Reviewerly/exHarmony huggingface dataset:
Reputation-Based Regional Federated Learning for Knowledge Trading in Blockchain-Enhanced IoV

The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in a collaborative manner. Previous works focus on data sharing between vehicles, but a large amount of data will lead to redundant transmission and network congestion. In addition, security and privacy issues prevent these nodes from participating in the sharing process. Knowledge is extracted from data through machine learning (ML) and shared in the form of small-scale well-trained model parameters, which improves collaborative learning more effectively and relieves network pressure. While traditional ML algorithms are not suitable for distributed IoV with local characteristics. Based on this, this paper first divides the vehicles into multiple regions and proposes a Regional Federated Learning (RFL) framework, in which all regions maintain their own learning models, i.e. knowledge. We design a reputation mechanism to measure the reliability of vehicles participating in RFL. To address the security challenges brought by the untrusted centralized trading market, we propose a blockchain-enhanced knowledge trading framework, in which an authorized market agency coordinates the trading quickly. We model the optimal pricing mechanism as a non-cooperative game, taking into account the competition among all knowledge providers. Numerical simulation shows that the proposed reputation mechanism improves the accuracy of knowledge up to 18%, and the optimal knowledge pricing mechanism effectively increases the utility of market.
Out of sight, out of mind? How vulnerable dependencies affect open-source projects

Software developers often use open-source libraries in their project to improve development speed. However, such libraries may contain security vulnerabilities, and this has resulted in several high-profile incidents in recent years. As usage of open-source libraries grows, understanding of these dependency vulnerabilities becomes increasingly important. In this work, we analyze vulnerabilities in open-source libraries used by 450 software projects written in Java, Python, and Ruby. Our goal is to examine types, distribution, severity, and persistence of the vulnerabilities, along with relationships between their prevalence and project as well as commit attributes. Our data is obtained by scanning versions of the sample projects after each commit made between November 1, 2017 and October 31, 2018 using an industrial software composition analysis tool, which provides information such as library names and versions, dependency types (direct or transitive), and known vulnerabilities. Among other findings, we found that project activity level, popularity, and developer experience do not translate into better or worse handling of dependency vulnerabilities. We also found “Denial of Service” and “Information Disclosure” types of vulnerabilities being common across the languages studied. Further, we found that most dependency vulnerabilities persist throughout the observation period (mean of 78.4%, 97.7%, and 66.4% for publicly-known vulnerabilities in our Java, Python, and Ruby datasets respectively), and the resolved ones take 3-5 months to fix. Our results highlight the importance of managing the number of dependencies and performing timely updates, and indicate some areas that can be prioritized to improve security in wide range of projects, such as prevention and mitigation of Denial-of-Service attacks.
Aspects of unmanned aerial vehicles path planning: Overview and applications

Summary Aiming to the applications from security surveillance, military operational capabilities to the content and package delivery, unmanned aerial vehicles (UAVs) has a successfully created his space in the available technologies. The compact sized powerful flying robots are wirelessly controlled and are capable to complete tasks with and without direct human intervention. UAVs however still face serious challenges that limit the dream of complete autonomous unmanned flying machines. The key challenges include path planning and obstacle avoidance of these unmanned flying robots that are unavoidable while performing the application‐specific functionalities both in indoor and outdoor environments. In this manuscript with a survey, we investigate the state‐of‐the‐art UAV path planning algorithms and obstacle avoidance techniques. We have also summarized and compared the schemes in tabular form. In addition, current and future research directions and challenges are also discussed, showing the prospective research directions.
FastGAE: Scalable graph autoencoders with stochastic subgraph decoding

Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of nodes and edges. Our strategy, based on an effective stochastic subgraph decoding scheme, significantly speeds up the training of graph AE and VAE while preserving or even improving performances. We demonstrate the effectiveness of FastGAE on various real-world graphs, outperforming the few existing approaches to scale graph AE and VAE by a wide margin.
Comprehensive Performance Analysis of a VCSEL-Based Photonic Reservoir Computer

Optical neural networks offer radically new avenues for ultrafast, energy-efficient hardware for machine learning and artificial intelligence.Reservoir Computing (RC), given its high performance and cheap training has attracted considerable attention for photonic neural network implementations, principally based on semiconductor lasers (SLs).Among SLs, Vertical Cavity Surface Emitting Lasers (VCSELs) possess unique attributes, e.g.high speed, low power, rich dynamics, reduced cost, ease to integrate in array architectures, making them valuable candidates for future photonic neural networks.This work provides a comprehensive analysis of a telecom-wavelength GHz-rate VCSEL RC system, revealing the impact of key system parameters on its performance across different processing tasks.
Preparing lessons: Improve knowledge distillation with better supervision

Knowledge distillation (KD) is widely applied in the training of efficient neural network. A compact model, which is trained to mimic the representation of a cumbersome model for the same task, generally obtains a better performance compared with being trained with the ground truth label. Previous KD-based works mainly focus on two aspects: (1) designing various feature representation for knowledge transfer; (2) introducing different training mechanism such as progressive learning or adversarial learning. In this paper, we revisit the standard KD and observe that training with teacher’s logits might suffer from incorrect and uncertain supervision. To tackle these problems, we propose two novel approaches to deal with incorrect logits and uncertain logits respectively, which are called Logits Adjustment (LA) and Dynamic Temperature Distillation (DTD). To be specific, LA rectifies the incorrect logits according to ground truth label and certain rules. While DTD treats the temperature of KD as a dynamic sample wise parameter rather than a static and global hyper-parameter, which actually notes the uncertainty for each sample’s logits. With iteratively updating the sample wise temperature, the student model could pay more attention on the samples that confuse the teacher model. Experiments on CIFAR-10/100, CINIC-10 and Tiny ImageNet verify that the proposed methods yield encouraging improvement compared with the standard KD. Furthermore, considering the simple implementations, LA and DTD can be easily attached to many KD-based frameworks and bring improvements without extra cost of training time and computing resources.
Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5,326 mobile app reviews. The findings show that (1) our model can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves an accuracy of 85% with relatively small training dataset; however, the accuracy improves as we increase the size of the training dataset.
Trusted data sharing with flexible access control based on blockchain

Along with the progress of cloud service, a growing quantity of data owners store their data on cloud databases, which can not only reduce data owners’ storage cost but also provide a quick search function. However, while cloud storage brings some conveniences to users, new privacy problems may emerge, such as the leakage of data privacy and user’s query privacy. The best way of protecting data privacy is to encrypt the data. So how to efficiently retrieve the ciphertext to make it available becomes a hot issue in recent years. In this paper, new searchable encryption with multiple keywords is described, it can improve the accuracy of retrieval results, and we present a secure and trusted data sharing framework based on attribute-based encryption (ABE), searchable encryption, and blockchain. Unlike the previous studies, we realize flexible data sharing by using ABE. Furthermore, we transfer the related calculation of ciphertext retrieval to blockchain for credible execution without relying on any trusted third party. The security analysis proves that our method meets the proposed security requirements of data, keyword index, trapdoor, and query. Finally, the experimental results indicate that our scheme suggested has certain practicability and efficiency.
Adversarial Attack on Large Scale Graph

Recent studies have shown that graph neural networks (GNNs) are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance. However, the high complexity of time and space makes them unmanageable for large scale graphs and becomes the major bottleneck that prevents the practical usage. We argue that the main reason is that they have to use the whole graph for attacks, resulting in the increasing time and space complexity as the data scale grows. In this work, we propose an efficient Simplified Gradient-based Attack (SGA) method to bridge this gap. SGA can cause the GNNs to misclassify specific target nodes through a multi-stage attack framework, which needs only a much smaller subgraph. In addition, we present a practical metric named Degree Assortativity Change (DAC) to measure the impacts of adversarial attacks on graph data. We evaluate our attack method on four real-world graph networks by attacking several commonly used GNNs. The experimental results demonstrate that SGA can achieve significant time and memory efficiency improvements while maintaining competitive attack performance compared to state-of-art attack techniques.
Proceed with Care: Reimagining Home IoT Through a Care Perspective

As the internet is increasingly embedded in the everyday things in our homes, we notice a need for greater focus on the role care plays in those relationships—and therefore an opportunity to realize unseen potential in reimagining home Internet of Things (IoT). In this paper we report on our inquiry of home dwellers’ relationships to caring for their everyday things and homes (referred to as thingcare). Findings from our design ethnography reveal four thematic qualities of their relationships to thingcare: Care Spectacle, Care Liminality, Ontological Braiding, and Care Condition. Using these themes as touchstones, we co-speculated to produce four speculative IoT concepts to explore what care as a design ethic might look like for IoT and reflect on nascent opportunities and challenges for domestic IoT design. We conclude by considering structures of power and privilege embedded within care practices that critically open new design imaginaries for IoT.
When Face Recognition Meets Occlusion: A New Benchmark

The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. Especially during the COVID-19 coronavirus epidemic, wearing a mask has become an effective means of preventing the virus spread. Traditional CNN-based face recognition models trained on existing datasets are almost ineffective for heavy occlusion. To this end, we pioneer a simulated occlusion face recognition dataset. In particular, we first collect a variety of glasses and masks as occlusion, and randomly combine the occlusion attributes (occlusion objects, textures,and colors) to achieve a large number of more realistic occlusion types. We then cover them in the proper position of the face image with the normal occlusion habit. Furthermore, we reasonably combine original normal face images and occluded face images to form our final dataset, termed as Webface-OCC. It covers 804,704 face images of 10,575 subjects, with diverse occlusion types to ensure its diversity and stability. Extensive experiments on public datasets show that the ArcFace retrained by our dataset significantly outperforms the state-of-the-arts. Webface-OCC is available at https://github.com/Baojin-Huang/Webface-OCC.
Improving classifier training efficiency for automatic cyberbullying detection with Feature Density

We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods in order to estimate dataset complexity, which in turn is used to comparatively estimate the potential performance of machine learning (ML) classifiers prior to any training. We hypothesize that estimating dataset complexity allows for the reduction of the number of required experiments iterations. This way we can optimize the resource-intensive training of ML models which is becoming a serious issue due to the increases in available dataset sizes and the ever rising popularity of models based on Deep Neural Networks (DNN). The problem of constantly increasing needs for more powerful computational resources is also affecting the environment due to alarmingly-growing amount of CO2 emissions caused by training of large-scale ML models. The research was conducted on multiple datasets, including popular datasets, such as Yelp business review dataset used for training typical sentiment analysis models, as well as more recent datasets trying to tackle the problem of cyberbullying, which, being a serious social problem, is also a much more sophisticated problem form the point of view of linguistic representation. We use cyberbullying datasets collected for multiple languages, namely English, Japanese and Polish. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
AdapTutAR: An Adaptive Tutoring System for Machine Tasks in Augmented Reality

Modern manufacturing processes are in a state of flux, as they adapt to increasing demand for flexible and self-configuring production. This poses challenges for training workers to rapidly master new machine operations and processes, i.e. machine tasks. Conventional in-person training is effective but requires time and effort of experts for each worker trained and not scalable. Recorded tutorials, such as video-based or augmented reality (AR), permit more efficient scaling. However, unlike in-person tutoring, existing recorded tutorials lack the ability to adapt to workers’ diverse experiences and learning behaviors. We present AdapTutAR, an adaptive task tutoring system that enables experts to record machine task tutorials via embodied demonstration and train learners with different AR tutoring contents adapting to each user’s characteristics. The adaptation is achieved by continually monitoring learners’ tutorial-following status and adjusting the tutoring content on-the-fly and in-situ. The results of our user study evaluation have demonstrated that our adaptive system is more effective and preferable than the non-adaptive one.
Fork It: Supporting Stateful Alternatives in Computational Notebooks

Computational notebooks, which seamlessly interleave code with results, have become a popular tool for data scientists due to the iterative nature of exploratory tasks. However, notebooks provide a single execution state for users to manipulate through creating and manipulating variables. When exploring alternatives, data scientists must carefully create many-step manipulations in visually distant cells.
Evaluating the Potential of Glanceable AR Interfaces for Authentic Everyday Uses

In the near future, augmented reality (AR) glasses are envisioned to become the next-generation personal computing platform. They could be always on and worn all day, delivering continuous and pervasive AR experiences for general-purpose everyday use cases. However, it remains unclear how we could enable unobtrusive and easy information access without distracting users, while being acceptable to use at the same time. To address this question, we implemented two prototypes based on the Glanceable AR paradigm, a promising way of managing and acquiring information through glancing at the periphery of AR head-worn displays (HWDs). We conducted two separate studies to evaluate our designs. In the first study, we obtained feedback from a large sample of participants of varied age and background about a video prototype that showcased some envisioned scenarios of using Glanceable AR for everyday tasks. In the second study, we asked participants to use a working prototype during authentic real-world activities for three days. We found that users appreciated the Glanceable AR approach. They found it less distracting or intrusive than existing devices in authentic everyday use cases, and would like to use the interface on a daily basis if the form factor of the AR headset was more like eyeglasses.
Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.
A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

The rapid growth of cloud computing in the last decade has led to an increasing concern about the energy requirement of cloud data centers. Dynamic virtual machine (VM) consolidation is an effective way to tackle this issue, where VMs are executed on as few physical machines (PMs) as possible. Meanwhile, VM placement must be performed strategically, by considering different factors of the available resources to optimal exploitation of them. Moreover, a major challenge is the system’s reliability degradation because of the high frequency of consolidation and placing VMs on unreliable PMs. In this paper, we address the problem by introducing a discrete-time Markov chain (DTMC) model to predict future resource usage. Using the DTMC model along with the reliability model of PMs leads to more accurate PMs categorization based on their status. Then, a multi-objective VM placement approach is proposed to achieve the optimal VMs to PMs mapping using the ε-dominance-based multi-objective artificial bee colony (ε-MOABC) algorithm which can efficiently balance the overall energy consumption, resource wastage, and the system reliability to meet SLA and QoS requirements. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using the CloudSim toolkit. Competitive analysis of the experimental results demonstrates that the proposed approach significantly improves energy consumption while avoiding the inefficient VM migrations.
Expanding Data Encoding Patterns For Quantum Algorithms

As quantum computers are based on the laws of quantum mechanics, they are capable of solving certain problems faster than their classical counterparts. However, often algorithms which a theoretical speed-up assume that data can be loaded efficiently. In general, the runtime complexity of the loading routine depends on (i) the data encoding that defines how the data is represented and (ii) the data itself. In some cases, loading the data requires at least exponential time which destroys a potential speed-up. And especially for the first generation of devices that are currently available, the resources (qubits and operations) needed to encode the data are limited. Therefore, understanding the consequences of a particular data encoding is crucial. To capture knowledge about different encodings, we present two data encoding patterns that extend our previous collection of encoding patterns [1].
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop

AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science. These systems have gained traction in enterprise settings where pools of skilled data workers are limited. In this study, we conduct interviews with 29 individuals from organizations of different sizes to characterize how they currently use, or intend to use, AutoML systems in their data science work. Our investigation also captures how data visualization is used in conjunction with AutoML systems. Our findings identify three usage scenarios for AutoML that resulted in a framework summarizing the level of automation desired by data workers with different levels of expertise. We surfaced the tension between speed and human oversight and found that data visualization can do a poor job balancing the two. Our findings have implications for the design and implementation of human-in-the-loop visual analytics approaches.