Reviewerly/exHarmony huggingface dataset:
A decentralized strongly secure attribute-based encryption and authentication scheme for distributed Internet of Mobile Things

Internet of Things (IoT) networks has been widely deployed as the distributed computing and communication component in the smart city. The security problems in the distributed IoT-assisted computing architecture are still noteworthy and has not been solved satisfactorily. So in this paper, to design and build an access control and admission detection model for the distributed IoT environment, we propose an IND-CCA-secure multi-authority ciphertext-policy ABE (MA-CP-ABE) scheme with outsourced decryption (OD) and progressive mode attribute-based authentication (ABAuthen). In our authenticaiton algorithm, by using the zero knowledge proof, the user's secret will be protected from leaking out to the server. And due to the randomness of the authentication message, our authentication algorithm can resist the impersonation attacks by the malicious server. Finally, by the theoretical analysis and performance evaluation for our scheme with other state-of-art schemes, we can observe that our encryption and authentication schemes are both efficient and applicable for the distributed IoT-assisted cloud computing.
Memobert: Pre-Training Model with Prompt-Based Learning for Multimodal Emotion Recognition

Multimodal emotion recognition study is hindered by the lack of labelled corpora in terms of scale and diversity, due to the high annotation cost and label ambiguity. In this paper, we propose a multimodal pre-training model MEmoBERT for multimodal emotion recognition, which learns multimodal joint representations through self-supervised learning from a self-collected large-scale unlabeled video data that come in sheer volume. Furthermore, unlike the conventional "pre-train, finetune" paradigm, we propose a prompt-based method that reformulates the downstream emotion classification task as a masked text prediction one, bringing the downstream task closer to the pre-training. Extensive experiments on two benchmark datasets, IEMOCAP and MSP-IMPROV, show that our proposed MEmoBERT significantly enhances emotion recognition performance.
SEAT

Given black-box access to the prediction API, model extraction attacks can steal the functionality of models deployed in the cloud. In this paper, we introduce the SEAT detector, which detects black-box model extraction attacks so that the defender can terminate malicious accounts. SEAT has a similarity encoder trained by adversarial training. Using the similarity encoder, SEAT detects accounts that make queries that indicate a model extraction attack in progress and cancels these accounts. We evaluate our defense against existing model extraction attacks and against new adaptive attacks introduced in this paper. Our results show that even against adaptive attackers, SEAT increases the cost of model extraction attacks by 3.8 times to 16 times.
MultiWOZ 2.3: A Multi-domain Task-Oriented Dialogue Dataset Enhanced with Annotation Corrections and Co-Reference Annotation

Task-oriented dialogue systems have made unprecedented progress with multiple state-of-the-art (SOTA) models underpinned by a number of publicly available MultiWOZ datasets. Dialogue state annotations are error-prone, leading to sub-optimal performance. Various efforts have been put in rectifying the annotation errors presented in the original MultiWOZ dataset. In this paper, we introduce MultiWOZ 2.3, in which we differentiate incorrect annotations in dialogue acts from dialogue states, identifying a lack of co-reference when publishing the updated dataset. To ensure consistency between dialogue acts and dialogue states, we implement co-reference features and unify annotations of dialogue acts and dialogue states. We update the state of the art performance of natural language understanding and dialogue state tracking on MultiWOZ 2.3, where the results show significant improvements than on previous versions of MultiWOZ datasets (2.0–2.2).
Multi-Domain Incremental Learning for Semantic Segmentation

Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experiment performed sequentially on three popular road scene segmentation datasets demonstrates that existing segmentation frameworks fail at incrementally learning on a series of visually disparate geographical domains. When learning a new domain, the model catastrophically forgets previously learned knowledge. In this work, we pose the problem of multi-domain incremental learning for semantic segmentation. Given a model trained on a particular geographical domain, the goal is to (i) incrementally learn a new geographical domain, (ii) while retaining performance on the old domain, (iii) given that the previous domain’s dataset is not accessible. We propose a dynamic architecture that assigns universally shared, domain-invariant parameters to capture homogeneous semantic features present in all domains, while dedicated domain-specific parameters learn the statistics of each domain. Our novel optimization strategy helps achieve a good balance between retention of old knowledge (stability) and acquiring new knowledge (plasticity). We demonstrate the effectiveness of our proposed solution on domain incremental settings pertaining to real-world driving scenes from roads of Germany (Cityscapes), the United States (BDD100k), and India (IDD). <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Network Embedding via Motifs

Network embedding has emerged as an effective way to deal with downstream tasks, such as node classification [ 16 , 31 , 42 ]. Most existing methods leverage multi-similarities between nodes such as connectivity, which considers vertices that are closely connected to be similar and structural similarity, which is measured by assessing their relations to neighbors; while these methods only focus on static graphs. In this work, we bridge connectivity and structural similarity in a uniform representation via motifs, and consequently present an algorithm for Learning Embeddings by leveraging Motifs Of Networks (LEMON), which aims to learn embeddings for vertices and various motifs. Moreover, LEMON is inherently capable of dealing with inductive learning tasks for dynamic graphs. To validate the effectiveness and efficiency, we conduct various experiments on two real-world datasets and five public datasets from diverse domains. Through comparison with state-of-the-art baseline models, we find that LEMON achieves significant improvements in downstream tasks. We release our code on Github at https://github.com/larry2020626/LEMON.
Calibrating large-FOV stereo digital image correlation system using phase targets and epipolar geometry

We propose an easy-to-implement and accurate calibration method for large-scale stereo-digital image correlation (stereo-DIC). First, the intrinsic parameters of each camera are separately calibrated using a regular-sized phase target. Using phase-shifted circular fringe patterns that feature the advantage of being insensitive to the defocus, the phase target can be placed in the close range of each camera. Then, scale-free extrinsic parameters are computed from the epipolar geometry, which can be easily retrieved from DIC registration of homologous point pairs in the stereo images of a test specimen surface. These intrinsic and scale-free extrinsic parameters are used as the initial guess for further optimizing the calibration results. Further, by measuring objects with known physical size, the scale information of the stereo-DIC system can be determined. The metrological performance of the proposed method is evaluated by large-scale shape reconstruction and deformation measurement tests. The obtained results demonstrate that the proposed method is a viable solution for the large-scale 3D deformation measurement tasks.
An Overview of Energy-Efficient Hardware Accelerators for On-Device Deep-Neural-Network Training

Deep Neural Networks (DNNs) have been widely used in various artificial intelligence (AI) applications due to their overwhelming performance. Furthermore, recently, several algorithms have been reported that require on-device training to deliver higher performance in real-world environments and protect users’ personal data. However, edge/mobile devices contain only limited computation capability with battery power, so an energy-efficient DNN training processor is necessary to realize on-device training. Although there are a lot of surveys on energy-efficient DNN inference hardware, the training is quite different from the inference. Therefore, analysis and optimization techniques targeting DNN training are required. This article aims to provide an overview of energy-efficient DNN processing that enables on-device training. Specifically, it will provide hardware optimization techniques to overcomes the design challenges in terms of distinct dataflow, external memory access, and computation. In addition, this paper summarizes key schemes of recent energy-efficient DNN training ASICs. Moreover, we will also show a design example of DNN training ASIC with energy-efficient optimization techniques.
You Are What and Where You Are

Point-of-interest (POI) recommendation is an emerging area of research on location-based social networks to analyze user behaviors and contextual check-in information. For this problem, existing approaches, with shallow or deep architectures, have two major drawbacks. First, for these approaches, the attributes of individuals have been largely ignored. Therefore, it would be hard, if not impossible, to gather sufficient user attribute features to have complete coverage of possible motivation factors. Second, most existing models preserve the information of users or POIs by latent representations without explicitly highlighting salient factors or signals. Consequently, the trained models with unjustifiable parameters provide few persuasive rationales to explain why users favor or dislike certain POIs and what really causes a visit. To overcome these drawbacks, we propose GEAPR, a POI recommender that is able to interpret the POI prediction in an end-to-end fashion. Specifically, GEAPR learns user representations by aggregating different factors, such as structural context, neighbor impact, user attributes, and geolocation influence. GEAPR takes advantage of a triple attention mechanism to quantify the influences of different factors for each resulting recommendation and performs a thorough analysis of the model interpretability. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed model. GEAPR is deployed and under test on an internal web server. An example interface is presented to showcase its application on explainable POI recommendation.
Real-Time Pashto Handwritten Character Recognition Using Salient Geometric and Spectral Features

Pashto scripts are cursive in nature and hard to recognize in real-time.Native speakers of the Pashto language are large in numbers and reside in different regions of the world.Due to the cursive nature of the Pashto script along with variations in character strokes, the printed, as well as handwritten characters, are difficult to be detected, classified or recognized.In real-time handwritten character recognition systems, the challenging factors that constraints the system depends on the stroke noise, geometric behavior (like rotation, scaling and shifting, etc.) of the text.In this article, we provide an efficient technique that aimed to recognize handwritten characters in real-time by first smoothing the noise components in the text and then extract shape-based invariant features from the handwritten strokes.For real-time recognition of characters, the probability-based multi-class Naïve Bayesian classifier is exploited, which determines the probabilities of geometric invariant features to predict the character with the highest likelihood.The performance of the proposed approach has been validated through extensive experiments and based on the recognition matrices, the proposed technique achieves an accuracy of 97.5% for online
Using logistic regression model selection towards interpretable machine learning in mineral prospectivity modeling

Regression or regression-like models are often employed in mineral prospectivity modeling, i.e., for the targeting of resources, either based on 2D map images or 3D geomodels both in raster mode or based on spatial point processes. Machine learning techniques like artificial neural networks are often applied and give decent results in the prediction of target events. However, they typically provide little insight into the problem regarding the importance, or relevance, of covariables. On the other hand, logistic regression has a well understood statistical foundation and uses an explicit model from which knowledge can be gained about the underlying phenomenon. Establishing such an explicit model is rather difficult for real world problems. In the context of mineral prospectivity modeling additional challenges arise, such as rare events, i.e. only a small fraction of data instances describes a positive target event, which is the event of interest. In this paper, we propose a model selection procedure applied to logistic regression incorporating explicit nonlinearities. The model selection procedure, based on the Wald test and the Bayes' information criterion (BIC), as proposed in this paper is new. The performance regarding the predictive power of the obtained model is comparable to logistic regression using a stepwise model selection and to neural networks on several real world datasets, one of them a dataset for the detection of gold mineralizations in Ghana. However, our new method is significantly faster than standard stepwise selection, while selecting fewer variables for the final model. In our numerical experiments, the prediction accuracy is also comparable to a neural network, which is currently in use in industry. In applications, the method can aid the model building process through an explicit model. Furthermore, it may be used as preprocessing step for other machine learning algorithms such as neural networks. In this paper, we intend to present mathematics of prospectivity modeling with the potential to contribute to bridging the gap between statistical and machine learning. Big Data and Artificial Intelligence are of increasing importance in mineral exploration. At the same time there is a growing demand for mathematically rigorous machine learning methods, which can still be interpreted by experts. This paper is a contribution to this field.
Blockchain in China

research-article Share on Blockchain in China Authors: Liang Cai Zhejiang University, Hangzhou, China Zhejiang University, Hangzhou, ChinaView Profile , Yi Sun Chinese Academy of Sciences, Beijing, China Chinese Academy of Sciences, Beijing, ChinaView Profile , Zibin Zheng Sun Yat-sen University, Guangzhou, China Sun Yat-sen University, Guangzhou, ChinaView Profile , Jiang Xiao Huazhong University of Science and Technology, Wuhan, China Huazhong University of Science and Technology, Wuhan, ChinaView Profile , Weiwei Qiu Hangzhou Qulian Technology Ltd., Hangzhou, China Hangzhou Qulian Technology Ltd., Hangzhou, ChinaView Profile Authors Info & Claims Communications of the ACMVolume 64Issue 11November 2021 pp 88–93https://doi.org/10.1145/3481627Published:25 October 2021Publication History 4citation6,430DownloadsMetricsTotal Citations4Total Downloads6,430Last 12 Months2,401Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Resource Efficient Gesture Sensing Based on FMCW Radar using Spiking Neural Networks

Gesture recognition is a natural and intuitive human computer-interface compared to traditional interfaces such as mouse and keyboards. Radar forms a promising modality for portable gesture recognition systems, where minute finger motions can be easily sensed and processed to extract meaningful information presenting the user's intention. Compared to several conventional deep learning approaches that have been proposed in the literature, in this paper we present a novel spiking neural network (SNNs) for gesture recognition using a 60-GHz frequency modulated continuous wave radar (FMCW) chipset. SNNs are more hardware friendly and energy-efficient than their deep learning counterparts making them attractive for portable devices. We have demonstrated in measurement the application of SNN for the processing of radar data.
Reversible data hiding with automatic contrast enhancement using two-sided histogram expansion

Recently, reversible data hiding (RDH) has emerged into a new class of data hiding methods that enables exact retrieving of both embedded data and cover medium. In the present study, a novel automatic RDH method with contrast enhancement is proposed, in which the data is embedded through two-sided histogram expansion. Two-sided histogram shifting doubles the number of bits embedded at each iteration. Moreover, it preserves the mean brightness of the cover image and prevents it from over enhancement with less calculation. Experimental results on two sets of images show that the proposed method enhances the image contrast at an appropriate level without using a mean brightness controller during data embedding and provides higher information security compared to the existing RDH approaches.
Classically Optimal Variational Quantum Algorithms

Hybrid quantum-classical algorithms, such as variational quantum algorithms (VQA), are suitable for implementation on NISQ computers.In this Article we expand an implicit step of VQAs: the classical pre-computation subroutine which can non-trivially use classical algorithms to simplify, transform, or specify problem instance-specific variational quantum circuits.In VQA there is a trade-off between quality of solution and difficulty of circuit construction and optimization.In one extreme, we find VQA for MAXCUT which are exact, but circuit design or variational optimization is NP-HARD.At the other extreme are low depth VQA, such as QAOA, with tractable circuit construction and optimization but poor approximation ratios.Combining these two we define the Spanning Tree QAOA (ST-QAOA) to solve MAXCUT, which uses an ansatz whose structure is derived from an approximate classical solution and achieves the same performance guarantee as the classical algorithm and hence can outperform QAOA at low depth.In general, we propose integrating these classical pre-computation subroutines into VQA to improve heuristic or guaranteed performance.
How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin

This paper investigates the differential sensitivity of Sharia-compliant stocks to Bitcoin's realized volatility of positive and negative intraday returns in bear, normal, and bull market states. We use a quantile regression approach, after orthogonalizing raw equity returns with respect to a variety of relevant global factors and accounting for structural breaks in the data. For developed markets, the results indicate that upside volatility tends to exert contemporaneous and lagged negative influences on Islamic stocks more in bear than in bull market conditions, whereas the downside counterpart positively affects returns when Sharia-compliant equities are in bear and bull phases. For emerging markets, we find that Bitcoin's upside (downside) volatility has lagged negative (positive) effects on returns across all market regimes. The dependence structures tend to be asymmetric and have noticeably become stronger in the last two years than in earlier periods of the sample. Our evidence offers important implications for investors.
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization

Deep neural networks have proven increasingly important for automotive scene understanding with new algorithms offering constant improvements of the detection performance. However, there is little emphasis on experiences and needs for deployment in embedded environments. We therefore perform a case study of the deployment of two representative object detection networks on an edge AI platform. In particular, we consider RetinaNet for image-based 2D object detection and PointPillars for LiDAR-based 3D object detection. We describe the modifications necessary to convert the algorithms from a PyTorch training environment to the deployment environment taking into account the available tools. We evaluate the runtime of the deployed DNN using two different libraries, TensorRT and Torch-Script. In our experiments, we observe slight advantages of TensorRT for convolutional layers and TorchScript for fully connected layers. We also study the trade-off between run-time and performance, when selecting an optimized setup for deployment, and observe that quantization significantly reduces the runtime while having only little impact on the detection performance.
Gaze Comes in Handy: Predicting and Preventing Erroneous Hand Actions in AR-Supported Manual Tasks

Emerging Augmented Reality headsets incorporate gaze and hand tracking and can, thus, observe the user’s behavior without interfering with ongoing activities. In this paper, we analyze hand-eye coordination in real-time to predict hand actions during target selection and warn users of potential errors before they occur. In our first user study, we recorded 10 participants playing a memory card game, which involves frequent hand-eye coordination with little task-relevant information. We found that participants’ gaze locked onto target cards 350ms before the hands touched them in 73.3% of all cases, which coincided with the peak velocity of the hand moving to the target. Based on our findings, we then introduce a closed-loop support system that monitors the user’s fingertip position to detect the first card turn and analyzes gaze, hand velocity and trajectory to predict the second card before it is turned by the user. In a second study with 12 participants, our support system correctly displayed color-coded visual alerts in a timely manner with an accuracy of 85.9%. The results indicate the high value of eye and hand tracking features for behavior prediction and provide a first step towards predictive real-time user support.
Smart Hat for the blind with Real-Time Object Detection using Raspberry Pi and TensorFlow Lite

The purpose of this paper is to introduce a system that uses the combination of working aid blind techniques to help blind people to have better navigation against objects, also to be aware of road and traffic lights signs using real-time image-processing. This study aims at improving the processing speed of object detection by introducing the latest Raspberry Pi 4 module, which is more powerful than the previous versions. As a result, the Single-Shot Multibox Detector MobileNet v2 convolutional neural network on Raspberry Pi 4 using TensorFlow Lite 2, is employed for object detection. A model called SSD MobileNet v2 320x320, which is trained on the COCO dataset from the TensorFlow model zoo, which is used in this system. Also, the traffic light signs classification is achieved via machine learning techniques. With all modules attached on the hat, video frames captured from the piCamera module are analyzed and searched for known objects by the Raspberry Pi module and playing the pre-recorded sound of the detected object(s) at an approximate distance away from the user. MobileNet uses 3 x 3 depthwise separable convolutions, which uses between 8 to 9 times lesser computing resources compared to the standard convolution at a small cost in accuracy. Thus, this makes the device suitable for the resource constraint devices such as the Raspberry Pi. The model achieves about 5 frames per second on the Raspberry P 4.