Reviewerly/exHarmony huggingface dataset:
Lightweight Yolov4 Target Detection Algorithm Fused with ECA Mechanism

For the task of garbage classification, to overcome the main disadvantages of the Yolov4 target detection algorithm, such as the large network model and lower detection accuracy for small objects, a lightweight Yolov4 target detection network based on the EfficientNet-B0 fusion ECA mechanism is presented. The lightweight EfficientNet was used to replace the original backbone network, which reduces the parameters of the network model and improves the detection accuracy. Moreover, a deep detachable convolution block replaced the common convolution block in the original network, which further reduced the number of parameters in the model. In the feature pyramid model PANet, a lightweight ECA attention mechanism was introduced to realize the weight analysis of the importance of different channel feature maps through cross-channel interaction, allowing the network to extract more obvious features with which to distinguish categories. Finally, a Soft-NMS algorithm was introduced in the post-processing stage of the detection frame to reduce the missed target detection rate in dense areas, which can improve the detection accuracy of the network and detection efficiency. As shown in the results, the size of the model was only 48 MB, and the mAP was 91.09%. Compared with the original Yolov4 network, the mAP was increased by 5.77% based on the 80% reduction in the model size. The recognition of small targets was also improved, which proved the effectiveness and robustness of the improved algorithm.
Blockchain-based Secure Client Selection in Federated Learning

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.
DNN-SAM: Split-and-Merge DNN Execution for Real-Time Object Detection

As real-time object detection systems, such as autonomous cars, need to process input images acquired from multiple cameras, they face significant challenges in delivering accurate and timely inferences often based on machine learning (ML). To meet these challenges, we want to provide different levels of object detection accuracy and timeliness to different portions within each input image with different criticality levels. Specifically, we develop DNN-SAM, a dynamic Split-And-Merge Deep Neural Network (DNN) execution and scheduling framework, that enables seamless split-and-merge DNN execution for unmodified DNN models. Instead of processing an entire input image once in a full DNN model, DNN-SAM first splits a DNN inference task into two smaller sub-tasks-a mandatory sub-task dedicated for a safety-critical (cropped) portion of each image and an optional sub-task for processing a down-scaled image–then executes them independently, and finally merges their results into a complete inference. To achieve DNN-SAM’s timely and accurate detection of objects in each image, we also develop two scheduling algorithms that prioritize sub-tasks according to their criticality levels and adaptively adjust the scale of the input image to meet the timing constraints while minimizing the response time of mandatory sub-tasks or maximizing the accuracy of optional sub-tasks. We have implemented and evaluated DNN-SAM on a representative ML framework. Our evaluation shows DNN-SAM to improve detection accuracy in the safety-critical region by $2.0-3.7\times$ and lower average inference latency by $4.8-9.7\times$ over existing approaches without violating any timing constraints.
Strategic Prototyping Technology Adoption in Startups: Framework, Challenges, and Opportunities

Startups use prototyping to develop and validate their products and business models. For startups, it is important to have a good knowledge of relevant adoption factors, to make decisions regarding which prototyping technology to adopt. In this article, we identify such factors based on our practical experiences of startups in various industries. We define a research framework, describe challenges affecting the factors, and present opportunities for startups to improve their adoption process.
Human action prediction in collaborative environments based on shared-weight LSTMs with feature dimensionality reduction

As robots are progressing towards being ubiquitous and an indispensable part of our everyday environments, such as home, offices, healthcare, education, and manufacturing shop floors, efficient and safe collaboration and cohabitation become imperative. Given that, such environments could benefit greatly from accurate human action prediction. In addition to being accurate, human action prediction should be computationally efficient, in order to ensure a timely reaction, and capable of dealing with changing environments, since unstructured interaction and collaboration with humans usually do not assume static conditions. In this paper, we propose a model for human action prediction based on motion cues and gaze using shared-weight Long Short-Term Memory networks (LSTMs) and feature dimensionality reduction. LSTMs have proven to be a powerful tool in processing time series data, especially when dealing with long-term dependencies; however, to maximize their performance, LSTM networks should be fed with informative and quality inputs. Given that, in this paper, we furthermore conducted an extensive input feature analysis based on (i) signal correlation and their strength to act as stand-alone predictors, and (ii) a multilayer perceptron inspired by the autoencoder architecture. We validated the proposed model on a publicly available MoGaze1 dataset for human action prediction, as well as on a smaller dataset recorded in our laboratory. Our model outperformed alternatives, such as recurrent neural networks, a fully connected LSTM network, and the strongest stand-alone signals (baselines), and can run in real-time on a standard laptop CPU. Since eye gaze might not always be available in a real-world scenario, we have implemented and tested a multi-layer perceptron for gaze estimation from more easily obtainable motion cues, such as head orientation and hand position. The estimated gaze signal can be utilized during inference of our LSTM-based model, thus making our action prediction pipeline suitable for real-time practical applications.
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a single domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of plausible distributions so that unseen domains can be interpolated from synthetic domains. We propose a mutual information regularizer to enforce the semantic consistency between images from the synthetic domains, which can be estimated by patch-level contrastive learning. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.
Fast and robust two-frame random phase-shifting interferometry without pre-filtering

To obtain higher phase accuracy with less computation time in phase-shifting interferometry, a random phase-shifting algorithm based on principal component analysis and least squares iteration (PCA&LSI) is proposed. The algorithm does not require pre-filtering, and only requires two-frame phase-shifted interferograms and less computation time to obtain a relatively accurate phase distribution. This method can still extract the phase with high precision when there are few fringes in the interferogram. Moreover, it eliminates the limitation that the PCA algorithm needs more than three frames of interferograms with uniform phase shift distribution to accurately extract the phase. Numerical simulations and experiments confirm that the method is suitable for complex situations with different fluctuations in background intensity and modulation amplitude. And it can still achieve accurate phase extraction compared with other methods under different noise conditions.
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unseen columns or tables when generating SQLs. In this work, we propose a novel framework to elicit relational structures from large-scale pre-trained language models (PLMs) via a probing procedure based on Poincaré distance metric, and use the induced relations to augment current graph-based parsers for better schema linking. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences, even when surface forms of mentions and entities differ. Moreover, our probing procedure is entirely unsupervised and requires no additional parameters. Extensive experiments show that our framework sets new state-of-the-art performance on three benchmarks. We empirically verify that our probing procedure can indeed find desired relational structures through qualitative analysis.
Distributed multi-camera multi-target association for real-time tracking

Abstract Tracking and associating different views of the same target across moving cameras is challenging as its appearance, pose and scale may vary greatly. Moreover, with multiple targets a management module is needed for new targets entering and old targets exiting the field of view of each camera. To address these challenges, we propose DMMA, a Distributed Multi-camera Multi-target Association for real-time tracking that employs a target management module coupled with a local data-structure containing the information on the targets. The target management module shares appearance and label information for each known target for inter-camera association. DMMA is designed as a distributed target association that allows a camera to join at any time, does not require cross-camera calibration, and can deal with target appearance and disappearance. The various parts of DMMA are validated using benchmark datasets and evaluation criteria. Moreover, we introduce a new mobile-camera dataset comprising six different scenes with moving cameras and objects, where DMMA achieves 92% MCTA on average. Experimental results show that the proposed tracker achieves a good association accuracy and speed trade-off by working at 32 frames per second (fps) with high definition (HD) videos.
A Moderation Framework for the Swift and Transparent Removal of Illicit Blockchain Content

Blockchains gained tremendous attention for their capability to provide immutable and decentralized event ledgers that can facilitate interactions between mutually distrusting parties. However, precisely this immutability and the openness of permissionless blockchains raised concerns about the consequences of illicit content being irreversibly stored on them. Related work coined the notion of redactable blockchains, which allow for removing illicit content from their history without affecting the blockchain's integrity. While honest users can safely prune identified content, current approaches either create trust issues by empowering fixed third parties to rewrite history, cannot react quickly to reported content due to using lengthy public votings, or create large per-redaction overheads.In this paper, we instead propose to outsource redactions to small and periodically exchanged juries, whose members can only jointly redact transactions using chameleon hash functions and threshold cryptography. Multiple juries are active at the same time to swiftly redact reported content. They oversee their activities via a global redaction log, which provides transparency and allows for appealing and reversing a rogue jury's decisions. Hence, our approach establishes a framework for the swift and transparent moderation of blockchain content. Our evaluation shows that our moderation scheme can be realized with feasible per-block and per-redaction overheads, i.e., the redaction capabilities do not impede the blockchain's normal operation.
Structured graph optimization for joint spectral embedding and clustering

Spectral Clustering (SC) is an important method in areas such as data mining, image processing, computer science and so on. It attracts more and more attention owing to its effectiveness in unsupervised learning. However, SC has poor performance in the high-dimensional data. Traditional SC methods conduct the spectral embedding of the affinity matrix among data at the first beginning, and then obtain clustering results by the K-means clustering. The also have drawbacks int two processing steps: the clustering results are sensitive to the affinity matrix which may be inaccurate and the post-processing K-means may also be limited by its initialization problem. In the paper, a new approach which joints spectral embedding and clustering with structured graph optimization (called JSEGO) is proposed. In the new model, the low-dimensional representation of data can first be obtained by the spectral embedding method, which can handle with the high-dimensional data better. Then, the optimization similarity matrix would be obtained with such the embedded data. Furthermore, the learning structure graph gives feedback to the similarity matrix to generate better spectral embedded data. As a result, better similarity matrix and clustering result can be obtained by the iterations simultaneously, which are often conducted in two separate steps in the spectral clustering. As a result, the drawbacks introduced by the two processing introduces can be solved. At last, we use an alternative optimization method in the new model and conduct the theoretical analysis by comparing this proposed method with K-means clustering. Experiments based on synthetic data and actual benchmark data prove the advantage of this new approach.
MRRNet: Learning multiple region representation for video person re-identification

Video person re-identification is a crucial component of a robust surveillance system. Within a video clip, different human regions exhibit unique stability characteristics, which would be harmful to generating a discriminative representation. Unfortunately, prior works cannot effectively deal with the stability characteristics of different regions. To tackle this problem, we propose a Multiple Region Representation Network (MRRNet) that aims to discover the discriminative information from different human regions. Firstly, a Stable Region Representation (SRR) layer is proposed to capture important clues from the stable regions and exchange temporal information by cross-relation aware operation. Secondly, a Multiple Region Representation (MRR) layer is designed to address the unstable regions and preserve the attention on stable regions. Thirdly, SRR and MRR can be conveniently inserted into multiple stages of the deep residual networks and significantly improve the performance of the network. Comprehensive experiments validate the effectiveness of our network. Particularly, MRRNet achieves 86.7% mAP and 91.1% Rank-1 accuracy on the MARS dataset, which outperforms state-of-the-arts.
A new approach for malaria diagnosis in thick blood smear images

This paper presents a new approach for detecting malaria parasites in full images of thick blood smear using pixel classifiers for obtaining foreground objects and delimiting parasite-stained objects. For both processes, the HSV components were used as input variables of the following pixel classifiers: multilayer perceptron and a decision tree. The obtained patches were classified using a deep neural network with 34 layers, trained from scratch. The image dataset used was divided into sets with different parasite sizes. This enables characterizing performance metrics (accuracy, sensitivity, specificity, precision, and F1-score) for parasite detection with varying parasite sizes. The best metric values were obtained in images with large parasite sizes. For image sets 1 and 2, with large parasite sizes, precision rates of 91.71% and 93.14% were obtained. For image sets 3 and 4, with small parasite sizes, precision rates of 76.58% and 71.58% were obtained. As shown by the literature review, these results are comparable to others previously published. Nevertheless, a rigorous comparison could not be done, as different works use different datasets.
Security Versus Performance Bugs: How Bugs are Handled in the Chromium Project

Bug fixing is a very important activity of software maintenance. Given the recent highlight on security and privacy, one may expect that the software vendors would give security bugs a higher priority in their bug fixing process. In this paper, we present an exploratory study of different categories (i.e., security, performance, and other) of bugs in the maintenance of the Chromium browser. In particular, we study the phenomena such as how much time is spent in bug triage, how fast different types of bugs are fixed, variations of developers' experiences who fix those bugs, and show often those fixed bugs are reopened. We find that the performance bugs are triaged and fixed faster. Security bugs, for fixing, are assigned to more experienced developers. All categories of bugs are almost equally reopened once closed.
Comparison of Performance of Machine Learning Algorithms for Cervical Cancer Classification

Cervical cancer, which is the fourth leading cause of mortality among women, displays no symptoms in its early stages. Cervical cancer is currently diagnosed using only a few approaches using Machine Learning techniques. Certain approaches such as PAP Test, HPV Test, Colposcopy and Biopsy require medical staff intervention and cancer is not detected until a certain stage is reached. These procedures are also too costly in developing countries. Detection of Cervical Cancer using Machine Learning and Deep Learning techniques come into play to solve this issue. A few to name are: CervDetect <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1],</sup> a hybridized model using a combination of Random Forest and Shallow Neural Networks, ResNet50 – A Convolutional Neural Network’s pre-trained model works effectively on classification of cervical cancer cells using images. This research paper experiments and analyses two Support Vector Machine (SVM) techniques as well as K-Nearest Neighbor (KNN), Random Forest(RF), Logistic Regression and Gaussian Naïve Bayes (GNB) algorithms for cervical cancer diagnosis. The dataset used is Cervical cancer (Risk Factors) Data Set from UCI Repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> . There are 32 risk factors and four target variables in cervical cancer dataset: Citology, Hinselmann, Schiller and Biopsy. The two SVM-based techniques namely SVM Linear and SVM Radial, KNN, RF, Logistic Regression and GNB have diagnosed and categorized all four targets respectively. Following that, a comparison between these six methods is done and inferences are drawn on which algorithm performs better on each of the targets.
SpeedyIBL: A comprehensive, precise, and fast implementation of instance-based learning theory

Instance-based learning theory (IBLT) is a comprehensive account of how humans make decisions from experience during dynamic tasks. Since it was first proposed almost two decades ago, multiple computational models have been constructed based on IBLT (i.e., IBL models). These models have been demonstrated to be very successful in explaining and predicting human decisions in multiple decision-making contexts. However, as IBLT has evolved, the initial description of the theory has become less precise, and it is unclear how its demonstration can be expanded to more complex, dynamic, and multi-agent environments. This paper presents an updated version of the current theoretical components of IBLT in a comprehensive and precise form. It also provides an advanced implementation of the full set of theoretical mechanisms, SpeedyIBL, to unlock the capabilities of IBLT to handle a diverse taxonomy of individual and multi-agent decision-making problems. SpeedyIBL addresses a practical computational issue in past implementations of IBL models, the curse of exponential growth, that emerges from memory-based tabular computations. When more observations accumulate over time, there is an exponential growth of the memory of instances that leads directly to an exponential slowdown of the computational time. Thus, SpeedyIBL leverages parallel computation with vectorization to speed up the execution time of IBL models. We evaluate the robustness of SpeedyIBL over an existing implementation of IBLT in decision games of increased complexity. The results not only demonstrate the applicability of IBLT through a wide range of decision-making tasks, but also highlight the improvement of SpeedyIBL over its prior implementation as the complexity of decision features the of agents increase. The library is open sourced for the use of the broad research community.
Reverse image filtering with clean and noisy filters

Abstract Given an image filter $${{\varvec{y}}}={{\varvec{f}}}\,({{\varvec{x}}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mspace /> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , where $${{\varvec{x}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:math> and $${{\varvec{y}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:math> are input and output images, respectively, reverse image filtering consists of rendering an approximation to $${{\varvec{x}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:math> from $${{\varvec{y}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:math> using the filter $${{\varvec{f}}}\,(\cdot )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mspace /> <mml:mo>(</mml:mo> <mml:mo>·</mml:mo> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> itself as a black box, without knowing the internal structure of the filter. In this paper, we propose to use modified Landweber iterations for reverse image filtering, evaluate the performance of our approach, and present applications to image deblurring and super-resolution. An important advantage of our approach over the existing reverse image filtering methods is high robustness to noise.