Reviewerly/exHarmony huggingface dataset:
FPGA-Based Network Traffic Classification Using Machine Learning

Real-time classification of internet traffic is critical for the efficient management of networks. Classification approaches based on machine learning techniques have shown promising results with high levels of accuracy. In this article, the suitability of packet-level and flow-level features is validated using stepwise regression and random forest feature selection. Moreover, the optimal percentage of packets considered within a flow while extracting flow-level features is determined. Several experiments are conducted using naïve Bayes, support vector machine, k -nearest neighbor, random forest, and artificial neural networks on the University of Brescia (UNIBS) and the University of New Brunswick (UNB) datasets, which are both publicly available. The performed experiments show that 60% of flow packets are a good compromise that ensures high performance in the least processing time. The results of the conducted experiments indicate that random forest outperforms other algorithms achieving a maximum accuracy of 98.5% and an F-score of 0.932. Further, and since software-based classifiers cannot meet the anticipated real-time requirements, we propose a Field-Programmable Gate Array (FPGA) based random forest implementation that utilizes a highly pipelined architecture to accelerate such a time-consuming task. The proposed design achieves an average throughput of 163.24 Gbps, exceeding throughputs of reported hardware-based classifiers that use comparable approaches, which in turn ensures the continuity of real-time traffic classification at congested data centers.
Facing the 2020 pandemic: What does cyberbiosecurity want us to know to safeguard the future?

As the entire world is under the grip of the coronavirus disease 2019 (COVID-19), and as many are eagerly trying to explain the origins of the virus and cause of the pandemic, it is imperative to place more attention on related potential biosafety risks. Biology and biotechnology have changed dramatically during the last ten years or so. Their reliance on digitization, automation, and their cyber-overlaps have created new vulnerabilities for unintended consequences and potentials for intended exploitation that are mostly under-appreciated. This study summarizes and elaborates on these new cyberbiosecurity challenges, (1) in terms of comprehending the evolving threat landscape and determining new risk potentials, (2) in developing adequate safeguarding measures, their validation and implementation, and (3) specific critical risks and consequences, many of them unique to the life-sciences. Drawing other's expertise and my previous work, this article reviews and critically interprets our current bio-economy situation. The goal is not to attribute causative aspects of past biosafety or biosecurity events, but to highlight the fact that the bioeconomy harbors unique features that have to be more critically assessed for their potential to unintentionally cause harm to human health or environment, or to be re-tasked with an intention to cause harm. It is concluded with recommendations that will need to be considered to help ensure converging and emerging biorisk challenges, in order to minimize vulnerabilities to the life-science enterprise, public health, and national security.
A fast and secure public-key image encryption scheme based on Mordell elliptic curves

Encryption schemes based on elliptic curves (ECs) have shown high resistance against modern cryptanalysis. However, these schemes involve heavy computation for the generation of points over ECs, and therefore they are not suitable for communication systems such as sensor networks and Internet of Things. Thus, we propose a fast and secure public-key image encryption scheme. In this scheme, the sender and receiver agree on a public EC. Then the scheme performs pixel-masking and pixel-scrambling procedures using random numbers and a dynamic substitution box over ECs that are isomorphic to the public EC. For a fixed public EC, we prove that our scheme has a computational complexity that is a polylogarithmic function of the size of the plain text. In other words, the computational complexity of our scheme is independent of the point generation over ECs. We tested the security of our scheme against modern attacks by encrypting all standard images in the USC-SIPI image database. Compared to some state-of-the-art cryptosystems, our scheme is fast and has high security.
HDR Imaging From Quantization Noise

Quantization is an integral part of image acquisition but also a major performance bottleneck due to the trade-off between dynamic range and resolution. As we discuss in this paper, in contrast, quantization noise can be acquired reliably even beyond the dynamic range by re-purposing recent hardware development. In this paper, we introduce and mathematically analyze an algorithm to recover images from this information, thus giving rise to a novel, single-shot, high-dynamic-range (HDR) imaging approach. Our method directly works with a refined model for sensor outputs at the digitization stage and crucially exploits smoothing anti-aliasing artifacts. We derive recovery guarantees and demonstrate the validity of our approach via computer experiments. Our work suggests re-thinking of the imaging pipeline as seeming sensing artifacts can lead to improved reconstruction when combined with proper computational methodology.
Neurodynamic Approximation-Based Quantized Control With Improved Transient Performances for Microelectromechanical System Gyroscopes: Theory and Experimental Results

This article investigates a neurodynamic quantized control problem with improved transient performances for microelectromechanical system (MEMS) gyroscopes subject to lumped disturbances induced by parameter uncertainties, dynamic coupling, and external disturbances. First, a modified prescribed performance control mechanism based on a hyperbolic cosecant performance function is proposed to specify the transient behavior of tracking errors with an arbitrarily small overshoot. Then, to eliminate the lumped disturbances with a better identification property, a novel echo state network approximator that uses the estimation error to learn neural weights is designed with the aid of a minimal learning parameter technique, which not only can exclude the poor transient behaviors occurring extensively in the existing neural control with a large adaptive gain, but also dramatically reduce the number of parameters to be updated online. Furthermore, contrasting to the available tracking results assuming a continuous control updating, a hysteresis quantizer is introduced to provide discrete values of control signals, supporting the use of digital devices in MEMS gyroscope implementations to realize a closed-loop error stabilization. Finally, ultimately uniformly bounded stability of closed-loop system is proved with the aid of Lyapunov function and nonsmooth analysis technique, while not only simulations but also experimental results are performed to validate the effectiveness of the proposed scheme.
Abstractions made of exemplars or ‘You’re all right, and I’ve changed my mind’: Response to commentators

In this response to commentators, I agree with those who suggested that the distinction between exemplar- and abstraction-based accounts is something of a false dichotomy and therefore move to an abstractions-made-of-exemplars account under which (a) we store all the exemplars that we hear (subject to attention, decay, interference, etc.) but (b) in the service of language use, re-represent these exemplars at multiple levels of abstraction, as simulated by computational neural-network models such as BERT, ELMo and GPT-3. Whilst I maintain that traditional linguistic abstractions (e.g. a DETERMINER category; SUBJECT VERB OBJECT word order) are no more than human-readable approximations of the type of abstractions formed by both human and artificial multiple-layer networks, I express hope that the abstractions-made-of-exemplars position can point the way towards a truce in the language acquisition wars: We were all right all along, just focusing on different levels of abstraction.
Predicting developers' negative feelings about code review

During code review, developers critically examine each others' code to improve its quality, share knowledge, and ensure conformance to coding standards. In the process, developers may have negative interpersonal interactions with their peers, which can lead to frustration and stress; these negative interactions may ultimately result in developers abandoning projects. In this mixed-methods study at one company, we surveyed 1,317 developers to characterize the negative experiences and cross-referenced the results with objective data from code review logs to predict these experiences. Our results suggest that such negative experiences, which we call "pushback", are relatively rare in practice, but have negative repercussions when they occur. Our metrics can predict feelings of pushback with high recall but low precision, making them potentially appropriate for highlighting interactions that may benefit from a self-intervention.
End-to-End Learning of Compressible Features

Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf feature generators and have been shown to perform very well on a variety of tasks. Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos. Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression, while general purpose lossy compression methods based on energy compaction (e.g. PCA followed by quantization and entropy coding) are sub-optimal, as they are not tuned to task specific objective. We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features. The plug-in nature of our method makes it straight-forward to integrate with any target objective and trade-off against compressibility. We present results on multiple benchmarks and demonstrate that our method produces features that are an order of magnitude more compressible, while having a regularization effect that leads to a consistent improvement in accuracy.
Distributed Data Strategies to Support Large-Scale Data Analysis Across Geo-Distributed Data Centers

As the volume of data grows rapidly, storing big data in a single data center is no longer feasible.Hence, companies have developed two scenarios to store their big data in multiple data centers.In the first scenario, the company's big data are distributed in multiple data centers without data replication.In the second scenario, data are also stored in multiple data centers but important data are replicated in these data centers to increase data safety and availability.However, in these scenarios, analyzing big data distributed in multiple data centers becomes a challenging task.In this paper, we propose two data distribution strategies to support big data analysis across geo-distributed data centers.In these strategies, we use the recent Random Sample Partition data model to convert big data into sets of random sample data blocks and distribute these data blocks into multiple data centers either without replication or with replication.In analyzing big data in multiple data centers without replication, we randomly select samples of data blocks from multiple data centers and download the sample data blocks to one data center for analysis.In the second strategy with replication of data blocks, we can analyze big data on any data center by randomly selecting a sample of data blocks replicated from other data centers.This strategy avoids data transformation between data centers.We demonstrate the performance of the two strategies in big data analysis by using simulation results produced on one local data center and four AWS data centers in North America, Asia, and Australia.
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we learn a motion-centric representation of surgical video demonstrations by grouping them into action segments/subgoals/options in a semi-supervised manner. We present Motion2Vec, an algorithm that learns a deep embedding feature space from video observations by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while pushed away from randomly sampled images of other segments, while respecting the temporal ordering of the images. The embeddings are iteratively segmented with a recurrent neural network for a given parametrization of the embedding space after pre-training the Siamese network. We only use a small set of labeled video segments to semantically align the embedding space and assign pseudo-labels to the remaining unlabeled data by inference on the learned model parameters. We demonstrate the use of this representation to imitate surgical suturing kinematic motions from publicly available videos of the JIGSAWS dataset. Results give 85.5% segmentation accuracy on average suggesting performance improvement over several state-of-the-art baselines, while kinematic pose imitation gives 0.94 centimeter error in position per observation on the test set. Videos, code and data are available at: https://sites.google.com/view/motion2vec.
Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches

Photovoltaic monitoring data are the primary source for studying photovoltaic plant behavior. In particular, performance loss and remaining-useful-lifetime calculations rely on trustful input data. Furthermore, a regular stream of high quality is the basis for pro-active operation and management activities which ensure a smooth operation of PV plants. The raw data under investigation are electrical measurements and usually meteorological data such as in-plane irradiance and temperature. Usually, performance analyses follow a strict pattern of checking input data quality followed by the application of appropriate filter, choosing a key performance indicator and the application of certain methodologies to receive a final result. In this context, this paper focuses on four main objectives. We present common photovoltaics monitoring data quality issues, provide visual guidelines on how to detect and evaluate these, provide new data imputation approaches, and discuss common filtering approaches. Data imputation techniques for module temperature and irradiance data are discussed and compared to classical approaches. This work is intended to be a soft introduction into PV monitoring data analysis discussing best practices and issues an analyst might face. It was seen that if a sufficient amount of training data is available, multivariate adaptive regression splines yields good results for module temperature imputation while histogram-based gradient boosting regression outperforms classical approaches for in-plane irradiance transposition. Based on tested filtering procedures, it is believed that standards should be developed including relatively low irradiance thresholds together with strict power-irradiance pair filters.
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems

In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, which has a 360° coverage of stereo observations of the environment. For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation, which are faster and more accurate than the existing networks. Second, we integrate our omnidirectional depth estimates into the visual odometry (VO) and add a loop closing module for global consistency. Using the estimated depth map, we reproject keypoints onto each other view, which leads to a better and more efficient feature matching process. Finally, we fuse the omnidirectional depth maps and the estimated rig poses into the truncated signed distance function (TSDF) volume to acquire a 3D map. We evaluate our method on synthetic datasets with ground-truth and real-world sequences of challenging environments, and the extensive experiments show that the proposed system generates excellent reconstruction results in both synthetic and real-world environments.
A BERT-based Approach with Relation-aware Attention for Knowledge Base Question Answering

Knowledge Base Question Answering (KBQA), which uses the facts in the knowledge base (KB) to answer natural language questions, has received extensive attention in recent years. The existing works mainly focus on the modeling method and neglect the relations between questions and KB facts, which might restrict the further improvements of the performance. To address this problem, this paper proposes a BERT-based approach for single-relation question answering (SR-QA), which consists of two models, entity linking and relation detection. For entity linking, we adopt pre-trained BERT and a heuristic algorithm to reduce the noise in the candidate facts. For relation detection, the existing approaches usually model the question and the candidate fact respectively before calculate their semantic similarity, which might lose part of the original interaction information between them. To work around this problem, a BERT-based model with relation-aware attention is proposed. We construct the question-fact pair as the input of pre-trained BERT to preserves the original interactive information. To bridge the semantic gap between the questions and the KB facts, we also use a relation-aware attention network to enhance the representation of candidates. The experimental results show that our entity linking model achieves new state-of-the-art results and our complete approach also achieves state-of-the-art accuracy of 80.9% on the SimpleQuestions dataset.
Proposal-Based Instance Segmentation With Point Supervision

Instance segmentation methods often require costly per-pixel labels. We propose a method called WISE-Net that only requires point-level annotations. During training, the model only has access to a single pixel label per object, yet the task is to output full segmentation masks. To address this challenge, we construct a network with two branches: (1) a 10-calization network (L-Net) that predicts the location of each object; and (2) an embedding network (E-Net) that learns an embedding space where pixels of the same object are close. The segmentation masks for the located objects are obtained by grouping pixels with similar embeddings. We evaluate our approach on PASCAL VOC, COCO, KITTI and CityScapes datasets. The experiments show that our method (1) obtains competitive results compared to fully-supervised methods in certain scenarios; (2) outperforms fully-and weakly-supervised methods with a fixed annotation budget; and (3) establishes a first strong baseline for instance segmentation with point-level supervision.
3D Face Reconstruction From Single 2D Image Using Distinctive Features

3D face reconstruction is considered to be a useful computer vision tool, though it is difficult to build. This paper proposes a 3D face reconstruction method, which is easy to implement and computationally efficient. It takes a single 2D image as input, and gives 3D reconstructed images as output. Our method primarily consists of three main steps: feature extraction, depth calculation, and creation of a 3D image from the processed image using a Basel face model (BFM). First, the features of a single 2D image are extracted using a two-step process. Before distinctive-features extraction, a face must be detected to confirm whether one is present in the input image or not. For this purpose, facial features like eyes, nose, and mouth are extracted. Then, distinctive features are mined by using scale-invariant feature transform (SIFT), which will be used for 3D face reconstruction at a later stage. Second step comprises of depth calculation, to assign the image a third dimension. Multivariate Gaussian distribution helps to find the third dimension, which is further tuned using shading cues that are obtained by the shape from shading (SFS) technique. Thirdly, the data obtained from the above two steps will be used to create a 3D image using BFM. The proposed method does not rely on multiple images, lightening the computation burden. Experiments were carried out on different 2D images to validate the proposed method and compared its performance to those of the latest approaches. Experiment results demonstrate that the proposed method is time efficient and robust in nature, and it outperformed all of the tested methods in terms of detail recovery and accuracy.
Transferable Task Execution from Pixels through Deep Planning Domain Learning

While robots can learn models to solve many manipulation tasks from raw visual input, they cannot usually use these models to solve new problems. On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal, but these approaches often struggle on the real world robotic tasks due to the challenges of grounding these symbols from sensor data in a partially-observable world. We propose Deep Planning Domain Learning (DPDL), an approach that combines the strengths of both methods to learn a hierarchical model. DPDL learns a high-level model which predicts values for a large set of logical predicates consisting of the current symbolic world state, and separately learns a low-level policy which translates symbolic operators into executable actions on the robot. This allows us to perform complex, multistep tasks even when the robot has not been explicitly trained on them. We show our method on manipulation tasks in a photorealistic kitchen scenario.
StereoEngine: An FPGA-Based Accelerator for Real-Time High-Quality Stereo Estimation With Binary Neural Network

Stereo estimation is essential to many applications such as mobile autonomous robots, most of which ask for real-time response, high energy, and storage efficiency. Deep neural networks (DNNs) have shown to yield significant gains in improving accuracy. However, these DNN-based algorithms are challenging to be deployed on energy and resource-constrained devices due to the high computational complexities of DNNs. In this article, we present StereoEngine, a fully pipelined end-to-end stereo vision accelerator that computes accurate dense depth in a real-time and energy-efficient manner. An efficient stereo algorithm is developed and optimized for a high-quality hardware-friendly implementation, that leverages binary neural network (BNN) to learn discriminative binary descriptors to improve the disparity. The design of StereoEngine is a standalone DNN-based stereo vision system where all processing procedures are implemented on a hardware platform. The effectiveness of StereoEngine is evaluated by comprehensive experiments. Compared with software-based implementations on the highend and embedded Nvidia GPUs, StereoEngine achieves up to 3×, 13×, and 50× speedups, as well as up to 211×, 58×, and 73× energy efficiency improvement, respectively. Furthermore, StereoEngine achieves leading accuracy when compared to state-of-the-art hardware implementations on the challenging KITTI dataset.