good Classification Measures and How to Find Them
Venue: arXiv cs.LG 2022
Year: 2022

Several performance measures can be used for evaluating classification results: accuracy, F-measure, and many others. Can we say that some of them are better than others, or, ideally, choose one measure that is best in all situations? To answer this question, we conduct a systematic analysis of classification performance measures: we formally define a list of desirable properties and theoretically analyze which measures satisfy which properties. We also prove an impossibility theorem: some desirable properties cannot be simultaneously satisfied. Finally, we propose a new family of measures satisfying all desirable properties except one. This family includes the Matthews Correlation Coefficient and a so-called Symmetric Balanced Accuracy that was not previously used in classification literature. We believe that our systematic approach gives an important tool to practitioners for adequately evaluating classification results.
Beamforming and Power Allocation for Double-RIS-aided Two-way Directional Modulation Network
Venue: arXiv cs.IT 2022
Year: 2022

To improve the information exchange rate between Alice and Bob in traditional two-way directional modulation (TWDM) network, a new double-reconfigurable intelligent surface (RIS)-aided TWDM network is proposed. To achieve the low-complexity transmitter design, two analytical precoders, one closed-form method of adjusting the RIS phase-shifting matrices, and semi-iterative power allocation (PA) strategy of maximizing secrecy sum rate (SSR) are proposed. First, the geometric parallelogram (GPG) criterion is employed to give the phase-shifting matrices of RISs. Then, two precoders, called maximizing singular value (Max-SV) and maximizing signal-to-leakage-noise ratio (Max-SLNR), are proposed to enhance the SSR. Evenly, the maximizing SSR PA with hybrid iterative closed-form (HICF) is further proposed to improve the SSR and derived to be one root of a sixth-order polynomial computed by: (1) the Newton-Raphson algorithm is repeated twice to reduce the order of the polynomial from six to four; (2) the remaining four feasible solutions can be directly obtained by the Ferrari's method. Simulation results show that using the proposed Max-SV and Max-SLNR, the proposed GPG makes a significant SSR improvement over random phase and no RIS. Given GPG, the proposed Max-SV outperforms the proposed leakage for small-scale or medium-scale RIS. Particularly, the proposed HICF PA stragey shows about ten percent performance gain over equal PA.
Neuronal Correlation: a Central Concept in Neural Network
Venue: arXiv cs.LG 2022
Year: 2022

This paper proposes to study neural networks through neuronal correlation, a statistical measure of correlated neuronal activity on the penultimate layer. We show that neuronal correlation can be efficiently estimated via weight matrix, can be effectively enforced through layer structure, and is a strong indicator of generalisation ability of the network. More importantly, we show that neuronal correlation significantly impacts on the accuracy of entropy estimation in high-dimensional hidden spaces. While previous estimation methods may be subject to significant inaccuracy due to implicit assumption on neuronal independence, we present a novel computational method to have an efficient and authentic computation of entropy, by taking into consideration the neuronal correlation. In doing so, we install neuronal correlation as a central concept of neural network.
Physical geometry of channel degradation
Venue: arXiv cs.IT 2022
Year: 2022

We outline a geometrical correspondence between capacity and effective free energy minima of discrete memoryless channels. This correspondence informs the behavior of a timescale that is important in effective statistical physics.
Neural Implicit Mapping via Nested Neighborhoods
Venue: arXiv cs.GR 2022
Year: 2022

We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (SDF) in real-time. We rely on nested neighborhoods of zero-level sets of neural SDFs, and mappings between them. This framework supports animations and achieves real-time performance without the use of spatial data-structures. It consists of three uncoupled algorithms representing the rendering steps. The multiscale sphere tracing focuses on minimizing iteration time by using coarse approximations on earlier iterations. The neural normal mapping transfers details from a fine neural SDF to a surface nested on a neighborhood of its zero-level set. It is smooth and it does not depend on surface parametrizations. As a result, it can be used to fetch smooth normals for discrete surfaces such as meshes and to skip later iterations when sphere tracing level sets. Finally, we propose an algorithm for analytic normal calculation for MLPs and describe ways to obtain sequences of neural SDFs to use with the algorithms.
Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval
Venue: arXiv cs.CV 2022
Year: 2022

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset.
Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets
Venue: arXiv cs.AI 2022
Year: 2022

We introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning to find sequential patterns that occur frequently and exhibit desired properties. This allows the creation of novel pattern embeddings that are useful for knowledge extraction and predictive modeling. Finally, we present an application on customer intent prediction from digital clickstream data. Overall, we show that pattern embeddings play an integrator role between semi-structured data and machine learning models, improve the performance of the downstream task and retain interpretability.
Weight Expansion: A New Perspective on Dropout and Generalization
Venue: arXiv cs.LG 2022
Year: 2022

While dropout is known to be a successful regularization technique, insights into the mechanisms that lead to this success are still lacking. We introduce the concept of \emph{weight expansion}, an increase in the signed volume of a parallelotope spanned by the column or row vectors of the weight covariance matrix, and show that weight expansion is an effective means of increasing the generalization in a PAC-Bayesian setting. We provide a theoretical argument that dropout leads to weight expansion and extensive empirical support for the correlation between dropout and weight expansion. To support our hypothesis that weight expansion can be regarded as an \emph{indicator} of the enhanced generalization capability endowed by dropout, and not just as a mere by-product, we have studied other methods that achieve weight expansion (resp.\ contraction), and found that they generally lead to an increased (resp.\ decreased) generalization ability. This suggests that dropout is an attractive regularizer, because it is a computationally cheap method for obtaining weight expansion. This insight justifies the role of dropout as a regularizer, while paving the way for identifying regularizers that promise improved generalization through weight expansion.
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Venue: arXiv cs.LG 2022
Year: 2022

Imperative programming allows users to implement their deep neural networks (DNNs) easily and has become an essential part of recent deep learning (DL) frameworks. Recently, several systems have been proposed to combine the usability of imperative programming with the optimized performance of symbolic graph execution. Such systems convert imperative Python DL programs to optimized symbolic graphs and execute them. However, they cannot fully support the usability of imperative programming. For example, if an imperative DL program contains a Python feature with no corresponding symbolic representation (e.g., third-party library calls or unsupported dynamic control flows) they fail to execute the program. To overcome this limitation, we propose Terra, an imperative-symbolic co-execution system that can handle any imperative DL programs while achieving the optimized performance of symbolic graph execution. To achieve this, Terra builds a symbolic graph by decoupling DL operations from Python features. Then, Terra conducts the imperative execution to support all Python features, while delegating the decoupled operations to the symbolic execution. We evaluated the performance improvement and coverage of Terra with ten imperative DL programs for several DNN architectures. The results show that Terra can speed up the execution of all ten imperative DL programs, whereas AutoGraph, one of the state-of-the-art systems, fails to execute five of them.
Task Scheduling in Cloud Computing Using Hybrid Meta-heuristic: A Review
Venue: arXiv cs.DC 2022
Year: 2022

In recent years with the advent of high bandwidth internet access availability, the cloud computing applications have boomed. With more and more applications being run over the cloud and an increase in the overall user base of the different cloud platforms, the need for highly efficient job scheduling techniques has also increased. The task of a conventional job scheduling algorithm is to determine a sequence of execution for the jobs, which uses the least resources like time, processing, memory, etc. Generally, the user requires more services and very high efficiency. An efficient scheduling technique helps in proper utilization of the resources. In this research realm, the hybrid meta-heuristic algorithms have proven to be very effective in optimizing the task scheduling by providing better cost efficiency than when singly employed. This study presents a systematic and extensive analysis of task scheduling techniques in cloud computing using the various hybrid variants of meta-heuristic methods, like Genetic Algorithm, Tabu Search, Harmony Search, Artificial Bee Colony, Particle Swarm Optimization, etc. In this research review, a separate section discusses the use of various performance evaluation metrics throughout the literature.
Two-level Just-in-Time Compilation with One Interpreter and One Engine
Venue: arXiv cs.PL 2022
Year: 2022

Modern, powerful virtual machines such as those running Java or JavaScript support multi-tier JIT compilation and optimization features to achieve their high performance. However, implementing and maintaining several compilers/optimizers that interact with each other requires hard-working VM developers. In this paper, we propose a technique to realize two-level JIT compilation in RPython without implementing several interpreters or compilers from scratch. As a preliminary realization, we created adaptive RPython, which performs both baseline JIT compilation based on threaded code and tracing JIT compilation. We also implemented a small programming language with it. Furthermore, we preliminarily evaluated the performance of that small language, and our baseline JIT compilation ran 1.77x faster than the interpreter-only execution. Furthermore, we observed that when we apply an optimal JIT compilation for different target methods, the performance was mostly the same as the one optimizing JIT compilation strategy, saving about 40 % of the compilation code size.
SpiroMask: Measuring Lung Function Using Consumer-Grade Masks
Venue: arXiv cs.HC 2022
Year: 2022

According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution. Regular lung health monitoring can lead to prognoses about deteriorating lung health conditions. This paper presents our system SpiroMask that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for continuous lung health monitoring. We evaluate our approach on 48 participants (including 14 with lung health issues) and find that we can estimate parameters such as lung volume and respiration rate within the approved error range by the American Thoracic Society (ATS). Further, we show that our approach is robust to sensor placement inside the mask.
Cuckoo Trie: Exploiting Memory-Level Parallelism for Efficient DRAM Indexing
Venue: arXiv cs.DS 2022
Year: 2022

We present the Cuckoo Trie, a fast, memory-efficient ordered index structure. The Cuckoo Trie is designed to have memory-level parallelism -- which a modern out-of-order processor can exploit to execute DRAM accesses in parallel -- without sacrificing memory efficiency. The Cuckoo Trie thus breaks a fundamental performance barrier faced by current indexes, whose bottleneck is a series of dependent pointer-chasing DRAM accesses -- e.g., traversing a search tree path -- which the processor cannot parallelize. Our evaluation shows that the Cuckoo Trie outperforms state-of-the-art-indexes by up to 20%--360% on a variety of datasets and workloads, typically with a smaller or comparable memory footprint.
Efficient and Robust Classification for Sparse Attacks
Venue: arXiv cs.LG 2022
Year: 2022

In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy. Parallel to this, we have also witnessed how fragile the very same prediction models are: tiny perturbations to the inputs can cause misclassification errors throughout entire datasets. In this paper, we consider perturbations bounded by the $\ell_0$--norm, which have been shown as effective attacks in the domains of image-recognition, natural language processing, and malware-detection. To this end, we propose a novel defense method that consists of "truncation" and "adversarial training". We then theoretically study the Gaussian mixture setting and prove the asymptotic optimality of our proposed classifier. Motivated by the insights we obtain, we extend these components to neural network classifiers. We conduct numerical experiments in the domain of computer vision using the MNIST and CIFAR datasets, demonstrating significant improvement for the robust classification error of neural networks.