Reviewerly/exHarmony huggingface dataset:
Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches

Machine Learning (ML) based Network Intrusion Systems (NIDSs) operate on flow features which are obtained from flow exporting protocols (<i>i.e.,</i> NetFlow). Recent success of ML and Deep Learning (DL) based NIDS solutions assume such flow information (<i>e.g.,</i> avg. packet size) is obtained from all packets of the flow. However, often in practice flow exporter is deployed on commodity devices where packet sampling is inevitable. As a result, applicability of such ML based NIDS solutions in the presence of sampling (<i>i.e.,</i> when flow information is obtained from sampled set of packets instead of full traffic) is an open question. In this study, we explore the impact of packet sampling on the performance and efficiency of ML-based NIDSs. Unlike previous work, our proposed evaluation procedure is immune to different settings of flow export stage. Hence, it can provide a robust evaluation of NIDS even in the presence of sampling. Through sampling experiments we established that malicious flows with shorter size (<i>i.e.,</i> number of packets) are likely to go unnoticed even with mild sampling rates such as 1/10 and 1/100. Next, using the proposed evaluation procedure we investigated the impact of various sampling techniques on NIDS detection rate and false alarm rate. Detection rate and false alarm rate is computed for three sampling rates (<i>i.e.,</i> 1/10, 1/100, 1/1000), for four different sampling techniques and for three (two tree-based, one deep learning based) classifiers. Experimental results show that systematic linear sampler - SketFlow performs better compared to non-linear samplers such as Sketch Guided and Fast Filtered sampling. We also found that random forest classifier with SketchFlow sampling was a better combination. The combination showed higher detection rate and lower false alarm rate across multiple sampling rates compared to other sampler-classifier combinations. Our results are consistent in multiple sampling rates, exceptional case is observed for Sketch Guided Sampling (SGS) as it caused a drastic performance drop when sampling rate was changed from 1/100 to 1/1000. Our results provide valuable insights for network practitioners and researchers regarding on how packet sampling effects ML-based NIDS performance. In this regard full source code for sampling and ML experiments has been released: github.com/Jumabek/sampledFlowMeter and github.com/Jumabek/nids-with-sampling
Latent Feature Pyramid Network for Object Detection

Object detection methods based on Convolution Neural Networks (CNN) usually utilize feature pyramid networks to detect objects with various scales. The state-of-the-art feature pyramid networks improve detection accuracy by enhancing multi-level feature representations. Fusing multi-level features is the most effective manner to enhance the feature representations. However, the existing feature pyramid networks usually fuse multi-level features by element-wise operations. It leads to the lack of long-range dependencies in the feature fusion. To address the problem, we propose a simple yet efficient feature pyramid network named latent feature pyramid network (LFPN). LFPN can enhance the feature representations by modeling inner-scale and cross-scale long-range dependencies through conducting inner-scale and cross-scale feature fusion in the latent space. Comprehensive experiments are performed on two challenge object detection datasets: MS COCO and Pascal VOC. The experimental results show consistent improvements on various feature pyramid networks, backbones, and object detectors, which demonstrates the effectiveness and generality of our LFPN.
OQFL: An Optimized Quantum-Based Federated Learning Framework for Defending Against Adversarial Attacks in Intelligent Transportation Systems

Intelligent transportation systems, especially Autonomous Vehicles (AVs), are emerging as a paradigm with the potential to change modern society. However, with this, there is a strong need to ensure the security and privacy of such systems. AV ecosystems depend on machine learning algorithms to autonomously control their operations. Given the amount of personal information AVs collect, coupled with the distributed nature of such ecosystems, there is a movement to employ federated learning algorithms to develop secure decision-making models. Although federated learning is a viable candidate for data privacy, it is vulnerable to adversarial attacks, particularly data poisoning attacks, where malicious vectors would be injected in the training phase. Additionally, hyperparameters play an important role in establishing an efficient federated learning model that can be resilient against adversarial attacks. In this paper, to address these challenges, we propose a novel Optimized Quantum-based Federated Learning (OQFL) framework to automatically adjust the hyperparameters of federated learning using various adversarial attacks in AV settings. This work is innovative in two ways: first, a quantum-behaved particle swarm optimization technique is used to update the hyperparameters of the learning rate, local and global epochs. Second, the proposed technique is utilized within a cyber defense framework to defend against adversarial attacks. The performance of the proposed framework was evaluated using two benchmark datasets: MINST and Fashion-MINST, where they include images that would be extracted from smart cameras of AVs. This framework is shown to be more resilient against various adversarial attacks compared with peer techniques.
Sign Language Recognition and Translation Method based on VTN

Sign language recognition plays an important role in real-time sign language translation, communication for deaf people, education and human-computer interaction. However, vision-based sign language recognition faces difficulties such as insufficient data, huge network models and poor timeliness. We use VTN (Video Transformer Net) to construct a lightweight sign language translation network. We construct the dataset called CSL_BS (Chinese Sign Language-Bank and Station) and two-way VTN to train isolated sign language and compares it with I3D (Inflated three Dimension). Then I3D and VTN are respectively used as feature extraction modules to extract the features of continuous sign language sequences, which are used as the input of the continuous sign language translation decoding network (seq2seq). Based on CSL-BS, two-way VTN achieves 87.9% accuracy while two-way I3D is 84.2%. And the recognition speed is increased by 46.8%. In respect of continuous sign language translation, the accuracy of VTN_seq2seq is 73.5% while I3D_seq2seq is 71.2%, the recognition speed is 13.91s and 26.54s respectively.
Multivehicle Object Tracking in Satellite Video Enhanced by Slow Features and Motion Features

With the development of video satellites, multimoving object tracking in satellite video is possible and has become a new challenging task. The difficulties are mainly caused by the characteristics of satellite videos: 1) small objects; 2) low contrast between objects and background; and 3) background in a state of continuous motion. These characteristics make it difficult for the advanced multiobject tracking algorithms in the natural video to give full play to their advantages, resulting in vast false alarms, missed objects, ID switches, and low-confidence bounding boxes. To tackle these problems, a novel multimoving object tracking method considering slow features (SFs) and motion features has been proposed in this research, named SF and motion feature-guided multiobject tracking (SFMFMOT), which realizes the continuous tracking of moving vehicles in satellite videos. A nonmaximum suppression (NMS) module guided by bounding box proposals based on SFs is designed to assist the object detection part by utilizing the sensitivity of SF analysis to the changed pixels. While removing a large number of static false alarms and supplementing missed objects, it improves the recall rate by increasing the confidence score of the correctly detected object bounding boxes. In order to improve the tracking performance, a set of optimization strategies based on motion features and time accumulation information are proposed to smooth the trajectory, remove static false alarms, and duplicate bounding boxes. The proposed method is evaluated in three satellite videos and its superiority is demonstrated.
Hierarchical pyramid attentive network with spatial separable convolution for crowd counting

To tackle the challenging scale variation issue of the crowd counting task so as to improve the counting accuracy, we present a novel method based on Hierarchical Pyramid Attentive Network (HPANet) for crowd counting. Specifically, a Scale-aware Pyramid Attentive (SPA) block, extracting the rich multi-scale context, is designed elaborately as using the two-branch spatial separable convolution as its core component to replace the conventional pure convolution with larger kernel size to reduce the computation, as well as adopting a self-attention operation for the spatial feature aggregation. In order to further learn the scale-aware feature representation well from the input image, we stack the designed SPA block in a hierarchical way and fuse their features flexibly as another crucial module of the proposed HPANet, the Hierarchical Feature Fusion (HFF) module. Combining the designed SPA block and HFF module, the developed HPANet could remedy the scale variation issue and thus improve the counting performance with the mighty scale-aware feature representation. The performance of the HPANet is evaluated on four public available benchmark datasets in this paper, including ShanghaiTech, Mall, Beijing BRT and UCF-QNRF. Extensive experimental results on benchmarks demonstrate that the proposed HPANet could have an effective performance for crowd counting and the ablation experimental results validate the effectiveness of the components of HPANet on the counting task. The designed HPANet could realize a preferable counting performance in view of alleviating the scale variation issue, without the cost of introducing too much additional parameters for the multi-column structure.
Pathologic liver tumor detection using feature aligned multi-scale convolutional network

The detection of the most common type of liver tumor, that is, hepatocellular carcinoma (HCC), is one essential step to liver pathology image analysis. In liver tissue, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign tissue. Hence, the detection of HCC may fail when the patches covered only limited tissue region without enough neighboring cell structure information. To address this problem, a Feature Aligned Multi-Scale Convolutional Network (FA-MSCN) architecture is proposed in this paper for automatic liver tumor detection based on whole slide images (WSI). The proposed network integrates the features obtained at different magnification levels to improve the detection performance by referencing more neighboring information. The FA-MSCN consists of two parallel convolutional networks in which one would extract high-resolution features and the other would extract low-resolution features by atrous convolution. The low-resolution features then go through central cropping, upsampling, and concatenation with high-resolution features for final classification. The experimental results demonstrated that Multi-Scale Convolutional Network (MSCN) improves the detection performance compared to Single-Scale Convolutional Network (SSCN), and that the FA-MSCN is superior to both SSCN and MSCN, demonstrating on HCC detection.
Explanations for Neural Networks by Neural Networks

Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming. Therefore, these approaches are not applicable in scenarios where timely or frequent explanations are required. In this paper, we introduce a real-time approach for generating a symbolic representation of the function learned by a neural network. Our idea is to generate explanations via another neural network (called the Interpretation Network, or I-Net), which maps network parameters to a symbolic representation of the network function. We show that the training of an I-Net for a family of functions can be performed up-front and subsequent generation of an explanation only requires querying the I-Net once, which is computationally very efficient and does not require training data. We empirically evaluate our approach for the case of low-order polynomials as explanations, and show that it achieves competitive results for various data and function complexities. To the best of our knowledge, this is the first approach that attempts to learn mapping from neural networks to symbolic representations.
The role of knowledge economy in Asian business

Abstract The study examines the role of the knowledge economy (KE) in Asian businesses in 45 countries for 2000–2019. KE indicators include education, economic incentives, innovation, institutional regime, and information and communication technology. The business indicators used in the study are starting, doing, and closing business. The empirical analysis is carried out by applying principal component analysis (PCA) and instrument variable panel fixed effects estimator. The results proved that the KE indicators are essential to improve businesses in Asia. They help the economies to boost their business sector and help to fight against poverty and unemployment.
A Detailed Review on Text Extraction Using Optical Character Recognition

There exist businesses and applications that involve huge amount of data generated be it in any form to be processed & stored on daily basis. It is an implicit requirement to be able to carry out quick search through this enormous data in order to deal with the high amount of document and data generated. Documents are being digitized in all possible fields as collecting the required data from these documents manually is very time consuming as well as a tedious task. We have been able to save a huge amount of efforts in creating, processing, and saving scanned documents using OCR. It proves to be very efficient due to its use in variety of applications in Healthcare, Education, Banking, Insurance industries, etc. There exists sufficient researches and papers that describe the methods for converting the data residing in the documents into machine readable form. This paper describes a detailed overview of general extraction methods from different types of documents with different forms of data and in addition to this, we have also illustrated on various OCR platforms. The current study is expected to advance OCR research, providing better understanding and assist researchers to determine which method is ideal for OCR.
TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes

Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks.Serverless applications based on this model are often composed of several fine-grained and ephemeral Function-as-a-Service (FaaS) functions that implement complex business processes via mutual interaction and interaction with Backend-as-a-Services (BaaS) such as databases.FaaS functions suffer from the cold start problem because of the scale to zero instances feature.In this work, we use neural Temporal Point Processes (TPPs) to model function invocations in FaaS compositions.A probability distribution over the time and class of the following invocations for a given history of invocations is predicted using these probabilistic models.The prediction can avoid cold starts by scaling functions in advance and reduce network load by optimizing the function-server assignment.In this regard, we developed a python-based tool called TppFaaS on top of OpenWhisk open-source serverless platform.TppFaaS uses the neural TPPs LogNormMix for modeling the time using a log-normal mixture distribution and TruncNorm for predicting a single value for the time.Furthermore, we built a custom trace data collector for OpenWhisk embedded into TppFaaS and created datasets for multiple FaaS compositions to train and test our models.For datasets without cold starts, the models achieved for most compositions a mean absolute error below 22ms and a percentage of correctly predicted function classes above 94%.
Kiro

Soon, voice assistants might be able to engage in fully-fledged social conversations with people, rather than merely providing a voice-operated interface to functionality and data. So far, not much is known about designing such "social" voice assistants and the potential social experiences, which could and should emerge in everyday situations. In the present paper, we created a design fiction to explore a sophisticated social voice assistant in the context of the car. Based on models from psychology and psychotherapy, we designed the fictional "virtual passenger" Kiro. We created a website for Kiro (http://www.heykiro.com/), distributed it, and collected responses in various ways (e.g., comments). We further ran a market research-type focus group. In general, we found people to accept Kiro as a conversation partner but not as a replacement for human-human conversations. We suggest designing social voice assistants in a way to enable novel types of socially fulfilling, yet distinct human-machine conversations.
Privacy-preserving and verifiable deep learning inference based on secret sharing

Deep learning inference, providing the model utilization of deep learning, is usually deployed as a cloud-based framework for the resource-constrained client. However, the existing cloud-based frameworks suffer from severe information leakage or lead to significant increase of communication cost. In this work, we address the problem of privacy-preserving deep learning inference in a way that both the privacy of the input data and the model parameters can be protected with low communication and computational costs. Additionally, the user can verify the correctness of results with small overhead, which is very important for critical application. Specifically, by designing secure sub-protocols, we introduce a new layer to collaboratively perform the secure computations involved in the inference. With the cooperation of the secret sharing, we inject the verifiable data into the input, enabling us to check the correctness of the returned inference results. Theoretical analyses and extensive experimental results over MNIST and CIFAR10 datasets are provided to validate the superiority of our proposed privacy-preserving and verifiable deep learning inference (PVDLI) framework.
Is Historical Data an Appropriate Benchmark for Reviewer Recommendation Systems? : A Case Study of the Gerrit Community

Reviewer recommendation systems are used to suggest community members to review change requests. Like several other recommendation systems, it is customary to evaluate recommendations using held out historical data. While history-based evaluation makes pragmatic use of available data, historical records may be: (1) overly optimistic, since past assignees may have been suboptimal choices for the task at hand; or (2) overly pessimistic, since "incorrect" recommendations may have been equal (or even better) choices.In this paper, we empirically evaluate the extent to which historical data is an appropriate benchmark for reviewer recommendation systems. We replicate the CHREV and WLRREC approaches and apply them to 9,679 reviews from the GERRIT open source community. We then assess the recommendations with members of the GERRIT reviewing community using quantitative methods (personalized questionnaires about their comfort level with tasks) and qualitative methods (semi-structured interviews).We find that history-based evaluation is far more pessimistic than optimistic in the context of GERRIT review recommendations. Indeed, while 86% of those who had been assigned to a review in the past felt comfortable handling the review, 74% of those labelled as incorrect recommendations also felt that they would have been comfortable reviewing the changes. This indicates that, on the one hand, when reviewer recommendation systems recommend the past assignee, they should indeed be considered correct. Yet, on the other hand, recommendations labelled as incorrect because they do not match the past assignee may have been correct as well.Our results suggest that current reviewer recommendation evaluations do not always model the reality of software development. Future studies may benefit from looking beyond repository data to gain a clearer understanding of the practical value of proposed recommendations.
Diverse Distractor Generation for Constructing High-Quality Multiple Choice Questions

Distractor generation task aims to generate incorrect options (i.e., distractors) for multiple choice questions from an article.Existing methods for this task often utilize a standard encoder-decoder framework. However, these methods often tend to generate semantically similar distractors, since the same article representations are used to generate different distractors. Multiple generated distractors with similar semantics are considered equivalent. Because the correct answer is unique, students can eliminate these distractors even without reading the article. In this paper, we propose a multi-selector generation network (MSG-Net) that generates distractors with rich semantics based on different sentences in an article. MSG-Net adopts a multi-selector mechanism to select multiple different sentences in an article that are useful to generate diverse distractors. Specifically, a question-aware and answer-aware mechanism are introduced to assist in selecting useful key sentences, where each key sentence is coherent with the question and not equivalent to the answer. MSG-Net can generate diverse distractors based on each selected key sentence with different semantics. Extensive experiments on the RACE dataset and Cosmos QA dataset show that the proposed model outperforms the state-of-the-art models in generating diverse distractors.
4D line-scan hyperspectral imaging

This paper proposes a 4D line-scan hyperspectral imager that combines 3D geometrical measurement and spectral detection with high spectral resolution and spatial accuracy. We investigated the geometrical optical model of a camera attaching with a spectrograph, theoretically explored the mathematical model for line-scan fringe projection profilometry, and established the 3D reconstruction and calibration methods under this proposed line-scan high-dimensional imaging system. The spectral resolution of the system is 2.8 nm, and the spatial root-mean-square-error is 0.0895 mm when measuring a standard sphere with a diameter of 40.234 mm. We measure a colored statue to showcase the intensity change along the dimension of wavelength. In addition, the quality and defect of the spinach leaves are inspected based on spectral data and depth data, which demonstrates the potential application of the system in the food industry.