Reviewerly/exHarmony huggingface dataset:
CGM

Local differential privacy (LDP) is a well-established privacy protection scheme for collecting sensitive data, which has been integrated into major platforms such as iOS, Chrome, and Windows. The main idea is that each individual randomly perturbs her data on her local device, and only uploads the noisy version to an untrusted data aggregator. This paper focuses on the collection of streaming data consisting of regular updates, e.g. , daily app usage. Such streams, when aggregated over a large population, often exhibit strong autocorrelations , e.g. , the average usage of an app usually does not change dramatically from one day to the next. To our knowledge, this property has been largely neglected in existing LDP mechanisms. Consequently, data collected with current LDP methods often exhibit unrealistically violent fluctuations due to the added noise, drowning the overall trend, as shown in our experiments. This paper proposes a novel correlated Gaussian mechanism ( CGM ) for enforcing (ϵ, δ)-LDP on streaming data collection, which reduces noise by exploiting public-known autocorrelation patterns of the aggregated data. This is done through non-trivial modifications to the core of the underlying Gaussian Mechanism; in particular, CGM injects temporally correlated noise, computed through an optimization program that takes into account the given autocorrelation pattern, data value range, and utility metric. CGM comes with formal proof of correctness, and consumes negligible computational resources. Extensive experiments using real datasets from different application domains demonstrate that CGM achieves consistent and significant utility gains compared to the baseline method of repeatedly running the underlying one-shot LDP mechanism.
Sustaining Education with Mobile Learning for English for Specific Purposes (ESP): A Systematic Review (2012–2021)

Mobile learning for English Language Teaching (ELT) has become an emerging trend to sustain education, providing the option to learn English for Specific Purposes (ESP). However, the review of mobile learning in ESP is scarce, despite its importance in sustaining education. Thus, this review systematically identifies the trends of mobile learning concerning ESP. Using three databases, namely Web of Science (WoS), Scopus, and Educational Resources Information Centre (ERIC), 28 articles were extracted out of 139, from 2012 to 2021, with exclusion and inclusion criteria taken into consideration. First, the significant findings portrayed that mobile apps are the most commonly used means of mobile learning. Second, vocabulary and language competency is more emphasised in mobile learning for ESP. Third, mobile learning in ESP mostly dominated the business field of studies. These three findings implied that there are still gaps in research on mobile learning and ESP. Generally, this review is valuable for practitioners to know which mobile learning tool is ready to be used and in identifying gaps for research in mobile learning and ESP, to sustain education for the future.
A Robust Device-to-Device Continuous Authentication Protocol for the Internet of Things

The Internet of Things (IoT) is a heterogeneous environment that connects billions of devices.Thus, it is a significantly high-value target for attackers and suffers from several threats, especially impersonation attacks during the session.Moreover, the denial of service attack (DoS) threatens IoT environments, as it affects the availability and energy of communicating devices.Continuous authentication solves session hijacking since it checks user legitimacy during the session.Several continuous authentication schemes were proposed to authenticate users to IoT devices, while few works addressed device-to-device authentication.Therefore, it is essential to authenticate devices because if one device gets compromised, then the whole system is at risk.Continuous authentication between devices differs from user-to-device authentication since it cannot rely on biometrics and passwords.This research proposes a fast and secure device-to-device continuous authentication protocol that relies on devices' features (token, battery, and location), and mitigates DoS attacks using shadow IDs and emergency keys.Moreover, it takes the sensor movement into account while preserving privacy.To evaluate the robustness and validate the security of the proposed protocol, we conducted informal and formal analyses using Scyther.In addition, we tested its performance to establish computation costs relative to the system counterparts.The results show the protocol is robust against security threats, incurring reasonable computational costs.
Evaluation of “innovation chain + supply chain” fusion driven by blockchain technology under typical scenario

Based on different innovation level of manufacturing enterprise's technological demands, this study builds a panoramic framework for the fusion of innovation and supply chain to match the technological demands with the technology market, and proposes three scenarios in which enterprise can achieve its goal with the help of blockchain technology (BCT). By choosing a typical scenario of collaboration between enterprise and innovators in the innovation chain, we construct principal-agent models applied into a chained collaborative innovation scenario. Focusing on level of efforts and revenue allocation ratio during the process of technological upgrade, we analyze the mechanism of value creation and interest sharing among enterprise and innovators under blockchain enabled innovation chain (BCEIC). The results show that under the blockchain enabled scenario: (1) It is beneficial to be an innovator in early stage of the innovation chain: the optimal level of efforts for early innovators is greater than that of traditional model, and also the optimal revenue obtained is greater than that of traditional model; (2) Innovators are more focused on innovation activities: the innovator's optimal level of effort respect to success rate fluctuates less than the traditional model; (3) Fully motivate all innovators: when success rate is low, proportion of revenue allocated to the innovator is extremely low, but once the success rate goes up by just a small margin, enterprise will elevate the optimal proportion of revenue allocated to innovators in order to motivate all innovators. Finally, based on the results in this typical scenario, we discuss the prospect of a panoramic application of blockchain technology into innovation chain, which will most likely produce even better results.
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive - potentially occupying hundreds of gigabytes in large-scale settings. To help manage this outsized memory consumption, we explore mixed dimension embeddings, an embedding layer architecture in which a particular embedding vector's dimension scales with its query frequency. Through theoretical analysis and systematic experiments, we demonstrate that using mixed dimensions can drastically reduce the memory usage, while maintaining and even improving the ML performance. Empirically, we show that the proposed mixed dimension layers improve accuracy by 0.1 % using half as many parameters or maintain it using 16 x fewer parameters for click-through rate prediction on the Criteo Kaggle dataset. They also train over 2x faster on a GPU. A full version of this paper is accessible at: https://arxiv.org/abs/1909.11810
Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women

Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of DL models to estimate the risk of interval and screening-detected breast cancers with and without clinical risk factors. Materials and Methods This study was performed on 25 096 digital screening mammograms obtained from January 2006 to December 2013. The mammograms were obtained in 6369 women without breast cancer, 1609 of whom developed screening-detected breast cancer and 351 of whom developed interval invasive breast cancer. A DL model was trained on the negative mammograms to classify women into those who did not develop cancer and those who developed screening-detected cancer or interval invasive cancer. Model effectiveness was evaluated as a matched concordance statistic (C statistic) in a held-out 26% (1669 of 6369) test set of the mammograms. Results The C statistics and odds ratios for comparing patients with screening-detected cancer versus matched controls were 0.66 (95% CI: 0.63, 0.69) and 1.25 (95% CI: 1.17, 1.33), respectively, for the DL model, 0.62 (95% CI: 0.59, 0.65) and 2.14 (95% CI: 1.32, 3.45) for the clinical risk factors with the Breast Imaging Reporting and Data System (BI-RADS) density model, and 0.66 (95% CI: 0.63, 0.69) and 1.21 (95% CI: 1.13, 1.30) for the combined DL and clinical risk factors model. For comparing patients with interval cancer versus controls, the C statistics and odds ratios were 0.64 (95% CI: 0.58, 0.71) and 1.26 (95% CI: 1.10, 1.45), respectively, for the DL model, 0.71 (95% CI: 0.65, 0.77) and 7.25 (95% CI: 2.94, 17.9) for the risk factors with BI-RADS density (b rated vs non-b rated) model, and 0.72 (95% CI: 0.66, 0.78) and 1.10 (95% CI: 0.94, 1.29) for the combined DL and clinical risk factors model. The P values between the DL, BI-RADS, and combined model's ability to detect screen and interval cancer were .99, .002, and .03, respectively. Conclusion The deep learning model outperformed in determining screening-detected cancer risk but underperformed for interval cancer risk when compared with clinical risk factors including breast density. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Bae and Kim in this issue.
The role of domain expertise in trusting and following explainable AI decision support systems

Although the roots of artificial intelligence (AI) stretch back some years, it currently flourishes in research and practice. However, AI deals with trust issues. One possible solution approach is making AI explain itself to its user, but it is still unclear how an AI can accomplish this in decision-making scenarios. This study focuses on how a user's expertise influences trust in explainable AI (XAI) and how this influences behaviour. To test our theoretical assumptions, we develop an AI-based decision support system (DSS), observe user behaviour in an online experiment, complemented with survey data. The results show that domain-specific expertise negatively affects trust in AI-based DSS. We conclude that the focus on explanations might be overrated for users with low domain-specific expertise, whereas it is vital for users with high expertise. Investigating the influence of expertise on explanations of an AI-based DSS, this study contributes to research on XAI and DSS.
Blockchain-Based Privacy Preserving and Energy Saving Mechanism for Electricity Prosumers

With the development of distributed and renewable energy resources and smart grids, energy management systems that allow electricity prosumers to schedule their power usage, are seen as a prominent solution for reducing electricity costs. This paper presents a novel blockchain-based mechanism to incentivize prosumers to save energy, while preserving their privacy. In the proposed mechanism, each prosumer utilizes an energy management system that is based on the percentage power change (PPC) at each hour of the day. The use of PPC values allows the proposed blockchain to preserve the privacy of the prosumers as no sensitive information is shared. The calculated PPC values are shared among the prosumers. The prosumer with the minimum PPC value is selected as the validator of the blockchain, which is responsible for creating the next block of the blockchain. The problem of approving the validator, by other prosumers, is formulated using a novel zero-metric weighted average consensus. The communication model required to reach this consensus is investigated and the consensus value is analytically derived. Multiple test systems with varying number of prosumers are simulated and analyzed. The results demonstrate the capability of the proposed mechanism to sustain energy while preserving privacy in a scalable manner.
Financial fraud detection using naive bayes algorithm in highly imbalance data set

This is the era, where the plastic money concept is widely adapted all over the world, but every new technology has its own loopholes also. In this scenario many types of anomalies can happen which can harm the user economically. These anomalies can be defined as frauds in financial sector. To detect these types of frauds, many techniques and models are proposed by the researchers. In this study the proposed work tries to implement an automated model using different machine learning techniques for the detection of these kinds of frauds, especially related to credit cards transactions. The proposed model applied four algorithms used in machine learning, namely Naive Bayes, Random Forest, Logistic Regression and SVM on a very large dataset to predict the fraud. Naive Bayes algorithm performance is outstanding for detection of credit card fraud among all the ML algorithms with the accuracy 80.4% and the area under the curve is 96.3%
LightQNet: Lightweight Deep Face Quality Assessment for Risk-Controlled Face Recognition

End-to-end face quality assessment based on deep learning can directly predict the overall quantitative score of face quality, thus helping to control the risk of face recognition system. Thanks to the development of automatic quality pseudo-label generation, most recent methods can use large-scale face datasets to learn the quality model. However, existing methods use regression models to fit the pseudo-labels, which lack attention to samples that are easy to be misidentified, and require large models for training. The paper treats the quality assessment as a classification problem, focusing on difficult samples near the classification boundary. Specifically, pairwise binary quality pseudo-label is generated based on the face similarity score without additional manual annotation. An identification quality loss is used to decouple the pairwise network training. In addition, a lightweight quality network is trained by performing knowledge distillation on the quality prediction branch of the face recognition network. Experiments show that the proposed quality network achieves state-of-the-art results with only 0.45M parameters and 77M FLOPs.
Learning to Classify With Incremental New Class

New class detection and effective model expansion are of great importance in incremental data mining. In open incremental data environments, data often come with novel classes, e.g., the emergence of new classes in image classification or new topics in opinion monitoring, and is denoted as class-incremental learning (C-IL) in literature. There are two main challenges in C-IL: how to conduct novelty detection and how to update the model with few novel class instances. Most previous methods pay much attention to the former challenge while ignoring the problem of efficiently updating models. To solve this problem, we propose a novel framework to handle the incremental new class, named learning to classify with incremental new class (LC-INC), which can process these two challenges automatically in one unified framework. In detail, LC-INC utilizes a novel structure network to consider the prototype information between class centers of known classes and newly incoming instances, which can dynamically combine the prediction information with structure information to detect novel class instances efficiently. On the other hand, the proposed structure network can also act as a meta-network, which can learn to expand the model much faster and more efficiently with inadequate novel class instances. Experiments on synthetic and real-world datasets successfully validate the effectiveness of our proposed method.
External Forces Resilient Safe Motion Planning for Quadrotor

Adaptive autonomous navigation with no prior knowledge of extraneous disturbance is of great significance for quadrotors in a complex and unknown environment. The mainstream approach that considers external disturbance is to implement disturbance-rejected control and path tracking. However, the robust control to compensate for tracking deviations is not well-considered regarding energy consumption, and even the reference path will become risky and intractable with disturbance. As recent external forces estimation advances, it is possible to incorporate a real-time force estimator to develop more robust and safe planning frameworks. This letter proposes a systematic (re)planning framework that can resiliently generate safe trajectories under volatile conditions. Firstly, a front-end kinodynamic path is searched with force-biased motion primitives. Then we develop a nonlinear model predictive control (NMPC) as a local planner with Hamilton-Jacobi (HJ) forward reachability analysis for error dynamics caused by external forces. It guarantees collision avoidance by constraining the ellipsoid of the quadrotor body expanded with the forward reachable sets (FRSs) within safe convex polytopes. Our method is validated in simulations and real-world experiments with different sources of external forces.
Periodic integration-based polarization differential imaging for underwater image restoration

Underwater image restoration is extensively used in many applications, such as ocean exploration and underwater rescue. With the development of polarization imaging technology, polarization differential imaging (PDI) has gradually become one of the most effective tools for image restoration in the underwater environment. To eliminate the interferences of inconsistent polarization direction and image noise in conventional PDI systems, we propose a new underwater image restoration method based on the periodic integration of polarization images. The method replaces one or two pairs of orthogonal polarization images with the integration of a series of polarization images in PDI system to achieve underwater image restoration. Firstly, we captured a series of polarization images in different polarization directions during a complete variation cycle of image intensity. Then, these polarization images are accumulated together, and the result is approximately regarded as the intensity integration of polarization light in the dimension of polarization direction. Finally, we can calculate the polarization degree of each pixel and obtain a clear polarization differential image. Compared with the existing PDI methods based on one or two pairs of orthogonal polarization images, both qualitative and quantitative experimental results show that the proposed method has better performance in texture enhancement and noise suppression.
Semantic-Interactive Graph Convolutional Network for Multilabel Image Recognition

Multilabel image recognition, a critically practical task in computer vision, aims to predict multiple objects present in each image. The existing studies mainly focus on conceptual visual cues but fail to reconcile the visual information with their semantic guidance. Intuitively, humans can not only associate extra topological concepts but also imagine other approximate scenes based on a semantic description. Inspired by such semantic-interactive capability, two different types of semantic priors, i.e., the concept correlations of the same scene and semantic similarities among different scenes, should be further explored for the recognition decisions. To efficiently interact with these semantic relationships, in this article, we propose a novel semantic-interactive graph convolutional network (SI-GCN), which can leverage the topological information learned from knowledge graphs to boost the performance of multilabel recognition. Specifically, the proposed SI-GCN framework consists of two different GCN-based branches in parallel, i.e., concept correlations learning (CCL) branch and semantic similarity learning (SSL) branch. Inputting the semantic-embedding vectors of all the concepts, the CCL branch maps the label co-occurrence graph into a set of interdependent concept classifiers. Recalibrating the image feature embedding with the standardized supervision of the semantic similarity graph, the SSL branch learns the semantically consistent in-batch visual representations. Finally, a well-established interactive learning scheme is formulated to concurrently optimize the obtained concept classifiers and the visual representation learning in an end-to-end manner. Extensive experiments on the MS-COCO and Pascal VOC 2007 & 2012 benchmarks demonstrate the superiorities of the proposed SI-GCN method compared to the state-of-the-art baselines.
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension

Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC. To fully exploit such discourse structure in multiparty dialogue, we present a discourse-aware dialogue graph neural network, DADgraph, which explicitly constructs the dialogue graph using discourse dependency links and discourse relations. To validate our model, we perform experiments on the Molweni corpus, a large-scale MRC dataset built over multiparty dialogue annotated with discourse structure. Experiments on Molweni show that our discourse-aware model achieves statistically significant improvements compared against strong neural network MRC baselines.
The Need for Verbal Robot Explanations and How People Would Like a Robot to Explain Itself

Although non-verbal cues such as arm movement and eye gaze can convey robot intention, they alone may not provide enough information for a human to fully understand a robot’s behavior. To better understand how to convey robot intention, we conducted an experiment ( N = 366 ) investigating the need for robots to explain , and the content and properties of a desired explanation such as timing , engagement importance , similarity to human explanations, and summarization . Participants watched a video where the robot was commanded to hand an almost-reachable cup and one of six reactions intended to show the unreachability : doing nothing (No Cue), turning its head to the cup (Look), or turning its head to the cup with the addition of repeated arm movement pointed towards the cup (Look &amp; Point), and each of these with or without a Headshake. The results indicated that participants agreed robot behavior should be explained across all conditions, in situ , in a similar manner as what human explain, and provide concise summaries and respond to only a few follow-up questions by participants. Additionally, we replicated the study again with N = 366 participants after a 15-month span and all major conclusions still held.