Reviewerly/exHarmony huggingface dataset:
A single shot multibox detector based on welding operation method for biometrics recognition in smart cities

As enhance of safety requirement in the smart cities, biometrics recognition, as an approach for society safety, has been greatly researched and developed. The identification of working status of welders will help judge whether they are wearing personal protective equipment correctly. We proposed an improved algorithm based on SSD (Single Shot Multibox Detector) that can identify three mainstream manual welding methods including SMAW (shielded metal arc welding), GMAW (gas metal arc welding) and TIG (tungsten inert gas), which has never been researched before and can promote the intelligentization of welding monitoring to construct smart cities. The improvement includes two parts. Firstly, the backbone of SSD is replaced with MobileNetV3. Then, a feature fusion module is added to enhance the information of low-level feature maps to improve detection accuracy. The experimental results of our welding behavior detector show that, the mAP is 87.45%, detection speed is 25FPS, and parameter memory is 87.5 MB, which has a relatively excellent performance considering speed, accuracy and memory.
A lightweight iterative error reconstruction network for infrared image super-resolution in smart grid

Thermal infrared (IR) images are widely used in smart grids for numerous applications. These applications prefer high-resolution (HR) IR images since HR IR images benefit the performance. However, HR IR imaging devices are extremely expensive. To save the cost of upgrading imaging devices, an iterative error reconstruction network (IERN) is proposed to improve the resolution of IR images. We first achieve efficient dense connections based on linearly compressive skip links. Slightly sacrificing the performance, the efficient dense connections can markedly reduce the parameters and computations of the vanilla dense connections. Then, an iterative error reconstruction mechanism is proposed to boost the performance, which enables IERN to restore many more textures and edges. Specifically, an initial SR image, high-level features, and up-sampled features are obtained firstly. Secondly, a SR error image is acquired by reconstructing the errors between the initial high-level features and the back-projected features from the up-sampled features. Thirdly, a new SR image is obtained by adding the SR error image to the initial SR image. Iterating the above process, the final SR image is achieved when the number of iterations reaches to the iteration threshold. Experimental results reveal the superiority of the proposed method over state-of-the-art methods.
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning

Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Mario Bros., DOOM, Zelda, and Kid Icarus), it is an open question how well these approaches can capture large-scale visual patterns such as symmetry. In this paper, we propose match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns. We demonstrate that popular algorithms such as Generative Adversarial Networks struggle in this domain and propose adaptations to improve their performance. In particular, we augment the neighbourhood of a Markov Random Fields approach to take into account not only local but also symmetric positional information. We conduct several empirical tests, including a user study that show the improvements achieved by the proposed modifications and obtain promising results.
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks

Effective semantic segmentation of lane marking is crucial for construction of high-precision lane level maps. In recent years, a number of different methods for semantic segmentation of images have been proposed. These methods concentrate mainly on analysis of camera images, due to limitations with the sensor itself, and thus far, the accurate three-dimensional spatial position of the lane marking could not be obtained, which hinders lane level map construction.This article proposes a lane marking semantic segmentation method based on LIDAR and camera image fusion using a deep neural network. In the approach, the object of the semantic segmentation is a bird’s-eye view converted from a LIDAR points cloud instead of an image captured by a camera. First, the DeepLabV3+ network image segmentation method is used to segment the image captured by the camera, and the segmentation result is then merged with the point clouds collected by the LIDAR as the input of the proposed network. A long short-term memory (LSTM) structure is added to the neural network to assist the network in semantic segmentation of lane markings by enabling use of time series information. Experiments on datasets containing more than 14,000 images, which were manually labeled and expanded, showed that the proposed method provides accurate semantic segmentation of the bird’s-eye view LIDAR points cloud. Consequently, automation of high-precision map construction can be significantly improved. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/rolandying/FusionLane</uri> .
Sequential Reinforced 360-Degree Video Adaptive Streaming With Cross-User Attentive Network

In the tile-based 360-degree video streaming, predicting user's future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user's quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin.
Efficient Security Algorithm for Power-Constrained IoT Devices

Internet-of-Things (IoT) devices characterized by low power and low processing capabilities do not exactly fit into the provision of existing security techniques due to their constrained nature. Classical security algorithms that are built on complex cryptographic functions often require a level of processing that low-power IoT devices are incapable to effectively achieve due to limited power and processing resources. Consequently, the option for constrained IoT devices lies in either developing new security schemes or modifying existing ones to be more suitable for constrained IoT devices. In this work, an efficient security algorithm for constrained IoT devices, based on the advanced encryption standard, is proposed. We present a cryptanalytic overview of the consequence of complexity reduction together with a supporting mathematical justification, and provisioned a secure element (ATECC608A) as a tradeoff. The ATECC608A doubles for authentication and guarding against implementation attacks on the associated IoT device (ARM Cortex M4 micro-processor) in line with our analysis. The software implementation of the efficient algorithm for constrained IoT devices shows up to 35% reduction in the time it takes to complete the encryption of a single block (16 B) of plain text, in comparison to the currently used standard AES-128 algorithm, and in comparison to current results in literature at 26.6%.
On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment

In this paper, we study the inference of gender, major/minor (computer science, non-computer science), typing style, age, and height from the typing patterns collected from 117 individuals in a multi-device environment. The inference of the first three identifiers was considered as classification tasks, while the rest as regression tasks. For classification tasks, we benchmark the performance of six classical machine learning (ML) and four deep learning (DL) classifiers. On the other hand, for regression tasks, we evaluated three ML and four DL-based regressors. We also present a novel method for the construction of feature space from keystrokes, which can be consumed directly by DL models. The overall experiment consisted of two text-entry (free and fixed) and four devices (Desktop, Tablet, Phone, and Combined) configurations. The best arrangements achieved accuracies of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor, respectively, and mean absolute errors of 1.77 years and 2.65 inches for age and height, respectively. The results are promising considering the variety of application scenarios that we have listed in this work.
Framing Effects on Online Security Behavior

We conducted an incentivized lab experiment examining the effect of gain vs. loss-framed warning messages on online security behavior. We measured the probability of suffering a cyberattack during the experiment as the result of five specific security behaviors: choosing a safe connection, providing minimum information during the sign-up process, choosing a strong password, choosing a trusted vendor, and logging-out. A loss-framed message led to more secure behavior during the experiment. The experiment also measured the effect of trusting beliefs and cybersecurity knowledge. Trusting beliefs had a negative effect on security behavior, while cybersecurity knowledge had a positive effect.
Ghost Sweeper: Using a Heavy Passive Haptic Controller to Enhance a Room-Scale VR Exergame

Virtual Reality (VR) exergaming is a beneficial and promising approach resulting from the combination of physical activity with the high motivation and enjoyment immersive VR offers. However, exergames can often fail to reach suggested levels of exertion. We, therefore, integrated a heavy passive haptic controller into a VR exergame. We explore the impact of the passive haptic controller and different kinds of kinesthetic feedback resulting from different interactions on the player experience. Our results of an extensive study (N=82) suggest, that using a heavy controller can significantly increase the exertion without reducing the motivation in contrast to using an HTC Vive controller. We discuss this finding in the context of haptic feedback and the experience of presence and the resulting implications for further research and VR exergames.
Rotation, Translation, and Cropping for Zero-Shot Generalization

Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games. However, the agent is usually trained on a fixed environment, e.g. a fixed number of levels. A growing mass of evidence suggests that these trained models fail to generalize to even slight variations of the environments they were trained on. This paper advances the hypothesis that the lack of generalization is partly due to the input representation, and explores how rotation, cropping and translation could increase generality. We show that a cropped, translated and rotated observation can get better generalization on unseen levels of two-dimensional arcade games from the GVGAI framework. The generality of the agents is evaluated on both human-designed and procedurally generated levels.
An Energy-Efficient Deep Convolutional Neural Network Accelerator Featuring Conditional Computing and Low External Memory Access

With its algorithmic success in many machine learning tasks and applications, deep convolutional neural networks (DCNNs) have been implemented with custom hardware in a number of prior works. However, such works have not exploited conditional/approximate computing to the utmost toward eliminating redundant computations of CNNs. This article presents a DCNN accelerator featuring a novel conditional computing scheme that synergistically combines precision cascading (PC) with zero skipping (ZS). To reduce many redundant convolutions that are followed by max-pooling operations, we propose precision cascading, where the input features are divided into a number of low-precision groups and approximate convolutions with only the most significant bits (MSBs) are performed first. Based on this approximate computation, the full-precision convolution is performed only on the maximum pooling output that is found. This way, the total number of bit-wise convolutions can be reduced by ~2× with <; 0.8% degradation in ImageNet accuracy. PC provides the added benefit of increased sparsity per low-precision group, which we exploit with ZS to eliminate the clock cycles and external memory accesses. The proposed conditional computing scheme has been implemented with custom architecture in a 40-nm prototype chip, which achieves a peak energy efficiency of 24.97 TOPS/W at 0.6-V supply and a low external memory access of 0.0018 access/MAC with VGG-16 CNN for ImageNet classification and a peak energy efficiency of 28.51 TOPS/W at 0.9-V supply with FlowNet for Flying Chair data set.
Partial Relationship Aware Influence Diffusion via a Multi-channel Encoding Scheme for Social Recommendation

Social recommendation tasks exploit social connections to enhance recommendation performance. To fully utilize each user's first-order and high-order neighborhood preferences, recent approaches incorporate influence diffusion process for better user preference modeling. Despite the superior performance of these models, they either neglect the latent individual interests hidden in the user-item interactions or rely on computationally expensive graph attention models to uncover the item-induced sub-relations, which essentially determine the influence propagation passages. Considering the sparse substructures are derived from original social network, we name them as partial relationships between users. We argue such relationships can be directly modeled such that both personal interests and shared interests can propagate along a few channels (or dimensions) of latent users' embeddings. To this end, we propose a partial relationship aware influence diffusion structure via a computationally efficient multi-channel encoding scheme. Specifically, the encoding scheme first simplifies graph attention operation based on a channel-wise sparsity assumption, and then adds an InfluenceNorm function to maintain such sparsity. Moreover, ChannelNorm is designed to alleviate the oversmoothing problem in graph neural network models. Extensive experiments on two benchmark datasets show that our method is comparable to state-of-the-art graph attention-based social recommendation models while capturing user interests according to partial relationships more efficiently.
Real-Time Vehicle Classification Using CNN

Convolutional Neural Network (CNN) is a model of artificial neural networks that has grown to be most well known in computer vision assignment. In this paper work, we presented convolutional neural network for classifying four types of common vehicle in our country. Vehicle classification plays a vital role of various application such as surveillance security system, traffic control system. We addressed these issues and fixed an aim to find a solution to reduce road accident due to traffic related cases. The greatest challenge of computer vision is to achieve effective results to implement a system due to variation in shapes and colors of data. To classify the vehicle we used two methods feature extraction and classification. These two methods can straightforwardly performed by convolutional neural network. The method shows quite good performance on real-time standard dataset. Our mentioned method able to reach 97% accuracy in case of vehicle classification.
Federated Learning for Cellular Networks: Joint User Association and Resource Allocation

Recent years have shown a remarkable interest in federated learning from researchers to make several Internet of Things applications smart. Although, federated learning offers users' privacy preservation, it has communication resources optimization challenge. In this paper, we consider federated learning for cellular networks. We formulate an optimization problem to jointly minimizes latency and effect of loss in federated learning model accuracy due to channel uncertainties. We decompose the main optimization problem into two sub-problems: resource allocation and device association sub-problems, due to the NP-hard nature of the main optimization problem. To solve these sub-problems, we propose an iterative approach which further uses efficient heuristic algorithms for resource blocks allocation and device association. Finally, we provide numerical results for the validation of our proposed scheme.
Advanced implementation of Montgomery Modular Multiplier

This paper presents an advanced hardware implementation of Modular Multiplication based on Montgomery Modular Multiplier (MMM) architecture. The proposed algorithm is a modification of radix-2 MMM algorithm. The proposed design is implemented on Xilinx Virtex-6 FPGA. The precomputation addition of the modulus and multiplicand, besides removing the final subtraction process, have enhanced the maximum frequency of the design on FPGA. The new modifications have improved the performance and reduced the area of the MMM hardware module. The proposed architecture occupies 908 LUT and 1318 LUT for 256-bit and 512-bit modular multiplier, respectively, for any modulus. Comparing with other related designs our design occupies the smallest area, and has a better efficiency. The proposed design has enhanced the efficiency in the range between 1.41 and 8.7 times the efficiency of other compared designs. The proposed design is intended for lightweight hardware applications of cryptographic modules that is utilized in the Internet of Things (IoT) and the System on Chip (SoC) devices.
An FPGA-Based Low-Latency Accelerator for Randomly Wired Neural Networks

Convolutional neural networks (CNNs) are widely used for image tasks in both embedded systems and data centers. Particularly, when deploying CNNs in a data center, achieving high accuracy and low latency are important for various tasks such as the image processing of streaming videos. However, conventional CNN accelerators often use architectures with a deep pipeline, resulting in not only high throughput but also high latency. We propose an FPGA-based inference accelerator for randomly wired neural networks (RWNNs), whose layer structures are based on random graph models. Because RWNN can be processed in parallel, we can reduce the latency by concurrently using multiple computational units. We use the HBM2 to store feature maps, as multiple computing units need to simultaneously access different feature maps. In addition, the HBM channels and computational units are connected using a crossbar switch to efficiently transfer the feature maps. We allocate each layer to computational units using a simple heuristic algorithm. In addition, we allocate each layer to the HBM channels by coloring a conflict graph built based on the allocated schedule. This makes it possible for the computational units to access HBM channels in parallel. We implemented our architecture on an Alveo U50 FPGA and compared it with a FPGA-based inference accelerator that targets ResNet-50. In the ImageNet image classification task, we could process an image in 16.6 ms, which is 43% lower than that for a conventional accelerator. In addition, our accelerator could reduce the latency by 5.53 times compared with a CPU and 1.98 times compared with a GPU.
Mining High Quality Patterns Using Multi-Objective Evolutionary Algorithm

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.
Distributed Differential Evolution for Anonymity-Driven Vertical Fragmentation in Outsourced Data Storage

Vertical fragmentation is a promising technique for outsourced data storage. It can protect data privacy while conserving original data without any transformation. Previous vertical fragmentation approaches need to predefine sensitive associations in data as the optimization objective, therefore unavailable for the data lacking related prior knowledge. Inspired by the anonymity measurement in anonymity approaches such as k-anonymity, an anonymity-driven vertical fragmentation problem is defined in this paper. To tackle this problem, a set-based distributed differential evolution (S-DDE) algorithm is proposed. An island model containing four sub-populations is adopted to improve population diversity and search efficiency. Two set-based update operators, i.e., set-based mutation operator and set-based crossover operator, are designed to transfer the calculation of discrete values to corresponding sets in vertical fragmentation. Extensive experiments are carried out, and the performance of S-DDE on anonymity-driven vertical fragmentation is verified. The computation efficiency of S-DDE is investigated, and the effectiveness of the generated vertical fragmentation solution by S-DDE is confirmed.