Reviewerly/exHarmony huggingface dataset:
LightSEEN: Real-Time Unknown Traffic Discovery via Lightweight Siamese Networks

With the increase in the proportion of encrypted network traffic, encrypted traffic identification (ETI) is becoming a critical research topic for network management and security. At present, ETI under closed world assumption has been adequately studied. However, when the models are applied to the realistic environment, they will face unknown traffic identification challenges and model efficiency requirements. Considering these problems, in this paper, we propose a lightweight unknown traffic discovery model LightSEEN for open-world traffic classification and model update under practical conditions. The overall structure of LightSEEN is based on the Siamese network, which takes three simplified packet feature vectors as input on one side, uses the multihead attention mechanism to parallelly capture the interactions among packets, and adopts techniques including 1D-CNN and ResNet to promote the extraction of deep-level flow features and the convergence speed of the network. The effectiveness and efficiency of the proposed model are evaluated on two public data sets. The results show that the effectiveness of LightSEEN is overall at the same level as the state-of-the-art method and LightSEEN has even better true detection rate, but the parameter used in LightSEEN is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>0.51</mn> <mo>%</mo> </math> of the baseline and its average training time is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>37.9</mn> <mo>%</mo> </math> of the baseline.
GPU-Efficient Dense Convolutional Network for Real-time Semantic Segmentation

Real-time semantic segmentation is a challenging task as both accuracy and inference speed need to be considered simultaneously. In real-world applications, it is usually achieved by deploying a deep neural network in modern GPU device. However, most of the work focused on real-time semantic segmentation is designed by significantly reducing computation complexity and model size. There are other factors that have a significant impact on inference speed are overlooked, especially when the network is running in modern GPU device. In this paper, we focus on designing a GPU-efficient network as backbone for real-time semantic segmentation. Dense connectivity can preserve and accumulate feature maps of multiple receptive fields and is therefore ideal for semantic segmentation. Therefore, we design a GPU-efficient network (DenseENet) with dense connectivity. The proposed DenseENet shows an obvious advantage in balancing accuracy and inference speed in modern GPU device. Specifically, on Cityscapes test set, DenseENet with a simple FCN decoder achieves 75.2% mIoU with 83.6 FPS for an input of 1024 × 2048 resolution and 73.6% mIoU with 132 FPS for an input of 768 × 1536 resolution on a single GTX 1080Ti card.
Improving Tail-Class Representation with Centroid Contrastive Learning

In vision domain, large-scale natural datasets typically exhibit long-tailed distribution which has large class imbalance between head and tail classes. This distribution poses difficulty in learning good representations for tail classes. Recent developments have shown good long-tailed model can be learnt by decoupling the training into representation learning and classifier balancing. However, these works pay insufficient consideration on the long-tailed effect on representation learning. In this work, we propose interpolative centroid contrastive learning (ICCL) to improve long-tailed representation learning. ICCL interpolates two images from a class-agnostic sampler and a class-aware sampler, and trains the model such that the representation of the interpolative image can be used to retrieve the centroids for both source classes. We demonstrate the effectiveness of our approach on multiple long-tailed image classification benchmarks.
Verifiable Spatial Range Query Over Encrypted Cloud Data in VANET

With the popularization of the geo-positioning technologies and Location Based Service (LBS), the spatial range query has gained increasing attention in VANET. To eliminate local data storage and computation burden, the data owner outsources his/her spatial data and the processing of range query tasks to location based service providers. However, outsourcing tasks may bring the risk of privacy leakage as location based service providers and clients, including data owner and data users are not in the same trusted domain. Besides, malicious service providers may forge or tamper retrieval results due to interest incentives such as saving storage or computational capacity. Therefore, we first propose and construct the Verifiable Spatial Range Query (VSRQ) scheme in VANET by using the hierarchical cell based encoding method, G-tree and the accumulator. To guarantee the correctness and completeness of search results, we leverage an accumulator-based technology. Then, we develop an improved G-tree to accelerate query processing and extend it to support multi-dimensional range queries. Security analysis shows that basic VSRQ and extension of VSRQ schemes are secure in terms of the privacy of index and confidentiality of spatial data. Extensive experiments further show that, basic (or extension of) VSRQ scheme can achieve result integrity while supporting spatial range query and lower computational and storage burden compared with existing schemes.
Enabling Ubiquitous Personal Fabrication by Deconstructing Established Notions of Artifact Modeling

With the notion of personal fabrication, users are handed industry-level processes to design and manufacture arbitrary physical artifacts. While personal fabrication is a powerful opportunity, it is currently employed by hobbyists and enthusiasts. Consumers, accounting for a majority of the population, still employ workflows like shopping to acquire physical artifacts. The core of my thesis focuses on partially or fully omitting steps of modeling, by relying on outsourced design effort, remixing, and low-effort interactions. Through such deliberate omission of workflow steps, the required effort can be reduced. Instead of starting ”from scratch”, users may remix existing designs, tune parametric designs or merely retrieve their desired artifacts. This moves processes in personal fabrication towards shopping-like interactions, away from complex but powerful industrial CAD (computer-aided design) systems. Instead of relegating design processes to a disconnected workstation, users may conduct search, remix, and preview procedures in-situ, at the location of use for the future artifact. This may simplify the transfer of requirements from the physical environment. Low-effort–high-expressivity fabrication workflows may not be easy to achieve, but crucial for widespread dissemination of personal fabrication. The broader vision behind my focus on ”ubiquitous personal fabrication” is one where any person can create highly personalized artifacts that suit their unique aesthetic and functional needs, without having to define and model every single detail of the artifact.
Improving Off-road Planning Techniques with Learned Costs from Physical Interactions

Autonomous ground vehicles have improved greatly over the past decades, but they still have their limitations when it comes to off-road environments. There is still a need for planning techniques that effectively handle physical interactions between a vehicle and its surroundings. We present a method of modifying a standard path planning algorithm to address these problems by incorporating a learned model to account for complexities that would be too hard to address manually. The model predicts how well a vehicle will be able to follow a potential plan in a given environment. These predictions are then used to assign costs to their associated paths, where the path predicted to be the most feasible will be output as the final path. This results in a planner that doesn't rely solely on engineered features to evaluate traversability of obstacles, and can also choose a better path based on an understanding of its own capability that it has learned from previous interactions. This modification was integrated into the Hybrid A* algorithm and experimental results demonstrated an improvement of 14.29% over the original version on a physical platform.
Information Design for Small Screens: Toward Smart Glass Use in Guidance for Industrial Maintenance

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> Smart glasses and other extended reality (XR) solutions provide new ways of utilizing technical documentation with hands-busy tasks in the field. Scaling up the use of XR solutions in industry has been difficult due to the manual authoring of content for each device and task. Therefore, authoring solutions and information design methods need to be developed to scale content automatically to different devices and applications. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> Related work includes smart glasses and industrial maintenance work, categorization based on users' skill levels, and standardized guidelines in information design. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</b> 1. How should information content be designed and created to support use in smart glasses and other small-screen devices in addition to existing delivery channels? 2. How can the same information content be utilized to deliver relevant content to users based on their skill levels? 3. Are the users of technical instructions ready to accept smart glasses and XR as a delivery channel? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</b> We describe a study that focused on designing maintenance instructions for small screens. The information was authored in DITA XML format, and a smart glass application was used in user tests to evaluate the delivery and usability of the information. We used thinking aloud and participant observation as well as questionnaires to collect data. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results and discussion:</b> The chosen information design methods successfully compressed technical information, and automatic filtering of content supported different use cases. Participants were enthusiastic about the use of smart glasses, and the instructions helped in performing tasks. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</b> Information designed with the user-centered approach of minimalism works best with instructions on small screens, and filtering information using DITA XML elements is an efficient way to scale information for different user needs.
Dynamic Decision Model of Real Estate Investment Portfolio Based on Wireless Network Communication and Ant Colony Algorithm

From reform and opening to the comprehensive construction of a well-off society, the rapid growth of the national economy and the advancement of urbanization have promoted the rapid development of China’s real estate industry. The real estate industry has become a pillar growth point in the development of the national economy. At the same time, China’s real estate markets also continue to mature. However, due to the short development time of my country’s real estate market, imperfect management mechanism, irregular organization, and other issues, coupled with the fierce competition and internationalization of the market investment environment, the risk of investment accumulation in the real estate industry is also increasing. Therefore, in real estate investment decision-making, it is of far-reaching significance to study how to control real estate investment risks and promote the healthy and stable development of the real estate industry. The purpose of this article is to build a set of investment portfolios based on the ant colony algorithm to diversify risks and obtain returns, so that the constructed investment portfolios will minimize the risk when the return reaches a certain amount of time. This article first gives a general introduction to wireless network communication and then analyzes the risk of real estate project investment. First, the variance is used as a measure of risk to establish a dynamic model of the real estate development project portfolio, and the ant colony algorithm is introduced to the investment risk of real estate development projects. In the dynamic analysis, an improved portfolio model was established, and the two were compared through case analysis. The experimental results show that under the condition of the same net present value and investment payback period, the ant colony algorithm based on variance is invested in lot H. The ratio is obviously higher, and the capital investment ratio of lot H based on the ant colony algorithm is obviously lower. The difference between the two is 30.1%.
UnfairGAN: An enhanced generative adversarial network for raindrop removal from a single image

Image deraining is a new challenging problem in real-world applications, such as autonomous vehicles. In a bad weather condition of heavy rainfall, raindrops, mainly hitting glasses or windshields, can significantly reduce observation ability. Moreover, raindrops spreading over the glass can yield refraction's physical effect, which seriously impedes the sightline or undermine machine learning systems. In this paper, we propose an enhanced generative adversarial network to deal with the challenging problems of raindrops. UnfairGAN is an enhanced generative adversarial network that can utilize prior high-level information, such as edges and rain estimation, to boost deraining performance. UnfairGAN can effectively conserve the essential details caused by heavy raindrops and remove artifacts caused by the instability of training the discriminator. This method is based on three main primary advantages. First, UnfairGAN consists of an advanced loss function of the discriminator that can improve the instabilities of traditional GAN. Second, UnfairGAN comprises a new advanced activation function that is able to increase the learning effectiveness of image classification and reconstruction. Finally, UnfairGAN is basically built on a new end-to-end cascade network, namely DRD-UNet, used to probe hierarchical features for image restoration effectively. When evaluating competing methods on the well-known Raindrop dataset, we achieve a peak signal-to-noise ratio value of 31.56 while retaining the essential details in the image. Besides, we introduce a new large image dataset (DeepRaindrops) for training deep learning networks of removing raindrops. In this dataset, our proposed method is superior to other state-of-the-art approaches of deraining raindrops regarding quantitative metrics and visual quality. Our source codes for UnfairGAN are available at https://github.com/ZeroZero19/UnfairGAN.git.
DenseNet for Breast Tumor Classification in Mammographic Images

Breast cancer screening is an efficient method to detect breast lesions early. The common screening techniques are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload for pathologists, and hence is prone to diagnostic errors. Thus, the aim of this study was to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to automate RoI segmentation. Then feature extraction, selection and classification were carried out by the DenseNet architecture. Finally, the precision and accuracy of the model was evaluated by the AUC, accuracy and precision metrics. To summarize, the findings of this study show that the methodology may improve the diagnosis and efficiency in automatic tumor localization through medical image classification.
Metalevel transformation of strategies

In the reflective Maude specification language, based on rewriting logic, a strategy language has been introduced to control rule rewriting while avoiding complex and verbose metalevel programs. However, just as multiple levels of reflection are required for some metaprogramming tasks, reflective manipulation and generation of strategies are convenient in multiple situations. Some examples of reflective strategy transformations are presented, which implement special forms of evaluation or extend the strategy language while preserving its advantages.
Multimodal User Satisfaction Recognition for Non-task Oriented Dialogue Systems

Multimodal dialogue systems (MDSs) are needed to allow users to converse with virtual agents that use natural language by sensing the multimodal behavior of users. One crucial step in the development of an MDS is measuring how well the dialogue system performs. Though previous research focused on the user satisfaction modeling from linguistic modality in text-to-text dialogue systems, the user satisfaction is observed by not only spoken dialogue contents but also the acoustic and visual nonverbal behaviors of users. Multimodal social signal sensing provides a solution that automatically measures dialogue systems based on subjective evaluation. With this background, we proposed a multimodal recognition model of the user using sequence modeling algorithms (RNN, LSTM, and GRU). It is a novel challenge to recognize the user satisfaction label at the dialogue level. Each label was annotated by the user based on the overall dialogue. We extracted both verbal features and nonverbal features at the exchange level (the unit is a pair of system and user utterances) and analyzed the contributions of multimodal features and unimodal features to recognize user satisfaction labels at the dialogue level. We used a multimodal user-system dialogue data corpus with user satisfaction labels at the dialogue level. To validate the recognition accuracy of the proposed multimodal modeling approach, we compared the proposed method with two models based on human perception by external human coders and the system operator (called “Wizard”) with whom the user talks. The experimental results showed that the multimodal model achieved a better performance in both classification and regression tasks. The results indicated that the performance of the multimodal model was higher than that of the human models.
On the Stability of Low Pass Graph Filter with a Large Number of Edge Rewires

Recently, the stability of graph filters has been studied as one of the key theoretical properties driving the highly successful graph convolutional neural networks (GCNs). The stability of a graph filter characterizes the effect of topology perturbation on the output of a graph filter, a fundamental building block for GCNs. Many existing results have focused on the regime of small perturbation with a small number of edge rewires. However, the number of edge rewires can be large in many applications. To study the latter case, this work departs from the previous analysis and proves a bound on the stability of graph filter relying on the filter’s frequency response. Assuming the graph filter is low pass, we show that the stability of the filter depends on perturbation to the community structure. As an application, we show that for stochastic block model graphs, the graph filter distance converges to a small constant when the number of nodes approaches infinity. Numerical simulations validate our findings.
Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models

Modeling biological dynamical systems is challenging due to the interdependence of different system components, some of which are not fully understood. To fill existing gaps in our ability to mechanistically model physiological systems, we propose to combine neural networks with physics-based models. Specifically, we demonstrate how we can approximate missing ordinary differential equations (ODEs) coupled with known ODEs using Bayesian filtering techniques to train the model parameters and simultaneously estimate dynamic state variables. As a study case we leverage a well-understood model for blood circulation in the human retina and replace one of its core ODEs with a neural network approximation, representing the case where we have incomplete knowledge of the physiological state dynamics. Results demonstrate that state dynamics corresponding to the missing ODEs can be approximated well using a neural network trained using a recursive Bayesian filtering approach in a fashion coupled with the known state dynamic differential equations. This demonstrates that dynamics and impact of missing state variables can be captured through joint state estimation and model parameter estimation within a recursive Bayesian state estimation (RBSE) framework. Results also indicate that this RBSE approach to training the NN parameters yields better outcomes (measurement/state estimation accuracy) than training the neural network with backpropagation through time in the same setting.
Data Mining for Targeted Inspections Against Undeclared Work by Applying the CRISP-DM Methodology

Undeclared work is a complex and explicitly hidden socio-economic problem that can take various forms and its consequences are substantial to states, employees and businesses. Thus, inspection authorities need to use their resources more effectively and cleverly to tackle this unlawful phenomenon successfully. This study presents the use of an advanced machine learning method – Associative Classification – through a data mining application following the CRISP-DM methodology, for more effective selection methods of on-site labour inspections against undeclared work. The study proves that the produced classifier can, on the one hand highly contribute in scheduling targeted inspections of increased efficiency and, on the other hand offer actionable and comprehensible knowledge to the labour inspectors regarding the employers’ illegal practices related to undeclared work and other labour law infringements.
A unified framework for interpreting a range of motivation-performance phenomena

Effects of motivation on cognition and performance have been found empirically in different fields. However, the relationship between motivation and performance seems complex and multi-faceted. While seemingly inconsistent or incompatible studies and theories of different disciplinary origins exist in this regard, we show that many of them can potentially be synthesized. Simulations within a unifying computational cognitive architecture account for empirical phenomena from different studies, which demonstrate that a mechanistic (computational) account can potentially unify the interpretations of these studies, largely based on utility calculation from intrinsic motives, and allow for further, more detailed explorations.