OpenRaiser/Intern-Atlas huggingface dataset:
IAUnet: Global Context-Aware Feature Learning for Person Re-Identification
Venue: arXiv cs.CV 2020
Year: 2020

Person re-identification (reID) by CNNs based networks has achieved favorable performance in recent years. However, most of existing CNNs based methods do not take full advantage of spatial-temporal context modeling. In fact, the global spatial-temporal context can greatly clarify local distractions to enhance the target feature representation. To comprehensively leverage the spatial-temporal context information, in this work, we present a novel block, Interaction-Aggregation-Update (IAU), for high-performance person reID. Firstly, Spatial-Temporal IAU (STIAU) module is introduced. STIAU jointly incorporates two types of contextual interactions into a CNN framework for target feature learning. Here the spatial interactions learn to compute the contextual dependencies between different body parts of a single frame. While the temporal interactions are used to capture the contextual dependencies between the same body parts across all frames. Furthermore, a Channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts. Therefore, the IAU block enables the feature to incorporate the globally spatial, temporal, and channel context. It is lightweight, end-to-end trainable, and can be easily plugged into existing CNNs to form IAUnet. The experiments show that IAUnet performs favorably against state-of-the-art on both image and video reID tasks and achieves compelling results on a general object categorization task. The source code is available at https://github.com/blue-blue272/ImgReID-IAnet.
Generalisation of Cyberbullying Detection
Venue: arXiv cs.CL 2020
Year: 2020

Cyberbullying is a problem in today's ubiquitous online communities. Filtering it out of online conversations has proven a challenge, and efforts have led to the creation of many different datasets, all offered as resources to train classifiers. Through these datasets, we will explore the variety of definitions of cyberbullying behaviors and the impact of these differences on the portability of one classifier to another community. By analyzing the similarities between datasets, we also gain insight on the generalization power of the classifiers trained from them. A study of ensemble models combining these classifiers will help us understand how they interact with each other.
Flow-based detection and proxy-based evasion of encrypted malware C2 traffic
Venue: arXiv cs.CR 2020
Year: 2020

State of the art deep learning techniques are known to be vulnerable to evasion attacks where an adversarial sample is generated from a malign sample and misclassified as benign. Detection of encrypted malware command and control traffic based on TCP/IP flow features can be framed as a learning task and is thus vulnerable to evasion attacks. However, unlike e.g. in image processing where generated adversarial samples can be directly mapped to images, going from flow features to actual TCP/IP packets requires crafting the sequence of packets, with no established approach for such crafting and a limitation on the set of modifiable features that such crafting allows. In this paper we discuss learning and evasion consequences of the gap between generated and crafted adversarial samples. We exemplify with a deep neural network detector trained on a public C2 traffic dataset, white-box adversarial learning, and a proxy-based approach for crafting longer flows. Our results show 1) the high evasion rate obtained by using generated adversarial samples on the detector can be significantly reduced when using crafted adversarial samples; 2) robustness against adversarial samples by model hardening varies according to the crafting approach and corresponding set of modifiable features that the attack allows for; 3) incrementally training hardened models with adversarial samples can produce a level playing field where no detector is best against all attacks and no attack is best against all detectors, in a given set of attacks and detectors. To the best of our knowledge this is the first time that level playing field feature set- and iteration-hardening are analyzed in encrypted C2 malware traffic detection.
An exploratory study of L1-specific non-words
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we explore L1-specific non-words, i.e. non-words in a target language (in this case Swedish) that are re-ranked by a different-language language model. We surmise that speakers of a certain L1 will react different to L1-specific non-words than to general non-words. We present the results from two small case studies exploring whether re-ranking non-words with different language models leads to a perceived difference in `Swedishness' (pilot study 1) and whether German and English native speakers have longer reaction times in a lexical decision task when presented with their respective L1-specific non-words (pilot study 2). Tentative results seem to indicate that L1-specific non-words are processed second-slowest, after purely Swedish-looking non-words.
Interplay of Probabilistic Shaping and Carrier Phase Recovery for Nonlinearity Mitigation
Venue: arXiv cs.IT 2020
Year: 2020

The interaction between carrier phase recovery and probabilistic amplitude shaping (PAS) in the nonlinear regime is investigated. We show that, for sufficiently high signal-to-noise ratio, the first provides the same inter-channel nonlinearity mitigation achieved by short block-length PAS.
On the existence of hidden machines in computational time hierarchies
Venue: arXiv cs.LO 2020
Year: 2020

Challenging the standard notion of totality in computable functions, one has that, given any sufficiently expressive formal axiomatic system, there are total functions that, although computable and "intuitively" understood as being total, cannot be proved to be total. In this article we show that this implies the existence of an infinite hierarchy of time complexity classes whose representative members are hidden from (or unknown by) the respective formal axiomatic systems. Although these classes contain total computable functions, there are some of these functions for which the formal axiomatic system cannot recognize as belonging to a time complexity class. This leads to incompleteness results regarding formalizations of computational complexity.
Benchmarking 50-Photon Gaussian Boson Sampling on the Sunway TaihuLight
Venue: arXiv cs.DC 2020
Year: 2020

Boson sampling is expected to be one of an important milestones that will demonstrate quantum supremacy. The present work establishes the benchmarking of Gaussian boson sampling (GBS) with threshold detection based on the Sunway TaihuLight supercomputer. To achieve the best performance and provide a competitive scenario for future quantum computing studies, the selected simulation algorithm is fully optimized based on a set of innovative approaches, including a parallel scheme and instruction-level optimizing method. Furthermore, data precision and instruction scheduling are handled in a sophisticated manner by an adaptive precision optimization scheme and a DAG-based heuristic search algorithm, respectively. Based on these methods, a highly efficient and parallel quantum sampling algorithm is designed. The largest run enables us to obtain one Torontonian function of a 100 x 100 submatrix from 50-photon GBS within 20 hours in 128-bit precision and 2 days in 256-bit precision.
Noise-Aware Texture-Preserving Low-Light Enhancement
Venue: arXiv cs.CV 2020
Year: 2020

A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise removal and natural texture preservation through a low-complexity solution. Its cost function includes an estimated piece-wise smooth illumination map and a noise-free texture-preserving reflectance map. Afterwards, illumination is adjusted to form the enhanced image together with the reflectance map. Extensive experiments are conducted on common low-light image enhancement datasets to demonstrate the superior performance of NATLE.
An Ensemble Learning Approach for In-situ Monitoring of FPGA Dynamic Power
Venue: arXiv cs.AR 2020
Year: 2020

As field-programmable gate arrays become prevalent in critical application domains, their power consumption is of high concern. In this paper, we present and evaluate a power monitoring scheme capable of accurately estimating the runtime dynamic power of FPGAs in a fine-grained timescale, in order to support emerging power management techniques. In particular, we describe a novel and specialized ensemble model which can be decomposed into multiple customized decision-tree-based base learners. To aid in model synthesis, a generic computer-aided design flow is proposed to generate samples, select features, tune hyperparameters and train the ensemble estimator. Besides this, a hardware realization of the trained ensemble estimator is presented for on-chip real-time power estimation. In the experiments, we first show that a single decision tree model can achieve prediction error within 4.51% of a commercial gate-level power estimation tool, which is 2.41--6.07x lower than provided by the commonly used linear model. More importantly, we study the extra gains in inference accuracy using the proposed ensemble model. Experimental results reveal that the ensemble monitoring method can further improve the accuracy of power predictions to within a maximum error of 1.90%. Moreover, the lookup table (LUT) overhead of the ensemble monitoring hardware employing up to 64 base learners is within 1.22% of the target FPGA, indicating its light-weight and scalable characteristics.
Decision Tree Based Hardware Power Monitoring for Run Time Dynamic Power Management in FPGA
Venue: arXiv cs.AR 2020
Year: 2020

Fine-grained runtime power management techniques could be promising solutions for power reduction. Therefore, it is essential to establish accurate power monitoring schemes to obtain dynamic power variation in a short period (i.e., tens or hundreds of clock cycles). In this paper, we leverage a decision-tree-based power modeling approach to establish fine-grained hardware power monitoring on FPGA platforms. A generic and complete design flow is developed to implement the decision tree power model which is capable of precisely estimating dynamic power in a fine-grained manner. A flexible architecture of the hardware power monitoring is proposed, which can be instrumented in any RTL design for runtime power estimation, dispensing with the need for extra power measurement devices. Experimental results of applying the proposed model to benchmarks with different resource types reveal an average error up to 4% for dynamic power estimation. Moreover, the overheads of area, power and performance incurred by the power monitoring circuitry are extremely low. Finally, we apply our power monitoring technique to the power management using phase shedding with an on-chip multi-phase regulator as a proof of concept and the results demonstrate 14% efficiency enhancement for the power supply of the FPGA internal logic.
Data Programming by Demonstration: A Framework for Interactively Learning Labeling Functions
Venue: arXiv cs.LG 2020
Year: 2020

Data programming is a programmatic weak supervision approach to efficiently curate large-scale labeled training data. Writing data programs (labeling functions) requires, however, both programming literacy and domain expertise. Many subject matter experts have neither programming proficiency nor time to effectively write data programs. Furthermore, regardless of one's expertise in coding or machine learning, transferring domain expertise into labeling functions by enumerating rules and thresholds is not only time consuming but also inherently difficult. Here we propose a new framework, data programming by demonstration (DPBD), to generate labeling rules using interactive demonstrations of users. DPBD aims to relieve the burden of writing labeling functions from users, enabling them to focus on higher-level semantics such as identifying relevant signals for labeling tasks. We operationalize our framework with Ruler, an interactive system that synthesizes labeling rules for document classification by using span-level annotations of users on document examples. We compare Ruler with conventional data programming through a user study conducted with 10 data scientists creating labeling functions for sentiment and spam classification tasks. We find that Ruler is easier to use and learn and offers higher overall satisfaction, while providing discriminative model performances comparable to ones achieved by conventional data programming.
Understanding User Experience of COVID-19 Maps through Remote Elicitation Interviews
Venue: arXiv cs.HC 2020
Year: 2020

During the coronavirus pandemic, visualizations gained a new level of popularity and meaning for a wider audience. People were bombarded with a wide set of public health visualizations ranging from simple graphs to complex interactive dashboards. In a pandemic setting, where large amounts of the world population are socially distancing themselves, it becomes an urgent need to refine existing user experience evaluation methods for remote settings to understand how people make sense out of COVID-19 related visualizations. When evaluating visualizations aimed towards the general public with vastly different socio-demographic backgrounds and varying levels of technical savviness and data literacy, it is important to understand user feedback beyond aspects such as speed, task accuracy, or usability problems. As a part of this wider evaluation perspective, micro-phenomenology has been used to evaluate static and narrative visualizations to reveal the lived experience in a detailed way. Building upon these studies, we conducted a user study to understand how to employ Elicitation (aka Micro-phenomenological) interviews in remote settings. In a case study, we investigated what experiences the participants had with map-based interactive visualizations. Our findings reveal positive and negative aspects of conducting Elicitation interviews remotely. Our results can inform the process of planning and executing remote Elicitation interviews to evaluate interactive visualizations. In addition, we share recommendations regarding visualization techniques and interaction design about public health data.
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services
Venue: arXiv cs.AI 2020
Year: 2020

In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q$\&$A is the most effective way to elicit user requirements. Obviously, complex Q$\&$A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement elicitation method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.
TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Data
Venue: arXiv cs.GR 2020
Year: 2020

Multidimensional Projection is a fundamental tool for high-dimensional data analytics and visualization. With very few exceptions, projection techniques are designed to map data from a high-dimensional space to a visual space so as to preserve some dissimilarity (similarity) measure, such as the Euclidean distance for example. In fact, although adopting distinct mathematical formulations designed to favor different aspects of the data, most multidimensional projection methods strive to preserve dissimilarity measures that encapsulate geometric properties such as distances or the proximity relation between data objects. However, geometric relations are not the only interesting property to be preserved in a projection. For instance, the analysis of particular structures such as clusters and outliers could be more reliably performed if the mapping process gives some guarantee as to topological invariants such as connected components and loops. This paper introduces TopoMap, a novel projection technique which provides topological guarantees during the mapping process. In particular, the proposed method performs the mapping from a high-dimensional space to a visual space, while preserving the 0-dimensional persistence diagram of the Rips filtration of the high-dimensional data, ensuring that the filtrations generate the same connected components when applied to the original as well as projected data. The presented case studies show that the topological guarantee provided by TopoMap not only brings confidence to the visual analytic process but also can be used to assist in the assessment of other projection methods.
End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
Venue: arXiv cs.NE 2020
Year: 2020

This paper introduces a novel "all-spike" low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs). In the proposed system, event-driven neuromorphic sensors produce asynchronous time-encoded data streams that are encoded by an SNN, whose output spiking signals are pulse modulated via IR and transmitted over general frequence-selective channels; while the receiver's inputs are obtained via hard detection of the received signals and fed to an SNN for classification. We introduce an end-to-end training procedure that treats the cascade of encoder, channel, and decoder as a probabilistic SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC). The proposed system, termed NeuroJSCC, is compared to conventional synchronous frame-based and uncoded transmissions in terms of latency and accuracy. The experiments confirm that the proposed end-to-end neuromorphic edge architecture provides a promising framework for efficient and low-latency remote sensing, communication, and inference.
Automated identification of metamorphic test scenarios for an ocean-modeling application
Venue: arXiv cs.SE 2020
Year: 2020

Metamorphic testing seeks to validate software in the absence of test oracles. Our application domain is ocean modeling, where test oracles often do not exist, but where symmetries of the simulated physical systems are known. In this short paper we present work in progress for automated generation of metamorphic test scenarios using machine learning. Metamorphic testing may be expressed as f(g(X))=h(f(X)) with f being the application under test, with input data X, and with the metamorphic relation (g, h). Automatically generated metamorphic relations can be used for constructing regression tests, and for comparing different versions of the same software application. Here, we restrict to h being the identity map. Then, the task of constructing tests means finding different g which we tackle using machine learning algorithms. These algorithms typically minimize a cost function. As one possible g is already known to be the identity map, for finding a second possible g, we construct the cost function to minimize for g being a metamorphic relation and to penalize for g being the identity map. After identifying the first metamorphic relation, the procedure is repeated with a cost function rewarding g that are orthogonal to previously found metamorphic relations. For experimental evaluation, two implementations of an ocean-modeling application will be subjected to the proposed method with the objective of presenting the use of metamorphic relations to test the implementations of the applications.
Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark
Venue: arXiv cs.LG 2020
Year: 2020

In the last few years, Automated Machine Learning (AutoML) has gained much attention. With that said, the question arises whether AutoML can outperform results achieved by human data scientists. This paper compares four AutoML frameworks on 12 different popular datasets from OpenML; six of them supervised classification tasks and the other six supervised regression ones. Additionally, we consider a real-life dataset from one of our recent projects. The results show that the automated frameworks perform better or equal than the machine learning community in 7 out of 12 OpenML tasks.
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency
Venue: arXiv cs.CV 2020
Year: 2020

Accurate real depth annotations are difficult to acquire, needing the use of special devices such as a LiDAR sensor. Self-supervised methods try to overcome this problem by processing video or stereo sequences, which may not always be available. Instead, in this paper, we propose a domain adaptation approach to train a monocular depth estimation model using a fully-annotated source dataset and a non-annotated target dataset. We bridge the domain gap by leveraging semantic predictions and low-level edge features to provide guidance for the target domain. We enforce consistency between the main model and a second model trained with semantic segmentation and edge maps, and introduce priors in the form of instance heights. Our approach is evaluated on standard domain adaptation benchmarks for monocular depth estimation and show consistent improvement upon the state-of-the-art.
Cyber Situation Awareness Monitoring and Proactive Response for Enterprises on the Cloud
Venue: arXiv cs.CR 2020
Year: 2020

The cloud model allows many enterprises able to outsource computing resources at an affordable price without having to commit the expense upfront. Although the cloud providers are responsible for the security of the cloud, there are still many security concerns due to inherently complex model the cloud providers operate on (e.g.,multi-tenancy). In addition, the enterprises whose services have migrated into the cloud have a preference for their own cybersecurity situation awareness capability on top of the security mechanisms provided by the cloud providers. In this way, the enterprises can monitor the performance of the security offerings of the cloud and have a choice to decide and select potential response strategies more appropriate to the enterprise in the presence of the attack where the defense provided by the cloud doesn't work for them. However, some response strategies, such as Moving Target Defense (MTD) techniques shown to be effective to secure cloud, cannot be deployed by the enterprise themselves. In this paper, we propose a framework that enables better collaboration between enterprises and cloud providers. Our proposed framework, which offers more in-depth security analysis based on the set of most advanced security metrics, allows the security experts of the enterprise to obtain better situational awareness in the cloud. With better and more effective situation awareness of cloud security, our framework can support better decision making and further allows to deploy more appropriate threat responses to protect the outsourced resources. We also propose a secure protocol which can facilitate more secure communication between the enterprises and cloud provider. Using our proposed secure protocol, which is based on authentication and key exchange mechanism, the enterprises can send a secure request to the cloud provider to perform a selected defensive strategy.