Reviewerly/exHarmony huggingface dataset:
COTSknight: Practical Defense against Cache Timing Channel Attacks using Cache Monitoring and Partitioning Technologies

Recent studies have shown how adversaries can exploit hardware cache structures to launch information leakage-based attacks. Among these attacks, timing channels are especially worrisome since adversaries communicate simply by modulating the timing of shared resource accesses, and do not leave any physical trace of the communication. Therefore, guarding the system against such attacks is critical. Unfortunately, most existing mitigation mechanisms either require non-trivial hardware modifications and/or incur high runtime overheads. In this paper, we propose COTSknight, a new framework that guards the system against several classes of cache timing channel attacks by making novel use of Commercial Off-The-Shelf (COTS) architectural support for cache resource monitoring and prioritization. We find that the adversary's attempt to modulate cache access latency during attacks can be captured using cache occupancy patterns. COTSknight leverages efficient signal processing techniques on cache occupancy patterns to determine the potential for timing channel attacks. Once suspicious domains are identified, COTSknight disbands timing channels using dynamic cache partitioning schemes in hardware. We implement a prototype of our COTSknight framework on an Intel Xeon v4 server and evaluate its efficacy extensively using different spatial encoding schemes, as well as serial and parallel implementations of Last Level Cache (LLC) timing channels. Our results show that COTSknight can successfully thwart several classes of timing channel attacks by allocating disjoint LLC ways to malicious processes. Even in benign cache-intensive workloads, we observe a 6% cache partition trigger rate that results in a relatively small 5% worst-case performance degradation. Interestingly, for some benign applications, upon COTSknight's cache partition, we observe an improved performance by up to 9.2% through eliminating cache interference.
Finding Users Who Act Alike

Audience Look-alike Targeting is an online advertising technique in which an advertiser specifies a set of seed customers and tasks the advertising platform with finding an expanded audience of similar users. We will describe a two-stage embedding-based audience expansion model that is deployed in production at Pinterest. For the first stage we trained a global user embedding model on sitewide user activity logs. In the second stage, we use transfer learning and statistical techniques to create lightweight seed list representations in the embedding space for each advertiser. We create a (user, seed list) affinity scoring function that makes use of these lightweight advertiser representations. We describe the end-to-end system that computes and serves this model at scale. Finally, we propose an ensemble approach that combines single-advertiser classifiers with the embedding-based technique. We show offline evaluation and online experiments to prove that the expanded audience generated by the ensemble model has the best results for all seed list sizes.
DeQA

Today there is no effective support for device-wide question answering on mobile devices. State-of-the-art QA models are deep learning behemoths designed for the cloud which run extremely slow and require more memory than available on phones. We present DeQA, a suite of latency- and memory- optimizations that adapts existing QA systems to run completely locally on mobile phones. Specifically, we design two latency optimizations that (1) stops processing documents if further processing cannot improve answer quality, and (2) identifies computation that does not depend on the question and moves it offline. These optimizations do not depend on the QA model internals and can be applied to several existing QA models. DeQA also implements a set of memory optimizations by (i) loading partial indexes in memory, (ii) working with smaller units of data, and (iii) replacing in-memory lookups with a key-value database. We use DeQA to port three state-of-the-art QA systems to the mobile device and evaluate over three datasets. The first is a large scale SQuAD dataset defined over Wikipedia collection. We also create two on-device QA datasets, one over a publicly available email data collection and the other using a cross-app data collection we obtain from two users. Our evaluations show that DeQA can run QA models with only a few hundred MBs of memory and provides at least 13x speedup on average on the mobile phone across all three datasets.% with less than a 1% drop in accuracy.
Blockchain Technology for e-Marketplace

We developed a blockchain-based E-marketplace. A decentralized E-marketplace platform utilizing the blockchain technology is implemented. We use the self-enforcement of smart contracts to secure the deposit and process the payment. Each transaction is verified through the blockchain and is recorded to the decentralized ledger. This enables trustless transactions since the smart contract is self-executed. The smart contract is able to perform credible transactions without trusted third parties, and the transactions on the blockchain are trackable and irreversible. Therefore, both the buyer and the seller cannot breach the contract. All processes are recorded on the blockchain including the product launch, purchase, delivery, and payment. It is trackable and could be submitted to courts as electronic evidence to solve the transaction disputes.
Deep fusion based video saliency detection

This paper introduces a novel video saliency model for salient object detection in videos. Firstly, we generate multi-level deep features via a symmetrical convolutional neural network, in which the inputs are the current frame and the optical flow image. Then, the multi-level deep features are integrated in a hierarchical manner using a fusion network, which deploys attention module to make a selection for deep features. Lastly, the integrated deep feature is combined with the boundary information originated from shallow layer of the feature extraction networks, and the saliency map is generated by the saliency prediction step. The key advantages of our model lie on the attention module, the hierarchical integration and the boundary information, in which the former one acts as weight filter and is used to select the most salient regions in deep features, the middle one gives an effective integration manner for features from different layers and the last one provides well-defined boundaries for saliency map. Extensive experiments are performed on two challenging video dataset, and the experimental results show that our model consistently outperforms the state-of-the-art saliency models in a large margin.
OmniGlobe

Video communication systems have been suffered from the narrow field of view. To solve this limitation, one study proposed symmetric 360° video communication system by combining an omnidirectional camera and a hemispherical display. However, the system still had several issues, e.g., the invisibility of hemisphere which was at the opposite side from a user caused the inconvenience of observing the remote environment. To solve these issues, we introduce OmniGlobe, a novel symmetric full 360° video communication system which incorporates an omnidirectional camera, a full spherical display, and several visual or interactive techniques. Based on an experiment, we could indicate that our system is effective in reducing the inconvenience of observing the remote environment and increased the remote space awareness and user's gaze awareness to support remote collaboration. We also discuss the takeaways, limitations and application areas in our system which help improve the system.
Two‐order graph convolutional networks for semi‐supervised classification

Currently, deep learning (DL) algorithms have achieved great success in many applications including computer vision and natural language processing. Many different kinds of DL models have been reported, such as DeepWalk, LINE, diffusionconvolutional neural networks, graph convolutional networks (GCN), and so on. The GCN algorithm is a variant of convolutional neural network and achieves significant superiority by using a one-order localised spectral graph filter. However, only a one-order polynomial in the Laplacian of GCN has been approximated and implemented, which ignores undirect neighbour structure information. The lack of rich structure information reduces the performance of the neural networks in the graph structure data. In this study, the authors deduce and simplify the formula of two-order spectral graph convolutions to preserve rich local information. Furthermore, they build a layerwise GCN based on this two-order approximation, i.e. two-order GCN (TGCN) for semi-supervised classification. With the two-order polynomial in the Laplacian, the proposed TGCN model can assimilate abundant localised structure information of graph data and then boosts the classification significantly. To evaluate the proposed solution, extensive experiments are conducted on several popular datasets including the Citeseer, Cora, and PubMed dataset. Experimental results demonstrate that the proposed TGCN outperforms the state-of-art methods.
Evaluation of solar irradiance forecasting skills of the Australian Bureau of Meteorology’s ACCESS models

The increasing penetration of solar power into the electricity grid has created the need for accurate solar power forecasts for facilitating safe and reliable electricity grid management. Numerical Weather Prediction (NWP) is a common method for forecasting solar irradiance beyond several hours ahead, which is crucial, particularly for applications in day-ahead energy markets as well as for energy management at different scales, ranging from utility-wide to residential level. In this study, we evaluated the ability of the Australian Bureau of Meteorology’s (BoM) numerical weather prediction system, the Australian Community Climate and Earth-System Simulator (ACCESS) model, to forecast solar irradiance. The global horizontal irradiance (GHI), direct horizontal irradiance (DIRHI) and diffuse horizontal irradiance (DHI) forecasts from the APS2 version of the regional (ACCESS-R) and the city scale (ACCESS-C) mesoscale models of ACCESS were validated against irradiance measurements from 11 ground stations across different climatic conditions in Australia. While the high resolution (1.5 km grid) city models were found to better capture the cloud variability in comparison to the coarse resolution (12 km grid) regional model, the forecast performance for both models were comparable in terms of Root Mean Square Deviation (RMSD). The annual average RMSD for both models ranged between 105 W/m2–156 W/m2, 131 W/m2–181 W/m2 and 71 W/m2–95 W/m2 for horizontal global, direct and diffuse components of irradiance, respectively, across different locations. Moreover, it was found that both models have a tendency to overpredict DHI up to 50% during clear skies, provided the sky conditions are correctly forecasted. Further, selection criteria based on forecasted sky conditions are presented to help identify the more accurate forecasts among the city and the regional model.
Cell Nuclei Detection and Segmentation for Computational Pathology Using Deep Learning

This work presents a deep learning model and image processing based processing flow to detect and segment nuclei from microscopy images. This work aims at isolating each nuclei by segmenting the boundary and detecting the geometric center of the nuclei. The deep learning model employs a multi-layer convolutional neural network based architecture to extract features from both spatial and color information and to generate a gray scaled image mask. Subsequent image processing steps smooth nuclei boundaries, isolate each individual nuclei and calculate the geometric center of the nuclei. The proposed work has been implemented and tested using H & E stained microscopy images containing seven different tissue samples. Experimental results demonstrated an average precision of 0.799, recall of 0.955, F-score of 0.86, and IoU of 0.835.
Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty

We suggest a general approach to quantification of different forms of aleatoric uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function and a specific hyperparameter λ > 0 that allows for automatically detecting noisy and clean regions in the input space and controlling their relative contribution to the loss and its gradients. After the model has been trained, one of the networks performs predictions and the other quantifies the uncertainty of these predictions by estimating the locally averaged loss of the first one. Unlike in many classical uncertainty quantification methods, we do not assume any a priori knowledge of the ground truth probability distribution, neither do we, in general, maximize the likelihood of a chosen parametric family of distributions. We analyze the learning process and the influence of clean and noisy regions of the input space on the loss surface, depending on λ. In particular, we show that small values of λ increase the relative contribution of clean regions to the loss and its gradients. This explains why choosing small λ allows for better predictions compared with neural networks without uncertainty counterparts and those based on classical likelihood maximization. Finally, we demonstrate that one can naturally form ensembles of pairs of our networks and thus capture both aleatoric and epistemic uncertainty and avoid overfitting.
Rendering multi-party mobile augmented reality from edge

Mobile augmented reality (MAR) augments a real-world environment (probably surrounding or close to the mobile user) by computer-generated perceptual information. Utilizing the emerging edge computing paradigm in MAR systems can reduce the power consumption and computation load for the mobile devices and improve responsiveness of the MAR service. Different from existing studies that mainly explored how to better enable the MAR services utilizing edge computing resources, our focus is to optimize the video generation stage of the edge-based MAR services-efficiently using the available edge computing resources to render and encode the augmented reality as video streams to the mobile clients. Specifically, for multi-party AR applications, we identify the advantages and disadvantages of two encoding schemes, namely colocated encoding and spilt encoding, and examine the trade-off between performance and scalability when the rendering and encoding tasks are colocated or split. Towards optimally placing AR video rendering and encoding in the edge, we formulate and solve the rendering and encoding task assignment problem for multi-party edge-based MAR services to maximize the QoS for the users and the edge computing efficiency. The proposed task assignment scheme is proved to be superior through extensive trace-driven simulations and experiments on our prototype system.
Adaptively Dense Feature Pyramid Network for Object Detection

We propose a novel one-stage object detection network, called adaptively dense feature pyramid network (ADFPNet), to detect objects cross various scales.The proposed network is developed on single shot multibox detector (SSD) framework with a new proposed ADFP module, which is consisted of two components: a dense multi scales and receptive fields block (DMSRB) and an adaptively feature calibration block (AFCB).Specifically, DMSRB block extracts rich semantic information in a dense way through atrous convolutions with different atrous rates to extract dense features in multi scales and receptive fields; the AFCB block calibrate the dense features to retain features contributing more and depress features contributing less.The extensive experiments have been conducted on VOC 2007, VOC 2012, and MS COCO dataset to evaluate our method.In particular, we achieve the new state of the art accuracy with the mAP of 82.5 on VOC 2007 test set and the mAP of 36.4 on COCO test-dev set using a simple VGG-16 backbone.When testing with a lower resolution (300 × 300), we achieve an mAP of 81.1 on VOC 2007 test set with an FPS of 62.5 on an NVIDIA 1080ti GPU, which meets the requirement for real-time detection.
Boa Meets Python: A Boa Dataset of Data Science Software in Python Language

The popularity of Python programming language has surged in recent years due to its increasing usage in Data Science. The availability of Python repositories in Github presents an opportunity for mining software repository research, e.g., suggesting the best practices in developing Data Science applications, identifying bug-patterns, recommending code enhancements, etc. To enable this research, we have created a new dataset that includes 1,558 mature Github projects that develop Python software for Data Science tasks. By analyzing the metadata and code, we have included the projects in our dataset which use a diverse set of machine learning libraries and managed by a variety of users and organizations. The dataset is made publicly available through Boa infrastructure both as a collection of raw projects as well as in a processed form that could be used for performing large scale analysis using Boa language. We also present two initial applications to demonstrate the potential of the dataset that could be leveraged by the community.
Twin minimax probability extreme learning machine for pattern recognition

Minimax probability machine (MPM) is an excellent discriminant classifier based on prior knowledge. It can directly estimate a probability accuracy bound by minimizing the maximum probability of misclassification. However, the traditional MPM learns only one hyperplane to separate different classes in the feature space, and it may bear a heavy computational burden during the training process because it needs to address a large-scale second-order cone programming (SOCP)-type problem. In this work, we propose a novel twin minimax probability extreme learning machine (TMPELM) for pattern classification. TMPELM indirectly determines the separation hyperplane by solving a pair of smaller-sized SOCP-type problems to generate a pair of non-parallel hyperplanes. Specifically, for each hyperplane, TMPELM attempts to maximize the probability of correct classification for one class sample points, that is, to minimize the worst-case (maximum) probability of misclassification of a class sample points, and is far away from the other class. TMPELM first utilizes the random feature mapping mechanism to construct the feature space, and then two nonparallel separating hyperplanes are learned for the final classification. The proposed TMPELM not only utilizes the geometric information of the samples, but also takes advantage of the statistical information (mean and covariance of the samples). Moreover, we extend a linear model of TMPELM to a nonlinear model by exploiting kernelization techniques. We also analyzed the computational complexity of TMPELM. Experimental results on both NIR spectroscopy datasets and benchmark datasets demonstrate the effectiveness of TMPELM.
4D Affect Detection: Improving Frustration Detection in Game-Based Learning with Posture-Based Temporal Data Fusion

Recent years have seen growing interest in utilizing sensors to detect learner affect. Modeling frustration has particular significance because of its central role in learning. However, sensor-based affect detection poses important challenges. Motion-tracking cameras produce vast streams of spatial and temporal data, but relatively few systems have harnessed this data successfully to produce accurate run-time detectors of learner frustration outside of the laboratory. In this paper, we introduce a data-driven framework that leverages spatial and temporal posture data to detect learner frustration using deep neural network-based data fusion techniques. To train and validate the detectors, we utilize posture data collected with Microsoft Kinect sensors from students interacting with a game-based learning environment for emergency medical training. Ground-truth labels of learner frustration were obtained using the BROMP quantitative observation protocol. Results show that deep neural network-based late fusion techniques that combine spatial and temporal data yield significant improvements to frustration detection relative to baseline models.
Gelicit

A gesture elicitation study, as originally defined, consists of gathering a sample of participants in a room, instructing them to produce gestures they would use for a particular set of tasks, materialized through a representation called referent, and asking them to fill in a series of tests, questionnaires, and feedback forms. Until now, this procedure is conducted manually in a single, physical, and synchronous setup. To relax the constraints imposed by this manual procedure and to support stakeholders in defining and conducting such studies in multiple contexts of use, this paper presents Gelicit, a cloud computing platform that supports gesture elicitation studies distributed in time and space structured into six stages: (1) define a study: a designer defines a set of tasks with their referents for eliciting gestures and specifies an experimental protocol by parameterizing its settings; (2) conduct a study: any participant receiving the invitation to join the study conducts the experiment anywhere, anytime, anyhow, by eliciting gestures and filling forms; (3) classify gestures: an experimenter classifies elicited gestures according to selected criteria and a vocabulary; (4) measure gestures: an experimenter computes gesture measures, like agreement, frequency, to understand their configuration; (5) discuss gestures: a designer discusses resulting gestures with the participants to reach a consensus; (6) export gestures: the consensus set of gestures resulting from the discussion is exported to be used with a gesture recognizer. The paper discusses Gelicit advantages and limitations with respect to three main contributions: as a conceptual model for gesture management, as a method for distributed gesture elicitation based on this model, and as a cloud computing platform supporting this distributed elicitation. We illustrate Gelicit through a study for eliciting 2D gestures executing Internet of Things tasks on a smartphone.
Optical image encryption and transmission with semiconductor lasers

Optical chaos has great advantages in image encryption and optical message secure transmission, hence a novel image encryption and transmission system based on optical chaos is proposed. In this paper, we introduce optical chaos generated by the semiconductor laser in the encryption process. For increasing the chaotic complexity of slave lasers (SL1 and SL2), the master laser (ML) is subjected to the optical feedback and the external optical injection from the driven laser (DL). Then the SL1 and SL2 can generate the optical chaotic signal simultaneously, which can be used as chaotic carrier and the key for better security. The secret key and a new chaos sequence for the permutation can be structured by selecting randomly numbers from the optical chaos signal generated by the SL1 (or SL2). Furthermore, the result of color image encryption based on the Hilbert curve (HC) can be improved by introducing the optical chaos, after employing logistic chaotic map, the pixel positions and values of plain image are changed disorderly. The security analysis and simulation results can demonstrate the feasibility of proposed algorithms. Moreover, under the condition of chaos synchronization between the SL1 and SL2, the encrypted image can be modulated by using the chaos masking (CMS) technique and transmitted in 10 km single mode fiber (SMF) channel from the SL1 to SL2. The bit error rate (BER) can be for less than 4.22×10-20 and high quality factor (Q-factor) can be also for more than 9.