Miracle-dz/UniFAR-FTUs huggingface dataset:
MIO-TCD: A New Benchmark Dataset for Vehicle Classification and Localization

The ability to train on a large dataset of labeled samples is critical to the success of deep learning in many domains. In this paper, we focus on motor vehicle classification and localization from a single video frame and introduce the “Miovision traffic camera dataset” (MIO-TCD) in this context. MIO-TCD is the largest dataset for motorized traffic analysis to date. It includes 11 traffic object classes such as cars, trucks, buses, motorcycles, bicycles, and pedestrians. It contains 786 702 annotated images acquired at different times of the day and different periods of the year by hundreds of traffic surveillance cameras deployed across Canada and the United States. The dataset consists of two parts: a “localization dataset,” containing 137 743 full video frames with bounding boxes around traffic objects and a “classification dataset,” containing 648 959 crops of traffic objects from the 11 classes. We also report the results from the 2017 CVPR MIO-TCD Challenge that leveraged this dataset and compare them with the results of the state-of-the-art deep learning architectures. These results demonstrate the viability of deep learning methods for vehicle localization and classification from a single video frame in real-life traffic scenarios. The top-performing methods achieve both accuracy and Kappa score above 96% on the classification dataset and mean-average precision of 77% on the localization dataset. We also identify the scenarios in which the state-of-the-art methods still fail, and we suggest avenues to address these challenges. Both the dataset and detailed results are publicly available online.
An Investigation Into Android Run-Time Permissions from the End Users' Perspective

To protect the privacy of end users from intended or unintended malicious behavior, the Android operating system provides a permissions-based security model that restricts access to privacy-relevant parts of the platform. Starting with Android 6, the permission system has been revamped, moving to a run-time model. Users are now prompted for con rmation when an app attempts to access a re-stricted part of the platform. We conducted a large-scale empirical study to investigate how end users perceive the new run-time permission system of Android, collecting and inspecting over 4.3 million user reviews about 5,572 apps published in the Google Play Store. Among them, we identi ed, classi ed, and analyzed 3,574 permission-related reviews, employing machine learning and Natural Language Processing techniques. Out of the permission-related reviews, we determined recurring points made by users about the new permission system and classi ed them into a taxonomy. Results of our analysis suggest that, even with the new system, permission-related issues are widespread, with 8% of collected apps having user reviews with negative comments about permissions. We identify a number of points for improvement in the Android run-time permission system, and provide recommendations for future research.
Detection of Automotive CAN Cyber-Attacks by Identifying Packet Timing Anomalies in Time Windows

Cyber-attacks on the automotive controller area network (CAN) have recently been shown to be achievable and potentially disruptive or deadly. Detecting an attack quickly will require the development of intrusion detection systems that can cope with the rapid broadcast of CAN data, the comparatively limited computational power of automotive components, and the proprietary nature of CAN data specifications. This paper presents an analysis of CAN broadcasts and consequent testing of statistical methods to detect timing changes in the CAN traffic indicative of some predicted attacks. The detection is implemented in time-defined windows. The generation of simulated attack data, and the determination of positive detections, are also considered.
Physical Activity in Children's Health and Cognition

1School of Kinesiology, The University of Minnesota, Minneapolis, MN, USA 2School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA 3College of Education, University of South Florida, Tampa, FL, USA 4Department of Physical Education, College of Education, Zhejiang University, Hangzhou, Zhejiang, China 5College of Education and Human Development, Texas A&M University, College Station, TX, USA
Risk Assessment and Security Countermeasures for Vehicular Instrument Clusters

The vehicular instrument cluster has the vital task of informing the driver on vehicle status or potential malfunctions. While this role is merely informative, the implications are far reaching as the driver needs to take decisions based on the reports provided by the instrument cluster. Past attacks on instrument clusters were rather concerned with mundane tasks, e.g., mileage modification, but giving false information to the driver on vehicle speed or triggering/hiding relevant alarms may have serious consequences as it can lead to severe traffic accidents. In this work we discuss risks associated to attacker actions on instrument clusters and envision a potential model based intrusion detection system to detect potential attacks. Rather than advocating a holistic approach, in which security is designed for the entire vehicle network, e.g. CAN or FlexRay, we follow a component-based approach in which particularities of the instrument cluster and redundancy of information are used to detect potential attacks.
Exploring Predictive Replacement Policies for Instruction Cache and Branch Target Buffer

Modern processors support instruction fetch with the instruction cache (I-cache) and branch target buffer (BTB). Due to timing and area constraints, the I-cache and BTB must efficiently make use of their limited capacities. Blocks in the I-cache or entries in the BTB that have low potential for reuse should be replaced by more useful blocks/entries. This work explores predictive replacement policies based on reuse prediction that can be applied to both the I-cache and BTB. Using a large suite of recently released industrial traces, we show that predictive replacement policies can reduce misses in the I-cache and BTB. We introduce Global History Reuse Prediction (GHRP), a replacement technique that uses the history of past instruction addresses and their reuse behaviors to predict dead blocks in the I-cache and dead entries in the BTB. This paper describes the effectiveness of GHRP as a dead block replacement and bypass optimization for both the I-cache and BTB. For a 64KB set-associative I-cache with a 64B block size, GHRP lowers the I-cache misses per 1000 instructions (MPKI) by an average of 18% over the least-recently-used (LRU) policy on a set of 662 industrial workloads, performing significantly better than Static Re-reference Interval Prediction (SRRIP) and Sampling Dead Block Prediction (SDBP). For a 4K-entry BTB, GHRP lowers MPKI by an average of 30% over LRU, 23% over SRRIP, and 29% over SDBP.
The Public Value Creation of eGovernment: An Empirical Study from Citizen Perspective

Due to its inherent benefits, governments all over the world have invested huge capital on eGovernment with a view to improving internal efficiency and providing better and quality services to the citizenry. However, the success of eGovernment is best measured by the perceptions of citizens who use the services. This paper, the second of a two-part study seeks to investigate the types of public value that are desired as outcomes of eGovernment as well as the salient factors that are significant in predicting value creation from the perspective of citizens. To fulfil its objectives, the study adopts a theoretical model that integrates two streams of research: (i) the updated DeLone and McLean IS Success Model and (ii) Public Value. Using survey data collected from Nigeria, the model is empirically tested and validated through structural equation modelling analysis. Findings identify democracy, reflexivity and productivity in order of importance, as the three distinct value types desired as outcomes by citizens. Furthermore, results show service quality as the most salient factor significant in predicting citizen satisfaction and citizen trust in eGovernment, while both service quality and citizen satisfaction are significant in predicting net benefits. This research has both theoretical and practical implications. First, it contributes to the advancement of knowledge in the public value creation of eGovernment. Second, it provides a practical reference point for improving service delivery in the public sector.
Customized Regression Model for Airbnb Dynamic Pricing

This paper describes the pricing strategy model deployed at Airbnb, an online marketplace for sharing home and experience. The goal of price optimization is to help hosts who share their homes on Airbnb set the optimal price for their listings. In contrast to conventional pricing problems, where pricing strategies are applied to a large quantity of identical products, there are no "identical" products on Airbnb, because each listing on our platform offers unique values and experiences to our guests. The unique nature of Airbnb listings makes it very difficult to estimate an accurate demand curve that's required to apply conventional revenue maximization pricing strategies. Our pricing system consists of three components. First, a binary classification model predicts the booking probability of each listing-night. Second, a regression model predicts the optimal price for each listing-night, in which a customized loss function is used to guide the learning. Finally, we apply additional personalization logic on top of the output from the second model to generate the final price suggestions. In this paper, we focus on describing the regression model in the second stage of our pricing system. We also describe a novel set of metrics for offline evaluation. The proposed pricing strategy has been deployed in production to power the Price Tips and Smart Pricing tool on Airbnb. Online A/B testing results demonstrate the effectiveness of the proposed strategy model.
Acute Effect of Extreme Sports on Serum Lipids.

Purpose The aim of this study is to determine the effect of rafting and paragliding exercises by sedentary males on serum lipids. Material and Methods 17 male rafters and 10 male paragliders volunteers (non-smoker, no known history of cardiovascular disease, body mass index <25 kg/m2, and no intake of prescription medications) participated in the study. Participants had blood samples taken a day before and after rafting and paragliding practices. Data were analyzed by Wilcoxon and Mann-Whitney U tests. Results Significant decreases occurred for the low-density lipoprotein (LDL)/very low-density lipoprotein (VLDL) ratio in the rafting group after the exercise; though, there was no significant difference in serum lipids parameters of the paraglide group after the exercise. Conclusion While acute rafting and paragliding exercises have similar effects on TG and HDL, effect on LDL/VLDL ratio is different.
Joint Time–Frequency Scattering

In time series classification and regression, signals are typically mapped into some intermediate representation used for constructing models. Since the underlying task is often insensitive to time shifts, these representations are required to be time-shift invariant. We introduce the joint time–frequency scattering transform, a time-shift invariant representation that characterizes the multiscale energy distribution of a signal in time and frequency. It is computed through wavelet convolutions and modulus non-linearities and may, therefore, be implemented as a deep convolutional neural network whose filters are not learned but calculated from wavelets. We consider the progression from mel-spectrograms to time scattering and joint time–frequency scattering transforms, illustrating the relationship between increased discriminability and refinements of convolutional network architectures. The suitability of the joint time–frequency scattering transform for time-shift invariant characterization of time series is demonstrated through applications to chirp signals and audio synthesis experiments. The proposed transform also obtains state-of-the-art results on several audio classification tasks, outperforming time scattering transforms and achieving accuracies comparable to those of fully learned networks.
Energy-Efficient Neural Network Accelerator Based on Outlier-Aware Low-Precision Computation

Owing to the presence of large values, which we call outliers, conventional methods of quantization fail to achieve significantly low precision, e.g., four bits, for very deep neural networks, such as ResNet-101. In this study, we propose a hardware accelerator, called the outlier-aware accelerator (OLAccel). It performs dense and low-precision computations for a majority of data (weights and activations) while efficiently handling a small number of sparse and high-precision outliers (e.g., amounting to 3% of total data). The OLAccel is based on 4-bit multiply-accumulate (MAC) units and handles outlier weights and activations in a different manner. For outlier weights, it equips SIMD lanes of MAC units with an additional MAC unit, which helps avoid cycle overhead for the majority of outlier occurrences, i.e., a single occurrence in the SIMD lanes. The OLAccel performs computations using outlier activation on dedicated, high-precision MAC units. In order to avoid coherence problem due to updates from low- and high-precision computation units, both units update partial sums in a pipelined manner. Our experiments show that the OLAccel can reduce by 43.5% (27.0%), 56.7% (36.3%), and 62.2% (49.5%) energy consumption for AlexNet, VGG-16, and ResNet-18, respectively, compared with a 16-bit (8-bit) state-of-the-art zero-aware accelerator. The energy gain mostly comes from the memory components, the DRAM, and on-chip memory due to reduced precision.
Non-IT Energy Accounting in Virtualized Datacenter

Energy accounting plays a crucial role in datacenter energy management, wherein the energy consumption of non-IT units (e.g., UPS and cooling system) makes up a significant portion. However, it is challenging to fairly account for non-IT energy on an individual VM basis, because the non-IT units are shared by multiple VMs in a virtualized datacenter and only the system-level non-IT energy consumption can be measured. Existing policies, e.g., equally or proportionally allocating non-IT energy to VMs based on their IT energy, are not fair, in the sense that they can not satisfy a set of desired axiomatic principles of fair allocation. In this paper, we propose LEAPS, a Lightweight Energy Accounting Policy based on a provably fair methodology called Shapley value. We evaluate it using real-world datacenter trace and demonstrate that, compared to original Shapley value approach that has an exponential complexity, LEAPS yields almost the same energy accounting result within a maximum relative error less than 6.97%, while having a negligible computation time.
A Dynamic Pipeline for Spatio-Temporal Fire Risk Prediction

Recent high-profile fire incidents in cities around the world have highlighted gaps in fire risk reduction efforts, as cities grapple with fewer resources and more properties to safeguard. To address this resource gap, prior work has developed machine learning frameworks to predict fire risk and prioritize fire inspections. However, existing approaches were limited by not including time-varying data, never deploying in real-time, and only predicting risk for a small subset of commercial properties in their city. Here, we have developed a predictive risk framework for all 20,636 commercial properties in Pittsburgh, based on time-varying data from a variety of municipal agencies. We have deployed our fire risk model on Pittsburgh Bureau of Fire's (PBF), and we have developed preliminary risk models for residential property fire risk prediction. Our commercial risk model outperforms the prior state of the art with a kappa of 0.33 compared to their 0.17, and is able to be applied to nearly 4 times as many properties as the prior model. In the 5 weeks since our model was first deployed, 58% of our predicted high-risk properties had a fire incident of any kind, while 23% of the building fire incidents that occurred took place in our predicted high or medium risk properties. The risk scores from our commercial model are visualized on an interactive dashboard and map to assist the PBF with planning their fire risk reduction initiatives. This work is already helping to improve fire risk reduction in Pittsburgh and is beginning to be adopted by other cities.
Migrating Towards Microservice Architectures: An Industrial Survey

Microservices are gaining tremendous traction in industry and a growing scientific interest in academia. More and more companies are adopting this architectural style for modernizing their products and taking advantage of its promised benefits (e.g., agility, scalability). Unfortunately, the process of moving towards a microservice-based architecture is anything but easy, as there are plenty of challenges to address from both technical and organizational perspectives. In this paper we report about an empirical study on migration practices towards the adoption of microservices in industry. Specifically, we designed and conducted a survey targeting practitioners involved in the process of migrating their applications and we collected information (by means of interviews and questionnaires) on (i) the performed activities, and (ii) the challenges faced during the migration. Our findings benefit both (i) researchers by highlighting future directions for industryrelevant problems and (ii) practitioners by providing a reference framework for their (future) migrations towards microservices.
Compressed Image Restoration via External-Image Assisted Band Adaptive PCA Model Learning

Visually annoying compression artifacts frequently appear in block-based transform coding at low bit rates, due to coarse and independent quantization of transform coefficients in coding blocks. This paper presents a subband adaptive modeling framework for reducing quantization artifacts. In this framework, each patch is jointly regularized by bandwise distribution priors adaptively learned in its PCA transform domain together with a quantization constraint prior in the DCT domain. Since the compression artifacts influence the covariance statistics of coded image patches remarkably, external images are utilized to provide more robust PCA domains for patch sparse modeling. Instead of using a global distribution model for all patches, the distribution prior of each patch is adaptively learned from similar patches within the compressed image itself to address the non-stationarity of image signals. The coefficients in different PCA bands are regularized unequally according to the learned priors. Experimental results show that the proposed scheme outperforms existing schemes in terms of both the objective and the perceptual qualities.
Towards Demystifying Membership Inference Attacks

Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API. Aiming at providing an in-depth characterization of membership privacy risks against machine learning models, this paper presents a comprehensive study towards demystifying membership inference attacks from two complimentary perspectives. First, we provide a generalized formulation of the development of a black-box membership inference attack model. Second, we characterize the importance of model choice on model vulnerability through a systematic evaluation of a variety of machine learning models and model combinations using multiple datasets. Through formal analysis and empirical evidence from extensive experimentation, we characterize under what conditions a model may be vulnerable to such black-box membership inference attacks. We show that membership inference vulnerability is data-driven and its attack models are largely transferable. Though different model types display different vulnerabilities to membership inferences, so do different datasets. Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning in federated systems exposes vulnerabilities to membership inference risks when the adversary is a participant in the federation. We also discuss countermeasure and mitigation strategies.
Exogenous attention intensifies perceived emotion expressions

Abstract Spatial attention not only enhances early visual processing and improves performance but also alters phenomenology of basic perceptual features. However, in spite of extensive research on attention altering appearance, it is still unknown whether attention also intensifies perceived facial emotional expressions. We investigated the effect of exogenous attention on two categories of emotions, one positive (happy) and one negative (sad) in separate sessions. Exogenous attention was manipulated using peripheral cues followed by two faces varying in emotional intensity that were presented on either side of fixation. Participants were asked to report the location of the emotional face displaying higher intensity of emotion. At short cue-to-target interval [CTI, Experiment 1 (60 ms)], participants reported the cued emotional face as more intense in expression compared with the uncued face. However, at longer CTI [Experiment 2 (500 ms)], this effect was absent. Results show that exogenous attention enhances appearance of higher level features, such as emotional intensity, irrespective of valence. Further, two experiments investigated the mediating role of facial contrast as a possible underlying mechanism for the observed effect. Although the results show that higher contrast faces are judged as more in emotional intensity, spatial attention effects seem to be dependent on task instructions. Possible mechanisms underlying the attentional effects on emotion intensity are discussed.
Real-World Multiobject, Multigrasp Detection

A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classified with null hypothesis competition instead of regression, the deep neural network with red, green, blue and depth (RGB-D) image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on imagewise and objectwise splits, respectively. Evaluation on a multiobject dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 89.0% grasping success rates on a test set of household objects. The real-time process takes less than 0.25 s from image to plan.