Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Matters: Jointly Train Advertising CTR Model with Image Representation of Ad and User Behavior

Click Through Rate(CTR) prediction is vital for online advertising system. Recently sparse ID features are widely adopted in the industry. While the ID features, e.g. the serial number of ad, are of low computation complexity and cheap to acquire, they can reveal little intrinsic information about the ad itself. In this work, we propose a novel Deep Image CTR Model(DICM). DICM i) introduces image content features to exploit the intrinsic description of ad/goods and shows effectiveness on complete dataset in accordance with the product environment of a real commercial advertising system; ii) not only represents ad with image features, but also, for the first time, jointly models the user behaviors and ads with image features to capture user interests. However, the users historical behaviors involve massive images for industry scale training(e.g. 2.4 million images per mini-batch with the batchsize of 60k), which brings great challenges on both computation and storage. To tackle the challenges, we carefully design a highly efficient distributed system which enables daily-updated model training on billions of samples essential for product deployment. Extensive experiments show that the image features can be effective representations as well as good complements to the corresponding ID features.
prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks

Phase retrieval (PR) algorithms have become an important component in many modern computational imaging systems. For instance, in the context of ptychography and speckle correlation imaging PR algorithms enable imaging past the diffraction limit and through scattering media, respectively. Unfortunately, traditional PR algorithms struggle in the presence of noise. Recently PR algorithms have been developed that use priors to make themselves more robust. However, these algorithms often require unrealistic (Gaussian or coded diffraction pattern) measurement models and offer slow computation times. These drawbacks have hindered widespread adoption. In this work we use convolutional neural networks, a powerful tool from machine learning, to regularize phase retrieval problems and improve recovery performance. We test our new algorithm, prDeep, in simulation and demonstrate that it is robust to noise, can handle a variety system models, and operates fast enough for high-resolution applications.
On the Complexity of Modal Team Logic and Two-Variable Team Logic

We study modal team logic MTL and the dependence-free fragment FO^2[~] of two-variable team logic, which extend modal logic ML and two-variable logic FO^2 with team semantics by a Boolean negation ~. We settle the open question of the complexity of their respective satisfiability problems, and prove that both are complete for a non-elementary complexity class. We also prove that the model checking problem is PSPACE-complete for several fragments of MTL and FO[~]. Based on the well-known standard translation from ML to FO^2, we propose an team-semantical translation from MTL into FO^2[~].
Attention-based CNN Matching Net

In this paper, we introduce attention-based CNN matching net (ACM-Net), an end-to-end neural network for question answering. ACM-Net matches between the given passage, query and multiple answer choices, and then it extracts features from passage and choices based on query information. We also propose a two-staged CNN architecture and a query-based attention mechanism in our model. These two component can effectively find out the most important parts in passage according to the query. Finally, we extract features from those important parts and find out the most possible answer choice. We conduct this model on the MovieQA dataset using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.
Rough terrain robot with 4 turn-over track frames-a feasibility study on the mechanism

Mobile mechanisms with tracks have been studied over the years, with the aim of using them in rough terrain environments. In 1992, the authors proposed a non-interference infinitely-turning 4-crawler mechanism. In this paper, the feasibility study results with a full-scale motorized model are announced. Field tests verify its powerful mobile functions such as climbing and descending high earth banks, and escaping from becoming stuck in muddy ground.
Dilated Residual Network for Image Denoising

Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean image patches. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount of parameters and are computationally expensive to train. In this paper, we develop a dilated residual CNN for Gaussian image denoising. Compared with the recently proposed residual denoiser, our method can achieve comparable performance with less computational cost. Specifically, we enlarge receptive field by adopting dilated convolution in residual network, and the dilation factor is set to a certain value. Appropriate zero padding is utilized to make the dimension of the output the same as the input. It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem. Moreover, we present a formula to calculate receptive field size when dilated convolution is incorporated. Thus, the change of receptive field can be interpreted mathematically. To validate the efficacy of our approach, we conduct extensive experiments for both gray and color image denoising with specific or randomized noise levels. Both of the quantitative measurements and the visual results of denoising are promising comparing with state-of-the-art baselines. Keywords— Image Denoising, Dilated Convolution, Residual Learning, Convolutional Neural Networks, Deep Learning, Image Processing
Discovering Adversarial Examples with Momentum

Machine learning models, especially Deep Neural Networks, are vulnerable to adversarial examples---malicious inputs crafted by adding small noises to real examples, but fool the models. Adversarial examples transfer from one model to another, enabling black-box attacks to real-world applications. In this paper, we propose a strong attack algorithm named momentum iterative fast gradient sign method (MI-FGSM) to discover adversarial examples. MI-FGSM is an extension of iterative fast gradient sign method (I-FGSM) but improves the transferability significantly. Besides, we study how to attack an ensemble of models efficiently. Experiments demonstrate the effectiveness of the proposed algorithm. We hope that MI-FGSM can serve as a benchmark attack algorithm for evaluating the robustness of various models and defense methods.
Using Statistical and Semantic Models for Multi-Document Summarization

We report a series of experiments with different semantic models on top of various statistical models for extractive text summarization. Though statistical models may better capture word co-occurrences and distribution around the text, they fail to detect the context and the sense of sentences /words as a whole. Semantic models help us gain better insight into the context of sentences. We show that how tuning weights between different models can help us achieve significant results on various benchmarks. Learning pre-trained vectors used in semantic models further, on given corpus, can give addition spike in performance. Using weighing techniques in between different statistical models too further refines our result. For Statistical models, we have used TF/IDF, TextRAnk, Jaccard/Cosine Similarities. For Semantic Models, we have used WordNet-based Model and proposed two models based on Glove Vectors and Facebook's InferSent. We tested our approach on DUC 2004 dataset, generating 100-word summaries. We have discussed the system, algorithms, analysis and also proposed and tested possible improvements. ROUGE scores were used to compare to other summarizers.
Using smartphone sensor technology for mental health research: Clear obstacles and hidden challenges

Background: Smartphone sensor technology has great potential in providing behavioural markers of mental health. However, this promise has not yet been brought to fruition. Objective: To examine the challenges involved in developing an app to extract behavioural markers of mental health from passive sensor data. Methods: Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results from a feasibility study. Socialise, a smartphone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, GPS and battery status) and investigate the views and experiences of a group of people with a lived experience of mental health challenges (n=32). Results: On average, sensor data was obtained for 55% (Android) and 45% (iOS) of the scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes, a reduction of 2.5 hours or 12%. Despite this limited reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organisation that collects the data, and perceived impact on privacy were identified as the main factors for acceptability. Conclusions: Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting, and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of their use to be rigorously assessed.
Securing the Internet of Things: New Perspectives and Research Challenges

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. IoT challenges require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges to address existing and next-generation IoT security threats.
Brief Announcement: Semi-MapReduce Meets Congested Clique

Graph problems are troublesome when it comes to MapReduce. Typically, to be able to design algorithms that make use of the advantages of MapReduce, assumptions beyond what the model imposes, such as the {\em density} of the input graph, are required. In a recent shift, a simple and robust model of MapReduce for graph problems, where the space per machine is set to be $O(|V|)$ has attracted considerable attention. We term this model {\em semi-MapReduce}, or in short, semi-MPC, and focus on its computational power. In this short note, we show through a set of simulation methods that semi-MPC is, perhaps surprisingly, almost equivalent to the congested clique model of distributed computing. However, semi-MPC, in addition to round complexity, incorporates another practically important dimension to optimize: the number of machines. Furthermore, we show that algorithms in other distributed computing models, such as CONGEST, can be simulated to run in the same number of rounds of semiMPC while also using an optimal number of machines. We later show the implications of these simulation methods by obtaining improved algorithms for these models using the recent algorithms that have been developed.
Demon: An Efficient Solution for on-Device MMU Virtualization in Mediated Pass-Through

Memory Management Units (MMUs) for on-device address translation are widely used in modern devices. However, conventional solutions for on-device MMU virtualization, such as shadow page table implemented in mediated pass-through, still suffer from high complexity and low performance. We present Demon, an efficient solution for on-DEvice MMU virtualizatiON in mediated pass-through. The key insight is that Demon takes advantage of IOMMU to construct a two-dimensional address translation and dynamically switches the 2nd-dimensional page table to a proper candidate when the device owner switches. In order to support fine-grained parallelism for the device with multiple engines, we put forward a hardware proposal that separates the address space of each engine and enables simultaneous device address remapping for multiple virtual machines (VMs). We implement Demon with a prototype named gDemon which virtualizes Intel GPU MMU. Nonetheless, Demon is not limited to this particular case. Evaluations show that gDemon provides up to 19.73x better performance in the media transcoding workloads and achieves performance improvement of up to 17.09% and 13.73% in the 2D benchmarks and 3D benchmarks, respectively, compared with gVirt. The current release of gDemon scales up to 6 VMs with moderate performance in our experiments. In addition, gDemon simplifies the implementation of GPU MMU virtualization with 37% code reduction.
Measurement Errors and Uncertainties: Theory and Practice

From the contents: Preface General information about measurements Measuring instruments and their properties Prerequisites for the analysis of the inaccuracy of measurements and for synthesis of their components Statistical methods for analysis of multiple measurements in the absence of systematic errors Direct measurements Indirect measurements Simultaneous and combined measurements Combining the results of measurements Calculation of the errors of measuring instruments Problems in the theory of calibration Methods for calculating the correlation coefficient and accounting of dependencies between components of measurement uncertainty Appendix Glossary References Index.
A Hybrid Data Mining Approach for Product Complexity Analysis

This paper proposes a hybrid data mining approach to quantitatively analyze product complexity of prefabricated construction components from product nonconforming quality performance data. The proposed model is constructed in three steps, which (1) measure product complexity by introducing a Bayesian-based nonconforming quality performance indicator; (2) score each type of product complexity by developing a Hellinger distance-based distribution similarity measurement; and (3) cluster products into homogeneous complexity groups by using the agglomerative hierarchical clustering technique. An illustrative example is provided to demonstrate the proposed approach, and a case study of an industrial company in Edmonton, Canada, is conducted to validate the feasibility and applicability of the proposed model. This research inventively defines and investigates product complexity from the perspective of product quality performance. The research outcomes provide valuable insights for practitioners to better analyze and manage product complexity. In addition to this practical contribution, a novel hierarchical clustering technique is devised. This technique is capable of clustering uncertain data (i.e., probability distributions) and has the potential to be generalized to cluster all types of uncertain data.
Oko: Extending Open vSwitch with Stateful Filters

With the Software-Defined Networking paradigm, software switches emerged as the new edge of datacenter networks. The widely adopted Open vSwitch implements the OpenFlow forwarding model; its simple match-action abstraction eases network management, while providing enough flexibility to define complex forwarding pipelines. OpenFlow, however, cannot express the many packets processing algorithms required for traffic measurement, network security, or congestion diagnosis, as it lacks a persistent state and basic arithmetic and logic operations. This paper presents Oko, an extension of Open vSwitch that enables runtime integration of stateful filtering and monitoring functionalities based on Berkeley Packet Filter (BPF) programs into the OpenFlow pipeline. BPF programs attached to OpenFlow rules act as intelligent filters over packets, while leaving the packets unmodified. This approach enables the transparent extension of Open vSwitch's flow caching architecture, retaining its high-performance benefits. Furthermore, the use of BPF allows for safe runtime extension and prevention of switch failures due to faulty programs. We compare our implementation based on Open vSwitch-DPDK to existing approaches with comparable isolation properties and measure a near 2x improvement of performance.
Generating Instance Segmentation Annotation by Geometry-guided GAN

Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, physical reasoning, and GAN techniques, we introduce a novel pipeline named Geometry-guided GAN (GeoGAN) to obtain large quantities of training samples with minor annotation. Our pipeline is well-suited to most indoor and some outdoor scenarios. To evaluate our performance, we build a new Instance-60K dataset, with various of common objects categories. Extensive experiments show that our pipeline can achieve decent instance segmentation performance given very low human annotation cost.
A Systematic Evaluation of API-Misuse Detectors

Application Programming Interfaces (APIs) often have usage constraints, such as call order or call conditions. API misuses, i.e., violations of these constraints, may lead to software crashes, bugs, and vulnerabilities. Though researchers developed many API-misuse detectors over the last two decades, recent studies show that API misuses are still prevalent. Therefore, we need to understand the capabilities and limitations of existing detectors in order to advance the state of the art. In this paper, we present the first-ever qualitative and quantitative evaluation that compares API-misuse detectors along the same dimensions, and with author validation. To accomplish this, we develop MUC, a classification of API misuses, and MUBENCHPIPE, an automated benchmark for detector comparison, on top of our misuse dataset, MUBENCH. Our results show that the capabilities of existing detectors vary greatly and that existing detectors, though capable of detecting misuses, suffer from extremely low precision and recall. A systematic root-cause analysis reveals that, most importantly, detectors need to go beyond the naive assumption that a deviation from the most-frequent usage corresponds to a misuse and need to obtain additional usage examples to train their models. Our work provides these and several other novel insights that enable more powerful API-misuse detectors.
Lightweight MPI Communicators with Applications to Perfectly Balanced Schizophrenic Quicksort

MPI uses the concept of communicators to connect groups of processes. It provides nonblocking collective operations on communicators to overlap communication and computation. Flexible algorithms demand flexible communicators. E.g., a process can work on different subproblems within different process groups simultaneously, new process groups can be created, or the members of a process group can change. Depending on the number of communicators, the time for communicator creation can drastically increase the running time of the algorithm. Furthermore, a new communicator synchronizes all processes as communicator creation routines are blocking collective operations. We present RBC, a communication library based on MPI, that creates range-based communicators in constant time without communication. These RBC communicators support (non)blocking point-to-point communication as well as (non)blocking collective operations. Our experiments show that the library reduces the time to create a new communicator by a factor of more than 400 whereas the running time of collective operations remains about the same. We propose Schizophrenic Quicksort, a distributed sorting algorithm that avoids any load imbalances. We improved the performance of this algorithm by a factor of 15 for moderate inputs by using RBC communicators. Finally, we discuss different approaches to bring nonblocking (local) communicator creation of lightweight (range-based) communicators into MPI.
Pausing and Resuming Network Flows using Programmable Buffers

The emerging 5G networks and applications require network traffic to be buffered at different points in a wide area network with different policies based on user mobility, usage patterns, device and application types. Existing Software Defined Network (SDN) solutions fail to provide sufficient decoupling between the data-plane and control-plane to enable efficient control of where, when, and how a network flow is buffered without causing excessive control-plane traffic. Alternative approaches either cater to application-specific use cases or follow completely new paradigms that are not compatible with 5G evolution. In this paper, we present (1) a new programming abstraction for network buffering, (2) a new set of northbound APIs for network applications to pause and resume traffic flows in the network, (3) a new set of southbound APIs to scalably control where, when, and how a network flow is buffered, and (4) evaluations based on the prototype implementation of data-plane and control-plane functions. Our evaluations indicate significant performance and scalability potentials, and show near-optimal results in mobility-management and connectionless communication use cases.
Interpretation of Mammogram and Chest Radiograph Reports using Deep Neural Networks - Preliminary Results

Radiology reports are an important means of communication between radiologists and other physicians. These reports express a radiologist's interpretation of a medical imaging examination and are critical in establishing a diagnosis and formulating a treatment plan. In this paper, we propose a Bi-directional convolutional neural network (Bi-CNN) model for the interpretation and classification of mammograms based on breast density and chest radiographic radiology reports based on the basis of chest pathology. The proposed approach helps to organize databases of radiology reports, retrieve them expeditiously, and evaluate the radiology report that could be used in an auditing system to decrease incorrect diagnoses. Our study revealed that the proposed Bi-CNN outperforms the random forest and the support vector machine methods.