Reviewerly/exHarmony huggingface dataset:
Dungeon and Platformer Level Blending and Generation using Conditional VAEs

Variational autoencoders (VAEs) have been used in prior works for generating and blending levels from different games. To add controllability to these models, conditional VAEs (CVAEs) were recently shown capable of generating output that can be modified using labels specifying desired content, albeit working with segments of levels and platformers exclusively. We expand these works by using CVAEs for generating whole platformer and dungeon levels, and blending levels across these genres. We show that CVAEs can reliably control door placement in dungeons and progression direction in platformer levels. Thus, by using appropriate labels, our approach can generate whole dungeons and platformer levels of interconnected rooms and segments respectively as well as levels that blend dungeons and platformers. We demonstrate our approach using The Legend of Zelda, Metroid, Mega Man and Lode Runner.
Extended Variational Message Passing for Automated Approximate Bayesian Inference

Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for approximating Bayesian inference in factorized probabilistic models that consist of conjugate exponential family distributions. The automation of Bayesian inference tasks is very important since many data processing problems can be formulated as inference tasks on a generative probabilistic model. However, accurate generative models may also contain deterministic and possibly nonlinear variable mappings and non-conjugate factor pairs that complicate the automatic execution of the VMP algorithm. In this paper, we show that executing VMP in complex models relies on the ability to compute the expectations of the statistics of hidden variables. We extend the applicability of VMP by approximating the required expectation quantities in appropriate cases by importance sampling and Laplace approximation. As a result, the proposed Extended VMP (EVMP) approach supports automated efficient inference for a very wide range of probabilistic model specifications. We implemented EVMP in the Julia language in the probabilistic programming package ForneyLab.jl and show by a number of examples that EVMP renders an almost universal inference engine for factorized probabilistic models.
Outlier detection in networks with missing links

Outliers arise in networks due to different reasons such as fraudulent behaviour of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely observed, with missing links due to individual non-response or machine failures. Therefore, identifying outliers in the presence of missing links is a crucial problem in network analysis. A new algorithm is introduced to detect outliers in a network and simultaneously predict the missing links. The proposed method is statistically sound: under fairly general assumptions, this algorithm exactly detects the outliers, and achieves the best known error for the prediction of missing links with polynomial computational cost. The sub-linear convergence of the algorithm is proven, which confirms its computational efficiency. A simulation study is provided, which demonstrates the good behaviour of the algorithm in terms of outlier detection and prediction of the missing links. The method is also illustrated with an application in epidemiology and with the analysis of a political Twitter network. The algorithm is freely available as an R package on the Comprehensive R Archive Network.
PocketNet: A Smaller Neural Network for Medical Image Analysis

Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings.
SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block

Multiple high-resolution (HR) images can be generated from a single low-resolution (LR) image, as super-resolution (SR) is an underdetermined problem. Recently, the conditional normalizing flow-based model, SRFlow, shows remarkable performance by learning an exact map-ping from HR image manifold to a latent space. The flow-based SR model allows sampling multiple output images from a learned SR space with a given LR image. In this work, we propose SRFlow-DA which has a more suitable architecture for the SR task based on the original SRFlow model. Specifically, our approach enlarges the receptive field by stacking more convolutional layers in the affine couplings, and so our model can get more expressive power. At the same time, we reduce the total number of model pa-rameters for efficiency. Compared to SRFlow, our SRFlow-DA achieves better or comparable PSNR and LPIPS for × 4 and ×8 SR tasks, while having a reduced number of parameters. In addition, our method generates visually clear results without excessive sharpness artifacts.
Tracklet-refined Multi-Camera Tracking based on Balanced Cross-Domain Re-Identification for Vehicles

Mutli-camera vehicle tracking and re-identification (re-ID) have gradually gained attention due to their applications in the intelligent transportation system. However, these problems are fundamentally challenging. Specifically, for vehicle tracking, we observe that the results generated from single camera tracking algorithm usually recognize tracklets with same identity as different vehicles when the tracklets are occluded. Hence, we propose a Tracklet Re-connection technique to refine tracking results with pre-defined zone areas and GPS information. The proposed method can efficiently filter invalid tracklet pairs and reconnect the split tracklets into complete ones, which is important for the afterwards multi-target multi-camera tracking. As for re-ID, we also find that when a large-scale auxiliary dataset is used to assist the learning of main dataset for better model capability and generalization, there is a performance drop caused by data imbalance when the full auxiliary dataset is applied. To tackle this problem, we introduce Balanced Cross-Domain Learning to avoid the overemphasis on larger auxiliary dataset by a newly introduced training data sampler and loss function. The extensive experiments validate the empirical effectiveness of our proposed components.
Exploring Summarization to Enhance Headline Stance Detection

The spread of fake news and misinformation is causing serious problems to society, partly due to the fact that more and more people only read headlines or highlights of news assuming that everything is reliable, instead of carefully analysing whether it can contain distorted or false information. Specifically, the headline of a correctly designed news item must correspond to a summary of the main information of that news item. Unfortunately, this is not always happening, since various interests, such as increasing the number of clicks as well as political interests can be behind of the generation of a headlines that does not meet its intended original purpose. This paper analyses the use of automatic news summaries to determine the stance (i.e., position) of a headline with respect to the body of text associated with it. To this end, we propose a two-stage approach that uses summary techniques as input for both classifiers instead of the full text of the news body, thus reducing the amount of information that must be processed while maintaining the important information. The experimentation has been carried out using the Fake News Challenge FNC-1 dataset, leading to a 94.13% accuracy, surpassing the state of the art. It is especially remarkable that the proposed approach, which uses only the relevant information provided by the automatic summaries instead of the full text, is able to classify the different stance categories with very competitive results, so it can be concluded that the use of the automatic extractive summaries has a positive impact for determining the stance of very short information (i.e., headline, sentence) with respect to its whole content.
Exploring HW/SW Co-Design for Video Analysis on CPU-FPGA Heterogeneous Systems

Deep neural network (DNN)-based video analysis has become one of the most essential and challenging tasks to capture implicit information from video streams. Although DNNs significantly improve the analysis quality, they introduce intensive compute and memory demands and require dedicated hardware for efficient processing. The customized heterogeneous system is one of the promising solutions with general-purpose processors (CPUs) and specialized processors (DNN Accelerators). Among various heterogeneous systems, the combination of CPU and FPGA has been intensively studied for DNN inference with improved latency and energy consumption compared to CPU &#x002B; GPU schemes and with increased flexibility and reduced time-to-market cost compared to CPU &#x002B; ASIC designs. However, deploying DNN-based video analysis on CPU &#x002B; FPGA systems still presents challenges from the tedious RTL programming, the intricate design verification, and the time-consuming design space exploration. To address these challenges, we present a novel framework, called EcoSys, to explore co-design and optimization opportunities on CPU-FPGA heterogeneous systems for accelerating video analysis. Novel technologies include 1) a coherent memory space shared by the host and the customized accelerator to enable efficient task partitioning and online DNN model refinement with reduced data transfer latency; 2) an end-to-end design flow that supports high-level design abstraction and allows rapid development of customized hardware accelerators from Python-based DNN descriptions; 3) a design space exploration (DSE) engine that determines the design space and explores the optimized solutions by considering the targeted heterogeneous system and user-specific constraints; and 4) a complete set of co-optimization solutions, including a layer-based pipeline, a feature map partition scheme, and an efficient memory hierarchical design for the accelerator and multithreading programming for the CPU. In this article, we demonstrate our design framework to accelerate the long-term recurrent convolution network (LRCN), which analyzes the input video and output one semantic caption for each frame. EcoSys can deliver 314.7 and 58.1 frames/s by targeting the LRCN model with AlexNet and VGG-16 backbone, respectively. Compared to the multithreaded CPU and pure FPGA design, EcoSys achieves <inline-formula> <tex-math notation="LaTeX">$20.6\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$5.3\times $ </tex-math></inline-formula> higher throughput performance.
Determining Peripersonal Space Boundaries and Their Plasticity in Relation to Object and Agent Characteristics in an Immersive Virtual Environment

In this paper we examine the extent of functional reaching space, or peripersonal space, in immersive three-dimensional virtual reality. In the real world a person’s peripersonal space boundaries can be altered by factors in the environment and by social context. We completed two studies with visual and tactile stimuli to determine peripersonal space boundaries. These studies investigated whether peripersonal space boundaries in an immersive virtual environment are consistent with those in the real world, and could be altered by object and virtual agent interactions. We found that while peripersonal space boundaries were consistent with those in the real world, they were responsive to object and agent interactions. Moreover, while people’s reactions to the objects and agents varied, the peripersonal space boundaries remained consistent. These findings have potential implications for the design of virtual environments.
Checking Robustness Against EM Side-Channel Attacks Prior to Manufacturing

Electromagnetic attacks, which in fact exploit essentially the magnetic field generated by ICs, are commonly used by adversaries to retrieve secret information manipulated by integrated circuits. Due to the increasing resolution and effectiveness of EM equipment used to perform these attacks, it is becoming increasingly difficult to design secure circuits robust enough to resist these attacks. The contribution of this article is threefold. First, it describes a simulation flow of the magnetic field radiated by ICs. The introduced flow is based on an industrial voltage drop tool: ANSYS RedHawk. Second, it introduces a methodology to localize the root cause of leakages in ICs as well as EM hotspots, i.e., positions above the IC surface, where an adversary can place its probe to capture secrets. The latter contribution is based on the concept of noise to add, which is introduced in this article in order to overcome the absence of noise in simulations (noise which is omnipresent in practice) that limits their interpretability. Finally, the article demonstrates the soundness of the proposed solution by confronting simulation results with measurements.
Speculative Design for Education: Using Participatory Methods to Map Design Challenges and Opportunities in Pakistan

The global education landscape continues to be disrupted by COVID-19. The unique circumstances created by this crisis has led to unexpected opportunities to leverage innovative uses of technologies that can radically innovate education service delivery. We map out the context of Pakistan – a country where challenges pertaining to both access to education and quality learning predate the pandemic. A context largely under explored in design and HCI research. This paper presents insights from conversations with 21 stakeholders across 15 design activities, that were structured around speculative design proposals to collaboratively envision the future of education in Pakistan. The paper offers directions for the design of EdTech through a plural, and culturally rooted speculative discussion, highlighting (1) opportunities and considerations for localising design for education, and (2) reframing the value of education in Pakistan.
The Concept of an Autonomic Avionics Platform and the Resulting Software Engineering Challenges

The self-* properties commonly associated with the concept of autonomic computing are capabilities desirable for avionics software platforms. They decrease the configuration effort and inherently provide new fault tolerance and resource savings possibilities. The rigid certification process and the requirements for a static and predetermined system behavior are, however, in contradiction with the adaptive and flexible nature of autonomic computing systems. We propose a partition-based architecture providing autonomic features for avionics software platforms while being compliant to regulations and accepted technologies, such as ARINC 653. The core is a platform consciousness based on a domain-specific model and a novel MAP-QE-K cycle. Moreover, we suggest a planning intelligence, a virtual qualification authority, and a minimized execution unit. For each component we define the required design assurance level and possible realization techniques. We discuss the overall feasibility and point out central challenges in the fields of runtime verification and models at runtime. These challenges need to be solved up to the realization of autonomic avionics, e.g. a virtual security assessment and a qualifiable domain-specific model database.
Cyber Range for Research-Inspired Learning of “Attack Defense by Pretense” Principle and Practice

There is an increasing trend in cloud adoption of enterprise applications in, for example, manufacturing, healthcare, and finance. Such applications are routinely subject to targeted cyberattacks, which result in significant loss of sensitive data (e.g., due to data exfiltration in advanced persistent threats) or valuable utilities (e.g., due to resource the exfiltration of power in cryptojacking). There is a critical need to train highly skilled cybersecurity professionals, who are capable of defending against such targeted attacks. In this article, we present the design, development, and evaluation of the Mizzou Cyber Range, an online platform to learn basic/advanced cyber defense concepts and perform training exercises to engender the next-generation cybersecurity workforce. Mizzou Cyber Range features flexibility, scalability, portability, and extendability in delivering cyberattack/defense learning modules to students. We detail our “research-inspired learning” and “learn-apply-create” three-phase pedagogy methodologies in the development of four learning modules that include laboratory exercises and self-study activities using realistic cloud-based application testbeds. The learning modules allow students to gain skills in using latest technologies (e.g., elastic capacity provisioning, software-defined everything infrastructure) to implement sophisticated “attack defense by pretense” techniques. Students can also use the learning modules to understand the attacker-defender game in order to create disincentives (i.e., pretense initiation) that make the attacker's tasks more difficult, costly, time consuming, and uncertain. Lastly, we show the benefits of our Mizzou Cyber Range through the evaluation of student learning using auto-grading, rank assessments with peer standing, and monitoring of students' performance via feedback from prelab evaluation surveys and postlab technical assessments.
Genetic programming is naturally suited to evolve bagging ensembles

Learning ensembles by bagging can substantially improve the generalization performance of low-bias, high-variance estimators, including those evolved by Genetic Programming (GP). To be efficient, modern GP algorithms for evolving (bagging) ensembles typically rely on several (often inter-connected) mechanisms and respective hyper-parameters, ultimately compromising ease of use. In this paper, we provide experimental evidence that such complexity might not be warranted. We show that minor changes to fitness evaluation and selection are sufficient to make a simple and otherwise-traditional GP algorithm evolve ensembles efficiently. The key to our proposal is to exploit the way bagging works to compute, for each individual in the population, multiple fitness values (instead of one) at a cost that is only marginally higher than the one of a normal fitness evaluation. Experimental comparisons on classification and regression tasks taken and reproduced from prior studies show that our algorithm fares very well against state-of-the-art ensemble and non-ensemble GP algorithms. We further provide insights into the proposed approach by (i) scaling the ensemble size, (ii) ablating the changes to selection, (iii) observing the evolvability induced by traditional subtree variation.
DUN: Dual-path Temporal Matching Network for Natural Language-based Vehicle Retrieval

Retrieving vehicles matching natural language descriptions from collections of videos is a novel and uniquely challenging task, requiring consideration not only of vehicle types and colors, but also of temporal relations, e.g., "A white crossover keeping straight behind a silver hatch-back." To perform this task, we propose Dual-path Temporal Matching Network (DUN). DUN uses a pre-trained CNN and GloVe to extract visual and text features, respectively, and GRUs to mine temporal relationships in videos and sentences. Furthermore, the proposed network can attain superior performance by including techniques such as re-ranking. With its simple structure, DUN achieved second place on the AI City Challenge 2021 Track 5. The codes are available at https://github.com/okzhili/AICITY2021_Track5_DUN.
Optimizing Reservoir Computers for Signal Classification

Reservoir computers are a type of recurrent neural network for which the network connections are not changed. To train the reservoir computer, a set of output signals from the network are fit to a training signal by a linear fit. As a result, training of a reservoir computer is fast, and reservoir computers may be built from analog hardware, resulting in high speed and low power consumption. To get the best performance from a reservoir computer, the hyperparameters of the reservoir computer must be optimized. In signal classification problems, parameter optimization may be computationally difficult; it is necessary to compare many realizations of the test signals to get good statistics on the classification probability. In this work, it is shown in both a spiking reservoir computer and a reservoir computer using continuous variables that the optimum classification performance occurs for the hyperparameters that maximize the entropy of the reservoir computer. Optimizing for entropy only requires a single realization of each signal to be classified, making the process much faster to compute.
System and Software Processes in Practice: Insights from Chinese Industry

Software development processes play a key role in the software and system development life cycle. Processes are becoming complex and evolve rapidly due to the modern-day continuous software engineering (CSE) concepts, which are mainly based on continuous integration, continuous delivery, infrastructure-as-code, automation and more. The fast growing Chinese software development industry adopts various processes to achieve potential benefits offered in the international market. This study is conducted with the aim to investigate the trends of processes in practice in the Chinese industry. The survey questionnaire data is collected from 34 practitioners working in software development firms across the China and the results highlight that iterative and agile processes are extensively used in industrial setting. Furthermore, agile and traditional approaches are combined to develop the hybrid processes. Most of the participants are satisfied using the current development processes, however, they show interest to continuously improve the existing process models and methods. Finally, we noticed that majority of the software development organizations used the ISO 9001 standard for process assessment and improvement activities. The given results provide preliminary overview of processes deployed in the Chinese industry.
Differentiable Particle Filters through Conditional Normalizing Flow

Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data. However, most existing differentiable particle filters are within the bootstrap particle filtering framework and fail to incorporate the information from latest observations to construct better proposals. In this paper, we utilize conditional normalizing flows to construct proposal distributions for differentiable particle filters, enriching the distribution families that the proposal distributions can represent. In addition, normalizing flows are incorporated in the construction of the dynamic model, resulting in a more expressive dynamic model. We demonstrate the performance of the proposed conditional normalizing flow-based differentiable particle filters in a visual tracking task.