Reviewerly/exHarmony huggingface dataset:
Uncertainty Estimation via Stochastic Batch Normalization

In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximizes the lower bound of its marginal log-likelihood. Then, according to the new probabilistic model, we design an algorithm which acts consistently during train and test. However, inference becomes computationally inefficient. To reduce memory and computational cost, we propose Stochastic Batch Normalization – an efficient approximation of proper inference procedure. This method provides us with a scalable uncertainty estimation technique. We demonstrate the performance of Stochastic Batch Normalization on popular architectures (including deep convolutional architectures: VGG-like and ResNets) for MNIST and CIFAR-10 datasets.
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision

Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry. Their data curation poses the challenges of expensive human labeling, inadequate computing resources and larger experiment turn around times. Training data subset selection and active learning techniques have been proposed as possible solutions to these challenges. A special class of subset selection functions naturally model notions of diversity, coverage and representation and can be used to eliminate redundancy thus lending themselves well for training data subset selection. They can also help improve the efficiency of active learning in further reducing human labeling efforts by selecting a subset of the examples obtained using the conventional uncertainty sampling based techniques. In this work, we empirically demonstrate the effectiveness of two diversity models, namely the Facility-Location and Dispersion models for training-data subset selection and reducing labeling effort. We demonstrate this across the board for a variety of computer vision tasks including Gender Recognition, Face Recognition, Scene Recognition, Object Detection and Object Recognition. Our results show that diversity based subset selection done in the right way can increase the accuracy by upto 5 - 10% over existing baselines, particularly in settings in which less training data is available. This allows the training of complex machine learning models like Convolutional Neural Networks with much less training data and labeling costs while incurring minimal performance loss.
On Cyber Risk Management of Blockchain Networks: A Game Theoretic Approach

Open-access blockchains based on proof-of-work protocols have gained tremendous popularity for their capabilities of providing decentralized tamper-proof ledgers and platforms for data-driven autonomous organization. Nevertheless, the proof-of-work based consensus protocols are vulnerable to cyber-attacks such as double-spending. In this paper, we propose a novel approach of cyber risk management for blockchain-based service. In particular, we adopt the cyber-insurance as an economic tool for neutralizing cyber risks due to attacks in blockchain networks. We consider a blockchain service market, which is composed of the infrastructure provider, the blockchain provider, the cyber-insurer, and the users. The blockchain provider purchases from the infrastructure provider, e.g., a cloud, the computing resources to maintain the blockchain consensus, and then offers blockchain services to the users. The blockchain provider strategizes its investment in the infrastructure and the service price charged to the users, in order to improve the security of the blockchain and thus optimize its profit. Meanwhile, the blockchain provider also purchases a cyber-insurance from the cyber-insurer to protect itself from the potential damage due to the attacks. In return, the cyber-insurer adjusts the insurance premium according to the perceived risk level of the blockchain service. Based on the assumption of rationality for the market entities, we model the interaction among the blockchain provider, the users, and the cyber-insurer as a two-level Stackelberg game. Namely, the blockchain provider and the cyber-insurer lead to set their pricing/investment strategies, and then the users follow to determine their demand of the blockchain service. Specifically, we consider the scenario of double-spending attacks and provide a series of analytical results about the Stackelberg equilibrium in the market game.
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation

Performance on benchmark datasets has drastically improved with advances in deep learning. Still, cross-dataset generalization performance remains relatively low due to the domain shift that can occur between two different datasets. This domain shift is especially exaggerated between synthetic and real datasets. Significant research has been done to reduce this gap, specifically via modeling variation in the spatial layout of a scene, such as occlusions, and scene environmental factors, such as time of day and weather effects. However, few works have addressed modeling the variation in the sensor domain as a means of reducing the synthetic to real domain gap. The camera or sensor used to capture a dataset introduces artifacts into the image data, which are unique to the sensor model, suggesting that sensor effects may also contribute to domain shift. To address this, we propose a learned augmentation network composed of physically-based augmentation functions. Our proposed augmentation pipeline transfers specific effects of the sensor model-chromatic aberration, blur, exposure, noise, and color temperature-from a real dataset to a synthetic dataset. We provide experiments which demonstrate that augmenting synthetic training datasets with the proposed learned augmentation framework reduces the domain gap between synthetic and real domains for object detection in urban driving scenes.
Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos

Action recognition greatly benefits motion understanding in video analysis. Recurrent networks such as long short-term memory (LSTM) networks are a popular choice for motion-aware sequence learning tasks. Recently, a convolutional extension of LSTM was proposed, in which input-to-hidden and hidden-to-hidden transitions are modeled through convolution with a single kernel. This implies an unavoidable trade-off between effectiveness and efficiency. Herein, we propose a new enhancement to convolutional LSTM networks that supports accommodation of multiple convolutional kernels and layers. This resembles a Network-in-LSTM approach, which improves upon the aforementioned concern. In addition, we propose an attention-based mechanism that is specifically designed for our multi-kernel extension. We evaluated our proposed extensions in a supervised classification setting on the UCF-101 and Sports-1M datasets, with the findings showing that our enhancements improve accuracy. We also undertook qualitative analysis to reveal the characteristics of our system and the convolutional LSTM baseline.
A Survey on the Security of Cloud Computing

Within the recent decade, major innovations in technology have emerged, that potentially add more convenience to daily life practices not only on an enterprise level but on an individual level as well. Cloud Computing technology has witnessed significant advances in its implementation and become widely adopted by either private or public sectors. It was obvious recently that a lot of organizations and enterprises are transferring their workloads to the cloud. However, security is a major concern for the cloud computing services which is based on Internet connection that makes it vulnerable to multiple types of attacks. Even though that the security measures implemented over cloud computing are developing every passing year, Security still a challenge. In this paper, we conducted a survey study on cloud computing and addressed different types of attacks and possible threats to this emerging technology, as well as protection methods and existing solutions to such attacks.
WRA: A 2.2-to-6.3 TOPS Highly Unified Dynamically Reconfigurable Accelerator Using a Novel Winograd Decomposition Algorithm for Convolutional Neural Networks

As convolutional neural networks (CNNs) become more and more diverse and complicated, acceleration of CNNs increasingly encounters a bottleneck of balancing performance, energy efficiency, and flexibility in a unified architecture. This paper proposed a Winograd-based highly efficient and dynamically Reconfigurable Accelerator (named WRA) for quickly evolving CNN models. A cost-effective convolution decomposition method (CDW) was proposed, and it extends the application of the fast Winograd algorithm. Based on CDW, a high-throughput and reconfigurable processing element (PE) array was designed to exploit the parallelism of Winograd. Besides, a highly compact memory structure employed four levels of data reuse schemes to achieve maximal data reuse and minimize external bandwidth requirement. Provided with dynamically reconfigurable capability, WRA implements CDW and other convolutions (e.g., standard convolution, depthwise separable convolution, and group convolution) on a unified hardware architecture. The WRA accelerator was implemented on a Xilinx XCVU9P platform running at 330 MHz clock frequency, controlled by a POWER8 processor via a coherent accelerator processor interface (CAPI) interface. At different configurations, WRA can provide 2.2-6.3 TOPS performance for different convolution shapes. The average performance and energy efficiency for VGG16/AlexNet/MobileNetV1/MobileNetV2 are 5288 GOP/s at 151.2 GOPs/W, 3478 GOP/s at 99.4 GOPs/W, 2674 GOP/s at 76.4 GOPs/W, and 2194 GOP/s at 62.7 GOPs/W. It achieves $1.7\times $ -$24\times $ speedup compared with the previous FPGA-based designs.
Perceptions and estimates of error rates in forensic science: A survey of forensic analysts

Every scientific technique features some error, and legal standards for the admissibility of scientific evidence (e.g., Daubert v. Merrill Dow Pharmaceuticals, Inc., 1993; Kumho Tire Co v. Carmichael, 1999) guide trial courts to consider known error rates. However, recent reviews of forensic science conclude that error rates for some common techniques are not well-documented or even established (e.g., NAS, 2009; PCAST, 2016). Furthermore, many forensic analysts have historically denied the presence of error in their field. Therefore, it is important to establish what forensic scientists actually know or believe about errors rates in their disciplines. We surveyed 183 practicing forensic analysts to examine what they think and estimate about error rates in their various disciplines. Results revealed that analysts perceive all types of errors to be rare, with false positive errors even more rare than false negatives. Likewise, analysts typically reported that they prefer to minimize the risk of false positives over false negatives. Most analysts could not specify where error rates for their discipline were documented or published. Their estimates of error in their fields were widely divergent - with some estimates unrealistically low.
Implementing a modal dependent type theory

Modalities are everywhere in programming and mathematics! Despite this, however, there are still significant technical challenges in formulating a core dependent type theory with modalities. We present a dependent type theory MLTT 🔒 supporting the connectives of standard Martin-Löf Type Theory as well as an S4 -style necessity operator. MLTT 🔒 supports a smooth interaction between modal and dependent types and provides a common basis for the use of modalities in programming and in synthetic mathematics. We design and prove the soundness and completeness of a type checking algorithm for MLTT 🔒 , using a novel extension of normalization by evaluation. We have also implemented our algorithm in a prototype proof assistant for MLTT 🔒 , demonstrating the ease of applying our techniques.
Adversarial explanations for understanding image classification decisions and improved neural network robustness

For sensitive problems, such as medical imaging or fraud detection, neural network (NN) adoption has been slow due to concerns about their reliability, leading to a number of algorithms for explaining their decisions. NNs have also been found to be vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. Here we demonstrate both that these attacks can invalidate previous attempts to explain the decisions of NNs, and that with very robust networks, the attacks themselves may be leveraged as explanations with greater fidelity to the model. We also show that the introduction of a novel regularization technique inspired by the Lipschitz constraint, alongside other proposed improvements including a half-Huber activation function, greatly improves the resistance of NNs to adversarial examples. On the ImageNet classification task, we demonstrate a network with an accuracy-robustness area (ARA) of 0.0053, an ARA 2.4 times greater than the previous state-of-the-art value. Improving the mechanisms by which NN decisions are understood is an important direction for both establishing trust in sensitive domains and learning more about the stimuli to which NNs respond. Deep neural networks can be led to misclassify an image when minute changes that are imperceptible to humans are introduced. While for some networks this ability can cast doubt on the reliability of the model, it also offers explainability for networks that use more robust regularization.
An artificial bee colony-based kernel ridge regression for automobile insurance fraud identification

Automobile insurance fraud is gradually spreading in its global scope, and mining automobile insurance fraud is of increasing interest to society. There are few studies on the application of Kernel Ridge Regression (KRR) to insurance fraud. KRR has shown its good generalization performance in many fields. The performance of KRR depends on the parameters. KRR requires fewer parameters. In this paper, an Artificial Bee Colony (ABC) algorithm-based Kernel Ridge Regression, called KRR-ABC, is proposed for automobile insurance fraud detection. The global optimization ability of the ABC algorithm is used to optimize the parameter combination of KRR. It can quickly find the global optimal value without traversing all the grid points and improve the generalization ability of the model. The parallel computing is also used to enhance the computing speed of KRR-ABC algorithm. The performance of the KRR-ABC model is evaluated on eight benchmark data sets and compared with other methods. The experiment results show that the KRR-ABC model has faster run time and better generation performance. The KRR-ABC model is applied to detect automobile insurance fraud and the fraud rules are obtained.
Anger or fear? Effects of discrete emotions on employee’s computer-related deviant behavior

Security research on the employees’ expressive motives in committing computer-related deviant behavior is limited. In this research, drawing on the appraisal tendency framework (ATF), we examine how employees’ emotions (anger vs. fear) and perceived sanctions influence their computer-related deviant behaviors in the workplace. Our empirical results show that employees who experienced anger were more likely to commit computer-related deviant behavior mediated by perceived informal sanctions. In contrast, employees who experienced fear were less likely to commit computer-related deviant behavior mediated by perceived formal and informal sanctions. The results provide important guidance for organizations to improve the effectiveness of deterrent controls.
The Personification of Big Data

Abstract Organizations all over the world, both national and international, gather demographic data so that the progress of nations and peoples can be tracked. This data is often made available to the public in the form of aggregated national level data or individual responses (microdata). Product designers likewise conduct surveys to better understand their customer and create personas. Personas are archetypes of the individuals who will use, maintain, sell or otherwise be affected by the products created by designers. Personas help designers better understand the person the product is designed for. Unfortunately, the process of collecting customer information and creating personas is often a slow and expensive process. In this paper, we introduce a new method of creating personas, leveraging publicly available databanks of both aggregated national level and information on individuals in the population. A computational persona generator is introduced that creates a population of personas that mirrors a real population in terms of size and statistics. Realistic individual personas are filtered from this population for use in product development.
A Deep Reinforcement Learning Method for Mobile Robot Collision Avoidance based on Double DQN

We propose a deep reinforcement learning method based on Double Q-learning Network(DDQN) to enable mobile robots to learn collision avoidance and navigation capabilities autonomously. Information such as target position, obstacle size and position is taken as input, and the direction of movement of the robot is taken as an output. Traditional mobile robots usually requires real-time accurate and fast Simultaneous Localization And Mapping(SLAM) technology for global navigation. We aim at the scenario that after an initial globally feasible path is established, the path could be split into finite segments of sub-goals, and the proposed method focuses on using deep reinforcement learning to control the robots reaching the subgoals in sequence. Experiments show that the proposed method can navigate the mobile robots to desired target position without colliding with any obstacle and other moving robots, and the method is successfully implied on a physical robot platform. In addition, the method is a non-global path planning method, which greatly reduces the computational cost.
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation

We propose a technique for depth estimation with a monocular structured-light camera, i.e., a calibrated stereo set-up with one camera and one laser projector. Instead of formulating the depth estimation via a correspondence search problem, we show that a simple convolutional architecture is sufficient for high-quality disparity estimates in this setting. As accurate ground-truth is hard to obtain, we train our model in a self-supervised fashion with a combination of photometric and geometric losses. Further, we demonstrate that the projected pattern of the structured light sensor can be reliably separated from the ambient information. This can then be used to improve depth boundaries in a weakly supervised fashion by modeling the joint statistics of image and depth edges. The model trained in this fashion compares favorably to the state-of-the-art on challenging synthetic and real-world datasets. In addition, we contribute a novel simulator, which allows to benchmark active depth prediction algorithms in controlled conditions.
An Image Segmentation Encryption Algorithm Based on Hybrid Chaotic System

Image encryption is an effective technology to protect digital image confidentiality.This paper presents an image segmentation encryption algorithm based on a hybrid chaotic system.First, a chaotic sequence is obtained by iterating a Quantum Cellular Neural Network (QCNN), and then it is scrambled by a 4-D hyperchaotic system to generate a key pool.Second, the chaotic pointers generated by 3-D chaotic systems and QCNN with different initial values are used to get the keys for image segmentation, scrambling, and diffusion from the key pool.Then, the plain-image is divided into two blocks by the chaotic segmentation method and scrambled by intra-block and inter-block pixel exchange.In addition, two blocks are statically diffused, and the cipher-image is obtained by dynamic diffusing after combining the image blocks.Especially, the key pool increases the efficiency of the proposed algorithm, and chaotic segmentation reduces the cipher-image pixel correlation.Finally, the simulation results and performance analysis indicate that the proposed algorithm has a well-security, high sensitivity, and faster speed.
VS-GRU: A Variable Sensitive Gated Recurrent Neural Network for Multivariate Time Series with Massive Missing Values

Multivariate time series are often accompanied with missing values, especially in clinical time series, which usually contain more than 80% of missing data, and the missing rates between different variables vary widely. However, few studies address these missing rate differences and extract univariate missing patterns simultaneously before mixing them in the model training procedure. In this paper, we propose a novel recurrent neural network called variable sensitive GRU (VS-GRU), which utilizes the different missing rate of each variable as another input and learns the feature of different variables separately, reducing the harmful impact of variables with high missing rates. Experiments show that VS-GRU outperforms the state-of-the-art method in two real-world clinical datasets (MIMIC-III, PhysioNet).
People Infer Recursive Visual Concepts from Just a Few Examples

Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the limits of this human ability to infer causal "programs"—latent generating processes with nontrivial algorithmic properties—from one, two, or three visual examples. People were asked to extrapolate the programs in several ways, for both classifying and generating new examples. As a theory of these inductive abilities, we present a Bayesian program learning model that searches the space of programs for the best explanation of the observations. Although variable, people's judgments are broadly consistent with the model and inconsistent with several alternatives, including a pretrained deep neural network for object recognition, indicating that people can learn and reason with rich algorithmic abstractions from sparse input data.
A Deep and Structured Metric Learning Method for Robust Person Re-Identification

Person re-identification (re-ID) is to match different images of the same pedestrian. It has attracted increasing research interest in pattern recognition and machine learning. Traditionally, person re-ID is formulated as a metric learning problem with binary classification output. However, higher order relationship, such as triplet closeness among the instances, is ignored by such pair-wise based metric learning methods. Thus, the discriminative information hidden in these data is insufficiently explored. This paper proposes a new structured loss function to push the frontier of the person re-ID performance in realistic scenarios. The new loss function introduces two margin parameters. They operate as bounds to remove positive pairs of very small distances and negative pairs of large distances. A trade-off coefficient is assigned to the loss term of negative pairs to alleviate class-imbalance problem. By using a linear function with the margin-based objectives, the gradients w.r.t. weight matrices are no longer dependent on the iterative loss values in a multiplicative manner. This makes the weights update process robust to large iterative loss values. The new loss function is compatible with many deep learning architectures, thus, it induces new deep network with pair-pruning regularization for metric learning. To evaluate the performance of the proposed model, extensive experiments are conducted on benchmark datasets. The results indicate that the new loss together with the ResNet-50 backbone has excellent feature representation ability for person re-ID.