OpenRaiser/Intern-Atlas huggingface dataset:
A Content Driven Resource Allocation Scheme for Video Transmission in Vehicular Networks
Venue: arXiv cs.NI 2020
Year: 2020

With the growing computer vision applications, lots of videos are transmitted for content analysis, the way to allocate resources can affect the performance of video content analysis. For this purpose, the traditional resource allocation schemes for video transmission in vehicular networks, such as qualityof-service (QoS) based or quality-of-experience (QoE) based schemes, are no longer optimal anymore. In this paper, we propose an efficient content driven resource allocation scheme for vehicles equipped with cameras under bandwidth constraints in order to improve the video content analysis performance. The proposed resource allocation scheme is based on maximizing the quality-of-content (QoC), which is related to the content analysis performance. A QoC based assessment model is first proposed. Then, the resource allocation problem is converted to a solvable convex optimization problem. Finally, simulation results show the better performance of our proposed scheme than the existing schemes like QoE based schemes.
Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification
Venue: arXiv cs.IT 2020
Year: 2020

In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool to support advanced single- and multi-user MIMO beamforming/precoding. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas, leading to quantization complexity, as well as, feedback overhead issues for larger MIMO systems. We have recently proposed a multi-stage recursive Grassmannian quantizer that enables a significant complexity reduction of CSI quantization. In this paper, we show that this recursive quantizer can effectively be combined with deep learning classification to further reduce the complexity, and that it can exploit temporal channel correlations to reduce the CSI feedback overhead.
Towards a Measure of Individual Fairness for Deep Learning
Venue: arXiv cs.AI 2020
Year: 2020

Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predictions. We propose a novel measure of individual fairness, called prediction sensitivity, that approximates the extent to which a particular prediction is dependent on a protected attribute. We show how to compute prediction sensitivity using standard automatic differentiation capabilities present in modern deep learning frameworks, and present preliminary empirical results suggesting that prediction sensitivity may be effective for measuring bias in individual predictions.
Breaking the Memory Wall for AI Chip with a New Dimension
Venue: arXiv cs.AR 2020
Year: 2020

Recent advancements in deep learning have led to the widespread adoption of artificial intelligence (AI) in applications such as computer vision and natural language processing. As neural networks become deeper and larger, AI modeling demands outstrip the capabilities of conventional chip architectures. Memory bandwidth falls behind processing power. Energy consumption comes to dominate the total cost of ownership. Currently, memory capacity is insufficient to support the most advanced NLP models. In this work, we present a 3D AI chip, called Sunrise, with near-memory computing architecture to address these three challenges. This distributed, near-memory computing architecture allows us to tear down the performance-limiting memory wall with an abundance of data bandwidth. We achieve the same level of energy efficiency on 40nm technology as competing chips on 7nm technology. By moving to similar technologies as other AI chips, we project to achieve more than ten times the energy efficiency, seven times the performance of the current state-of-the-art chips, and twenty times of memory capacity as compared with the best chip in each benchmark.
A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
Venue: arXiv cs.LG 2020
Year: 2020

The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user flexibility by allowing bidders to submit bids for combinations of different items instead of only for individual items. However, the problem of allocating items among the bidders to maximize the auctioneers" revenue, i.e., the winner determination problem (WDP), is NP-complete to solve and inapproximable. Existing works for WDPs are generally based on mathematical optimization techniques and most of them focus on the single-unit WDP, where each item only has one unit. On the contrary, few works consider the multi-unit WDP in which each item may have multiple units. Given that the multi-unit WDP is more complicated but prevalent in cloud computing, we propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss. Specifically, we model the multi-unit WDP as an augmented bipartite bid-item graph and use a graph neural network (GNN) with half-convolution operations to learn the probability of each bid belonging to the optimal allocation. To improve the sample generation efficiency and decrease the number of needed labeled instances, we propose two different sample generation processes. We also develop two novel graph-based post-processing algorithms to transform the outputs of the GNN into feasible solutions. Through simulations on both synthetic instances and a specific virtual machine (VM) allocation problem in a cloud computing platform, we validate that our proposed method can approach optimal performance with low complexity and has good generalization ability in terms of problem size and user-type distribution.
Leader: Prefixing a Length for Faster Word Vector Serialization
Venue: arXiv cs.CL 2020
Year: 2020

Two competing file formats have become the de facto standards for distributing pre-trained word embeddings. Both are named after the most popular pre-trained embeddings that are distributed in that format. The GloVe format is an entirely text based format that suffers from huge file sizes and slow reads, and the word2vec format is a smaller binary format that mixes a textual representation of words with a binary representation of the vectors themselves. Both formats have problems that we solve with a new format we call the Leader format. We include a word length prefix for faster reads while maintaining the smaller file size a binary format offers. We also created a minimalist library to facilitate the reading and writing of various word vector formats, as well as tools for converting pre-trained embeddings to our new Leader format.
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment
Venue: arXiv cs.LG 2020
Year: 2020

Manual annotation of ICD-9 codes is a time consuming and error-prone process. Deep learning based systems tackling the problem of automated ICD-9 coding have achieved competitive performance. Given the increased proliferation of electronic medical records, such automated systems are expected to eventually replace human coders. In this work, we investigate how a simple typo-based adversarial attack strategy can impact the performance of state-of-the-art models for the task of predicting the top 50 most frequent ICD-9 codes from discharge summaries. Preliminary results indicate that a malicious adversary, using gradient information, can craft specific perturbations, that appear as regular human typos, for less than 3% of words in the discharge summary to significantly affect the performance of the baseline model.
New Rewriter Features in FGL
Venue: arXiv cs.LO 2020
Year: 2020

FGL is a successor to GL, a proof procedure for ACL2 that allows complicated finitary conjectures to be translated into efficient Boolean function representations and proved using SAT solvers. A primary focus of FGL is to allow greater programmability using rewrite rules. While the FGL rewriter is modeled on ACL2's rewriter, we have added several features in order to make rewrite rules more powerful. A particular focus is to make it more convenient for rewrite rules to use information from the syntactic domain, allowing them to replace built-in primitives and meta rules in many cases. Since it is easier to write, maintain, and prove the soundness of rewrite rules than to do the same for rules programmed at the syntactic level, these features help make it feasible for users to precisely program the behavior or the rewriter. We describe the new features that FGL's rewriter implements, discuss the solutions to some technical problems that we encountered in their implementation, and assess the feasibility of adding these features to the ACL2 rewriter.
Computing and Proving Well-founded Orderings through Finite Abstractions
Venue: arXiv cs.LO 2020
Year: 2020

A common technique for checking properties of complex state machines is to build a finite abstraction then check the property on the abstract system -- where a passing check on the abstract system is only transferred to the original system if the abstraction is proven to be representative. This approach does require the derivation or definition of the finite abstraction, but can avoid the need for complex invariant definition. For our work in checking progress of memory transactions in microprocessors, we need to prove that transactions in complex state machines always make progress to completion. As a part of this effort, we developed a process for computing a finite abstract graph of the target state machine along with annotations on whether certain measures decrease or not on arcs in the abstract graph. We then iteratively divide the abstract graph by splitting into strongly connected components and then building a measure for every node in the abstract graph which is ensured to be reducing on every transition of the original system guaranteeing progress. For finite state target systems (e.g. hardware designs), we present approaches for extracting the abstract graph efficiently using incremental SAT through GL and then the application of our process to check for progress. We present an implementation of the Bakery algorithm as an example application.
Generating Mutually Inductive Theorems from Concise Descriptions
Venue: arXiv cs.LO 2020
Year: 2020

We describe defret-mutual-generate, a utility for proving ACL2 theorems about large mutually recursive cliques of functions. This builds on previous tools such as defret-mutual and make-flag, which automate parts of the process but still require a theorem body to be written out for each function in the clique. For large cliques, this tends to mean that certain common hypotheses and conclusions are repeated many times, making proofs difficult to read, write, and maintain. This utility automates several of the most common patterns that occur in these forms, such as including hypotheses based on formal names or types. Its input language is rich enough to support forms that have some common parts and some unique parts per function. One application of defret-mutual-generate has been to support proofs about the FGL rewriter, which consists of a mutually recursive clique of 49 functions. The use of this utility reduced the size of the forms that express theorems about this clique by an order of magnitude. It also greatly has reduced the need to edit theorem forms when changing definitions in the clique, even when adding or removing functions.
A comparison of classical and variational autoencoders for anomaly detection
Venue: arXiv cs.CV 2020
Year: 2020

This paper analyzes and compares a classical and a variational autoencoder in the context of anomaly detection. To better understand their architecture and functioning, describe their properties and compare their performance, it explores how they address a simple problem: reconstructing a line with a slope.
HINT3: Raising the bar for Intent Detection in the Wild
Venue: arXiv cs.CL 2020
Year: 2020

Intent Detection systems in the real world are exposed to complexities of imbalanced datasets containing varying perception of intent, unintended correlations and domain-specific aberrations. To facilitate benchmarking which can reflect near real-world scenarios, we introduce 3 new datasets created from live chatbots in diverse domains. Unlike most existing datasets that are crowdsourced, our datasets contain real user queries received by the chatbots and facilitates penalising unwanted correlations grasped during the training process. We evaluate 4 NLU platforms and a BERT based classifier and find that performance saturates at inadequate levels on test sets because all systems latch on to unintended patterns in training data.
Weakly-supervised Salient Instance Detection
Venue: arXiv cs.CV 2020
Year: 2020

Existing salient instance detection (SID) methods typically learn from pixel-level annotated datasets. In this paper, we present the first weakly-supervised approach to the SID problem. Although weak supervision has been considered in general saliency detection, it is mainly based on using class labels for object localization. However, it is non-trivial to use only class labels to learn instance-aware saliency information, as salient instances with high semantic affinities may not be easily separated by the labels. We note that subitizing information provides an instant judgement on the number of salient items, which naturally relates to detecting salient instances and may help separate instances of the same class while grouping different parts of the same instance. Inspired by this insight, we propose to use class and subitizing labels as weak supervision for the SID problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids. This complementary information is further fused to produce salient instance maps. We conduct extensive experiments to demonstrate that the proposed method plays favorably against carefully designed baseline methods adapted from related tasks.
Aligning Intraobserver Agreement by Transitivity
Venue: arXiv cs.CL 2020
Year: 2020

Annotation reproducibility and accuracy rely on good consistency within annotators. We propose a novel method for measuring within annotator consistency or annotator Intraobserver Agreement (IA). The proposed approach is based on transitivity, a measure that has been thoroughly studied in the context of rational decision-making. The transitivity measure, in contrast with the commonly used test-retest strategy for annotator IA, is less sensitive to the several types of bias introduced by the test-retest strategy. We present a representation theorem to the effect that relative judgement data that meet transitivity can be mapped to a scale (in terms of measurement theory). We also discuss a further application of transitivity as part of data collection design for addressing the problem of the quadratic complexity of data collection of relative judgements.
SoK: On the Security Challenges and Risks of Multi-Tenant FPGAs in the Cloud
Venue: arXiv cs.CR 2020
Year: 2020

In their continuous growth and penetration into new markets, Field Programmable Gate Arrays (FPGAs) have recently made their way into hardware acceleration of machine learning among other specialized compute-intensive services in cloud data centers, such as Amazon and Microsoft. To further maximize their utilization in the cloud, several academic works propose the spatial multi-tenant deployment model, where the FPGA fabric is simultaneously shared among mutually mistrusting clients. This is enabled by leveraging the partial reconfiguration property of FPGAs, which allows to split the FPGA fabric into several logically isolated regions and reconfigure the functionality of each region independently at runtime. In this paper, we survey industrial and academic deployment models of multi-tenant FPGAs in the cloud computing settings, and highlight their different adversary models and security guarantees, while shedding light on their fundamental shortcomings from a security standpoint. We further survey and classify existing academic works that demonstrate a new class of remotely exploitable physical attacks on multi-tenant FPGA devices, where these attacks are launched remotely by malicious clients sharing physical resources with victim users. Through investigating the problem of end-to-end multi-tenant FPGA deployment more comprehensively, we reveal how these attacks actually represent only one dimension of the problem, while various open security and privacy challenges remain unaddressed. We conclude with our insights and a call for future research to tackle these challenges.
Designing everyday automation with well-being in mind
Venue: arXiv cs.HC 2020
Year: 2020

Nowadays, automation not only permeates industry but also becomes a substantial part of our private, everyday lives. Driven by the idea of increased convenience and more time for the "important things in life," automation relieves us from many daily chores - robots vacuum floors and automated coffee makers produce supposedly barista-quality coffee on the press of a button. In many cases, these offers are embraced by people without further questioning. However, while we save time by delegating more and more everyday activities to automation, we also may lose chances for enjoyable and meaningful experiences. In two field studies, we demonstrate that a manual process has experiential benefits over more automated processes by using the example of coffee-making. We present a way to account for potential experiential costs of everyday automation and strategies of how to design interaction with automation to reconcile experience with the advantages of a more and more powerful automation.
Residual acoustic echo suppression based on efficient multi-task convolutional neural network
Venue: arXiv cs.SD 2020
Year: 2020

Acoustic echo degrades the user experience in voice communication systems thus needs to be suppressed completely. We propose a real-time residual acoustic echo suppression (RAES) method using an efficient convolutional neural network. The double talk detector is used as an auxiliary task to improve the performance of RAES in the context of multi-task learning. The training criterion is based on a novel loss function, which we call as the suppression loss, to balance the suppression of residual echo and the distortion of near-end signals. The experimental results show that the proposed method can efficiently suppress the residual echo under different circumstances.
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning
Venue: arXiv cs.LG 2020
Year: 2020

We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers). We assume the communication network between the workers is synchronized and can be modeled as a doubly-stochastic mixing matrix without having any master node. In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. Using alternating-direction-method-of-multipliers (ADMM) along with a layerwise convex optimization approach, we propose a decentralized learning algorithm which enjoys low computational complexity and communication cost among the workers. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place. Finally, we experimentally illustrate the time complexity and convergence behavior of the algorithm.
Hotel Recommendation System Based on User Profiles and Collaborative Filtering
Venue: arXiv cs.LG 2020
Year: 2020

Nowadays, people start to use online reservation systems to plan their vacations since they have vast amount of choices available. Selecting when and where to go from this large-scale options is getting harder. In addition, sometimes consumers can miss the better options due to the wealth of information to be found on the online reservation systems. In this sense, personalized services such as recommender systems play a crucial role in decision making. Two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system. This paper presents a new hybrid hotel recommendation system that has been developed by combining content-based and collaborative filtering approaches that recommends customer the hotel they need and save them from time loss.
Online platforms of public participation -- a deliberative democracy or a delusion?
Venue: arXiv cs.SI 2020
Year: 2020

Trust and confidence in democratic institutions is at an all-time low. At the same time, many of the complex issues faced by city administrators and politicians remain unresolved. To tackle these concerns, many argue that citizens should, through the use of digital platforms, have greater involvement in decision-making processes. This paper describes research into two such platforms, 'Decide Madrid' and 'Better Reykjavik'. Through the use of interviews, questionnaires, ethnographic observation, and analysis of platform data, the study will determine if these platforms provide greater participation or simply replicate what is already offered by numerous other digital tools. The findings so far suggest that to be successful platforms must take on a form of deliberative democracy, allowing for knowledge co-production and the emergence of collective intelligence. Based on this, we aim to identify key features of sustainable models of online participation.
DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data
Venue: arXiv cs.DC 2020
Year: 2020

In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowd-sourcing participants. For mitigating the privacy concern, while traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multi-dimensional crowd-sourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy-preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowd-sourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multi-dimensional infinite crowd-data streams. Extensive experiments on several datasets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.