Reviewerly/exHarmony huggingface dataset:
Interpretable Synthetic Reduced Nearest Neighbor: An Expectation Maximization Approach

Synthetic Reduced Nearest Neighbor (SRNN) is a Nearest Neighbor model which is constrained to have K synthetic samples (prototypes/centroids). There has been little attempt toward direct optimization and interpretability of SRNN with proper guarantees like convergence. To tackle these issues, this paper, inspired by K-means algorithm, provides a novel optimization of Synthetic Reduced Nearest Neighbor based on Expectation Maximization (EM-SRNN) that always converges while also monotonically decreases the objective function. The optimization consists of iterating over the centroids of the model and assignment of training samples to centroids. The EM-SRNN is interpretable since the centroids represent sub-clusters of the classes. Such type of interpretability is suitable for various studies such as image processing and epidemiological studies. In this paper, analytical aspects of problem are explored and linear complexity of optimization over the trainset is shown. Finally, EM-SRNN is shown to have superior or similar performance when compared with several other interpretable and similar state-of-the-art models such trees and kernel SVMs.
Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes

Nowadays, mobile robots are deployed in many indoor environments such as offices or hospitals. These environments are subject to changes in the traversability that often happen following patterns. In this paper, we investigate the problem of navigating in such environments over extended periods of time by capturing and exploiting these patterns to make informed decisions for navigation. Our approach uses a probabilistic graphical model to incrementally estimate a model of the traversability changes from the robot's observations and to make predictions at currently unobserved locations. In the belief space defined by the predictions, we plan paths that trade off the risk to encounter obstacles and the information gain of visiting unknown locations. We implemented our approach and tested it in different indoor environments. The experiments suggest that, in the long run, our approach leads robots to navigate along shorter paths compared to following a greedy shortest path policy.
A Systolic Dataflow Based Accelerator for CNNs

Modern Artificial Intelligence (AI) systems deploy Convolution Neural Networks (CNN) as they offer very high accuracy. Computational complexity of CNNs necessitates hardware acceleration, especially in mobile phones and other hand held devices due to stringent power and area budget. In this paper, we propose a systolic dataflow based accelerator architecture with the aim of improving energy efficiency as well as scalability. We demonstrate that a prototype of our proposed accelerator comprising 4096 Multiply-Accumulate (MAC) units is capable of achieving 8.89-9.54 TOPs/W power efficiency when executing a number of state of the art CNN models. We also observe that the throughput of the accelerator scales almost linearly with power dissipated. This is attributed to low power consumption due to minimal non-compute overhead. Our proposed accelerator outperforms state of the art accelerators power efficiency by factors of 2.6 to 3.8× while executing various layers of Inception V3 model.
Clearing the Shadows

Out-of-order processors heavily rely on speculation to achieve high performance, allowing instructions to bypass other slower instructions in order to fully utilize the processor's resources. Speculatively executed instructions do not affect the correctness of the application, as they never change the architectural state, but they do affect the micro-architectural behavior of the system. Until recently, these changes were considered to be safe but with the discovery of new security attacks that misuse speculative execution to leak secrete information through observable micro-architectural changes (so called side-channels), this is no longer the case. To solve this issue, a wave of software and hardware mitigations have been proposed, the majority of which delay and/or hide speculative execution until it is deemed to be safe, trading performance for security. These newly enforced restrictions change how speculation is applied and where the performance bottlenecks appear, forcing us to rethink how we design and optimize both the hardware and the software.
Self-Adaptive Priority Correction for Prioritized Experience Replay

Deep Reinforcement Learning (DRL) is a promising approach for general artificial intelligence. However, most DRL methods suffer from the problem of data inefficiency. To alleviate this problem, DeepMind proposed Prioritized Experience Replay (PER). Though PER improves data utilization, the priorities of most samples in its Experience Memory (EM) are out of date, as only the priorities of a small part of the data are updated while the Q network parameters are updated. Consequently, the difference between storage and real priority distributions gradually increases, which will introduce bias into the gradients of Deep Q-Learning (DQL) and make the DQL update toward a non-ideal direction. In this work, we propose a novel self-adaptive priority correction algorithm named Importance-PER (Imp-PER) to fix the update deviation. Specifically, we predict the sum of real Temporal-Difference error (TD-error) of all data in EM. Data are corrected by an importance weight, which is estimated by the predicted sum and the real TD-error calculated by the latest agent. To control the unbounded importance weight, we use truncated importance sampling with a self-adaptive truncation threshold. The conducted experiments on various games of Atari 2600 with Double Deep Q-Network and MuJoCo with Deep Deterministic Policy Gradient demonstrate that Imp-PER improves the data utilization and final policy quality on discrete states and continuous states tasks without increasing the computational cost.
A Novel Loss Calibration Strategy for Object Detection Networks Training on Sparsely Annotated Pathological Datasets

Recently, object detection frameworks based on Convolutional Neural Networks (CNNs) have become powerful methods for various tasks of medical image analysis; however, they often struggle with most pathological datasets, which are impossible to annotate all the cells. Obviously, sparse annotations may lead to a seriously miscalculated loss in training, which limits the performance of networks. To address this limitation, we investigate the internal training process of object detection networks. Our core observation is that there is a significant density difference between the regression boxes of the positive instances and negative instances. Our novel Boxes Density Energy (BDE) focuses on utilizing the densities of regression boxes to conduct loss-calibration, which is dedicated to reducing the miscalculated loss, meanwhile to penalizing mispredictions with a relatively more significant loss. Thus BDE can guide networks to be trained along the right direction. Extensive experiments have demonstrated that, BDE on the sparsely annotated pathological dataset can significantly boost the performance of networks, and even with 1.0–1.5% higher recall than networks trained on the fully annotated dataset.
Pay More Attention to Discontinuity for Medical Image Segmentation

Medical image segmentation is one of the most important tasks for computer aided diagnosis in medical image analysis. Thanks to deep learning, great progress has been made recently. Yet, most existing segmentation methods still struggle at discontinuity positions (including region boundary and discontinuity within regions), especially when generalized to unseen datasets. In particular, discontinuity within regions and being close to the real region contours may cause wrong boundary delineation. In this paper, different from existing methods that focus only on alleviating the discontinuity issue on region boundary, we propose to pay more attention to all discontinuity including the discontinuity within regions. Specifically, we leverage a simple edge detector to locate all the discontinuity and apply additional supervision on these areas. Extensive experiments on cardiac, prostate, and liver segmentation tasks demonstrate that such a simple approach effectively mitigates the inaccurate segmentation due to discontinuity and achieves noticeable improvements over some state-of-the-art methods.
AsynGraph

Recently, iterative graph algorithms are proposed to be handled by GPU-accelerated systems. However, in iterative graph processing, the parallelism of GPU is still underutilized by existing GPU-based solutions. In fact, because of the power-law property of the natural graphs, the paths between a small set of important vertices (e.g., high-degree vertices) play a more important role in iterative graph processing’s convergence speed. Based on this fact, for faster iterative graph processing on GPUs, this article develops a novel system, called AsynGraph , to maximize its data parallelism. It first proposes an efficient structure-aware asynchronous processing way . It enables the state propagations of most vertices to be effectively conducted on the GPUs in a concurrent way to get a higher GPU utilization ratio through efficiently handling the paths between the important vertices. Specifically, a graph sketch (consisting of the paths between the important vertices) is extracted from the original graph to serve as a fast bridge for most state propagations. Through efficiently processing this sketch more times within each round of graph processing, higher parallelism of GPU can be utilized to accelerate most state propagations. In addition, a forward-backward intra-path processing way is also adopted to asynchronously handle the vertices on each path, aiming to further boost propagations along paths and also ensure smaller data access cost. In comparison with existing GPU-based systems, i.e., Gunrock, Groute, Tigr, and DiGraph, AsynGraph can speed up iterative graph processing by 3.06–11.52, 2.47–5.40, 2.23–9.65, and 1.41–4.05 times, respectively.
Reactive Control and Metric-Topological Planning for Exploration

Autonomous navigation in unknown environments with the intent of exploring all traversable areas is a significant challenge for robotic platforms. In this paper, a simple yet reliable method for exploring unknown environments is presented based on bio-inspired reactive control and metric-topological planning. The reactive control algorithm is modeled after the spatial decomposition of wide and small-field patterns of optic flow in the insect visuomotor system. Centering behaviour and small obstacle detection and avoidance are achieved through wide-field integration and Fourier residual analysis of instantaneous measured nearness respectively. A topological graph is estimated using image processing techniques on a continuous occupancy grid. Node paths are rapidly generated to navigate to the nearest unexplored edge in the graph. It is shown through rigorous field-testing that the proposed control and planning method is robust, reliable, and computationally efficient.
Seraph

As blockchain becomes increasingly popular across various industries in recent years, many companies started designing and developing their own smart contract platforms to enable better services on blockchain. While smart contracts are notoriously known to be vulnerable to external attacks, such platform diversity further amplified the security challenge. To mitigate this problem, we designed the very first cross-platform security analyzer called Seraph for smart contracts. Specifically, Seraph enables automated security analysis for different platforms built on two mainstream virtual machine architectures, i.e., EVM and WASM. To this end, Seraph introduces a set of general connector API to abstract interactions between the virtual machine and blockchain, e.g., load and update storage data on blockchain. Moreover, we proposed the symbolic semantic graph to model critical dependencies and decoupled security analysis from contract code as well. Our preliminary evaluation on four existing smart contract platforms demonstrated the potential of Seraph in finding security threats both flexibly and accurately. A video of Seraph is available at https://youtu.be/wxixZkVqUsc.
Effective Polarization-Based Image Dehazing With Regularization Constraint

Image taken in turbid media generally exists poor visibility and low contrast, which results from attenuation of the propagated light. In this letter, an effective polarization-based image dehazing method is proposed, which relies on the relationship between the angle of polarization (AoP) from the Stokes vector and the scattered light. To avoid the influence of noise, AoP is optimized based on regularization constraints. The regularization function is made using an assumption that adjacent pixels with similar colors have similar values of AoP. Moreover, according to the revised AoP information, all the key parameters can be effectively and automatically estimated without considering the no-object region (or the sky region) exists or not, which relies on a frequency prior strategy. Extensive experiments on real-world images demonstrate that the proposed method is more effective than several previous image restoration or enhancement works.
Ingredient/Recipe Algorithm using Web Mining and Web Scraping for Smart Chef

Due to busy lifestyle and irregular food habits, many of us have to eat diet food according to ingredients parameters. It is difficult to ask chef to make variety of recipes as per the diet plan. Many recipes exist on number of recipe websites, tempting the chef to prepare in their kitchen without knowing the diet ingredient proportion and side effects. In addition, listing all recipes containing a specific ingredient is still not an easy job. This paper proposes an algorithm for extracting all the recipes' details using web scraping and searching recipes containing intended ingredient using python and MongoDB. Web scraping retrieves the contents of a web page using python scrappy library and form database of them in the MongoDB format. This database is used for further research on smart chef application for healthy diet dishes in various varieties. A web and mobile app is developed for smart chef to maintain healthy life with variety of dishes.
Hrnet: Hamiltonian Rescaling Network for Image Downscaling

Image downscaling has become a classical problem in image processing and has recently connected to image super-resolution (SR), which restores high-quality images from low-resolution ones generated by predetermined downscaling kernels (e.g., bicubic). However, most existing image downscaling methods are deterministic and lose information during the downscaling process, while rarely designing specific downscaling methods for image SR. In this paper, we propose a novel learning-based image downscaling method, Hamiltonian Rescaling Network (HRNet). The design of HRNet is based on the discretization of Hamiltonian System, a pair of iterative updating equations, which formulate a mechanism of iterative correction of the error caused by information missing during image or feature downscaling. Extensive experiments demonstrate the effectiveness of our proposed method in terms of both quantitative and qualitative results.
Robust Adversarial Learning For Semi-Supervised Semantic Segmentation

The semi-supervised semantic segmentation adversarial learning well reduces the use of a large number of manually labeled labels. However, the convolution operator of the generator in the generative adversarial network (GAN) has a local receptive field, so it can only deal with long-range dependencies after passing through multiple convolutional layers. In order to solve this problem, we added two layers of self-attention modules to the GAN generator, and modeled the semantic dependency relationship in the spatial dimension. The self-attention module selectively aggregates the features at each location by weighting and summing the features at all locations. Some recent studies have shown that the adjustment of the discriminator affects the performance of GAN. In order to solve the problem of GAN training instability, we applied spectral normalization to the GAN discriminator and found that this improved the stability of the training. Our method has better performance than existing full/semi-supervised semantic image segmentation techniques.
Mobile Match on Card Active Authentication Using Touchscreen Biometric

With the wide use of personal consumer electronics devices such as smartphones, people store sensitive and confidential information more on their devices. Active authentication (AA) systems continuously authenticate users to reduce possible attacks after a successful login on the device. In this article, we propose match-on-card (MOC) approach for a secure active authentication scheme using touchscreen for smartphones to enhance the security and privacy and decrease the performance overhead on the consumer device. We train a Deep Neural Network (DNN) model, and store the model on the smart card available on the device for user authentication. To implement the user verification on smart cards, we quantize inputs to the model and the model's parameters. A speed-up technique is added to the verification phase to improve the execution time. Evaluation results show that with a well configured DNN model, our on-card authentication reaches an Equal Error Rate (EER) of 2.6% for 15 strokes and verification time of 0.65 second for each stroke. Considering the average user's stroke frequency of 1 stroke/s, our proposed scheme shows the potential for mobile MOC active authentication using touchscreen gestures on consumer devices.
SR-ITM-GAN: Learning 4K UHD HDR With a Generative Adversarial Network

Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.
Image super-resolution based on dense convolutional auto-encoder blocks

Deep convolutional neural networks (DCNNs) have recently boosted the performance of image super-resolution (SR) by learning deep non-linear mappings from low-resolution images to their high-resolution counterparts. In general, these methods learn the mapping relationship in image space of a single scale. In this paper, we consider that features across different scales can provide various types of information for SR. Thus, we propose a novel network that extracts features of different spatial resolutions for image super-resolution. We successfully build a model to learn non-linear mappings across feature spaces of various spatial resolutions. Specifically, we propose a dense convolutional auto-encoder block (DCAE), which includes several auto-encoder (AE) units and a squeeze unit, as the basic component of our model. The AE units exploit features of different resolutions through paired encoding and decoding layers. Further, we employ skip connections to combine features of the same spatial scale in one AE unit and dense connections across successive AE units in one DCAE block to establish a temporal feature reuse mechanism. The squeeze units combine features in a DCAE block and the previous DCAE block to achieve long-term temporal feature reuse. Furthermore, we extend our work by performing multi-scale supervised training to build a single framework for SR of all scale factors. Comprehensive experiments show that the proposed method outperforms state-of-the-art methods.
The emotive politics of digital mood tracking

Through a values in design (VID) analysis, this article assesses two mood-tracking apps (Moodscope and MoodPanda) to argue the particular interface design choices of these applications serve to influence their users’ sense of sociality and self-fashioning. The design features of these artifacts signal a broader shift in the sociotechnical definitions and discourses of the feeling of an individual, enabling an emergent emotive politics at work across contemporary digital media technologies.
Tight Compression: Compressing CNN Model Tightly Through Unstructured Pruning and Simulated Annealing Based Permutation

The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. The coarse-grained structured pruning, on the other hand, tends to have higher accuracy loss than unstructured pruning when the pruned models are of the same size. In this work, we propose a compression method based on the unstructured pruning and a novel weight permutation scheme. Through permutation, the sparse weight matrix is further compressed to a small and dense format to make full use of the hardware resources. Compared to the state-of-the-art works, the matrix compression rate is effectively improved from 5.88x to 10.28x. As a result, the throughput and energy efficiency are improved by 2.12 and 1.57 times, respectively.
Anisotropic Weighted KS-NLM Filter for Noise Reduction in MRI

The topic of denoising magnetic resonance (MR) images is considered in this paper. More in detail, an enhanced Non-Local Means (NLM) filter using the Kolmogorov-Smirnov (KS) distance is proposed. The KS-NLM approach estimates the similarity between image patches by computing the KS distance. To overcome that NLM filters assign the same role to all pixels in patches, that is, not privileging the central one, we propose a new filter, namely the Anisotropic Weighted KS-NLM (Aw KS-NLM), which better deals with central pixels within the patches by, on one hand, including a suitable weighted strategy and, on the other, by performing a local anisotropy analysis. The Aw KS-NLM has been compared to other existing non-local Means (NLM) methodologies in both MRI simulated and real datasets. The results provide excellent noise reduction and image-detail preservation.
LiS: Lightweight Signature Schemes for Continuous Message Authentication in Cyber-Physical Systems

Cyber-Physical Systems (CPS) provide the foundation of our critical infrastructures, which form the basis of emerging and future smart services and improve our quality of life in many areas. In such CPS, sensor data is transmitted over the network to the controller, which will make real-time control decisions according to the received sensor data. Due to the existence of spoofing attacks (more specifically to CPS, false data injection attacks), one has to protect the authenticity and integrity of the transmitted data. For example, a digital signature can be used to solve this issue. However, the resource-constrained field devices like sensors cannot afford conventional signature computation. Thus, we have to seek for an efficient signature mechanism that can support the fast and continuous message authentication in CPS, while being easy to compute on the devices.