Reviewerly/exHarmony huggingface dataset:
Margin-aware rectified augmentation for long-tailed recognition

The long-tailed data distribution is prevalent in real world and it poses great challenge on deep neural network training. In this paper, we propose Margin-aware Rectified Augmentation (MRA) to tackle this problem. Specifically, the MRA consists of two parts. From the data perspective, we analyze that data imbalance will cause the decision boundary be biased, and we propose a novel Margin-aware Rectified mixup (MR-mixup) that adaptively rectifies the biased decision boundary. Furthermore, from the model perspective, we analyze that the imbalance will also lead to consistent 'gradient suppression' on minority class logits. Then we propose Reweighted Mutual Learning (RML) that provides extra 'soft target' as supervision signal and augments the 'encouraging gradients' on the minority classes. We conduct extensive experiments on benchmark datasets CIFAR-LT, ImageNet-LT and iNaturalist18. The results demonstrate that the proposed MRA not only achieves state-of-the-art performance, but also yields a better-calibrated prediction.
What affects the usage of artificial conversational agents? An agent personality and love theory perspective

Artificial-intelligence (AI) powered conversational (CAI) agents have been growing in popularity. Like human personality, CAI agent personality can also impact the relationship users develop with them. In this work we adopt a multi-methodological systematic approach for generating CAI agent personality descriptors. 235 unique descriptors are obtained grouped into 8 personality dimensions. Having generated the personality framework, we propose a research model based on Stimulus Organism Response framework and Sternberg's Triangular Theory of Love for explaining how these personality traits lead to formation of love with these agents affecting their usage scenario. Results indicate that all the three components of love (passion, intimacy, and commitment) have significant effects on the usage scenario. However, depending upon the agent personality the nature of relationships varies. Cognitive personality results in fatuous love, affective personality results in consummate love, and social personality results in friendly relationship. A conceptual difference is observed between brand love, and the "love for AI." The results of this research will not only help the HCI designers to create suitable machine personality for various AI-based agents, but it will also provide an unconventional approach towards examining adoption of emerging AI-based technologies by exploring the love aspect between man and machines.
A Practical Compiler for Attribute-Based Encryption: New Decentralized Constructions and More

The pair encodings framework is an important result in the simplified design of complex attribute-based encryption schemes. In particular, it reduces the effort of proving security of a scheme to proving security of the associated pair encoding, which can then be transformed into a provably secure pairing-based encryption scheme with a compiler. Especially the symbolic property, as introduced by Agrawal and Chase (EUROCRYPT '17), has proven to be a valuable security notion that is both simple to verify and applies to many schemes. Nevertheless, several practical extensions using full-domain hashes or employing multiple authorities cannot be instantiated with this compiler, and therefore still require complicated proof techniques. In this work, we present the first compiler for attribute-based encryption schemes that supports such extensions. To this end, we generalize the definitions of pair encodings and the symbolic property. With our compiler, we flexibly instantiate any pair encodings that satisfy this new notion of the symbolic property in any pairing-friendly groups, and generically prove the resulting scheme to be selectively secure. To illustrate the effectiveness of our new compiler, we give several new multi-authority and hash-based constructions.
RETRACTED: Can Digital Financial Inclusion Help Reduce Urban Crime? Evidence from Chinese Criminal Judgment on Theft Cases

The rapid development of digital finance has changed all aspects of human life and has also had a deep impact on the social governance system. This paper constructs an unbalanced panel of data of the theft crime rates for 289 cities in China during 2014–2019 based on the theft criminal judgments published on China’s Judicial Documents website and explores the impact of digital financial inclusion on urban theft crime. It shows that there is a significantly negative correlation between digital financial inclusion and the urban theft crime rate, indicating that the development of digital financial inclusion can effectively reduce urban theft crime, which is also confirmed by instrumental variable analysis based on the spherical distance between cities and Hangzhou, and that digital financial inclusion mainly reduces theft crime committed by more serious and highly educated individuals. In addition, mechanism analysis shows that digital financial inclusion can reduce the expected benefits of theft by enhancing payment convenience and raise the opportunity cost by promoting employment. Therefore, in the Internet era, it is essential for China to continuously improve social governance tools that adapt to the development of new technologies to achieve high-quality urban development.
Data Hiding Scheme using Difference Expansion and Modulus Function

Our information is constantly under threat when transmitted through public networks. So, research to keep information secret has been carried out. Mainly, steganography, which consists of hiding data in digital media, receives much attention. Existing steganographic systems identified the need to improve performance by reducing a tradeoff between the peak signal-to-noise ratio (PSNR) and bits per pixel (BPP). In this paper, we propose a new steganographic scheme to embed the bits of secret messages in a digital image’s pixels. Our method expands the differences between the neighbouring pixels for secret data concealment. We group the pixels in blocks of size $1\times 3$, and two of the three pixels of the block are candidates to hold the secret bit. We also propose extracting the hidden data to validate our data concealment scheme. To extract the secret data, we also arrange the neighbouring pixels into blocks of three and use their differences, and a modulus function, based on pixels identified carrying the secret data based on the key generated during data concealment. To evaluate the performance of our scheme, we consider the PSNR and the BPP as metrics. The experimental results showed better performance over the existing methods with 68.7790 dB for the PSNR and 0.1562 BPP.
Reliability in software engineering qualitative research through Inter-Coder Agreement

The research on empirical software engineering that uses qualitative data analysis is increasing. However, most of them do not deepen into the validity of the findings, specifically in the reliability of coding in which these methodologies rely on. This paper aims to establish a novel theoretical framework that enables a methodological approach for conducting this validity analysis through Inter-Coder Agreement (ICA), based on the use of coefficients to measure the degree of agreement in collaborative coding. We systematically review several existing variants of Krippendorff's α coefficients and provide a novel common mathematical framework to unify them. Finally, this paper illustrates the use of this theoretical framework in a large case study on DevOps culture. We expect that this work will help researchers who are committed to measuring consensus with quantitative techniques in collaborative coding, conducted as part of a qualitative research, to improve the rigor of their findings.
A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm's sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population's variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person's location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network's average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.
Sensory Adaptation in Biomolecular Memristors Improves Reservoir Computing Performance

Despite its prevalence in neurosensory systems for pattern recognition, event detection, and learning, the effects of sensory adaptation (SA) are not explored in reservoir computing (RC). Monazomycin‐based biomolecular synapse (MzBS) devices that exhibit volatile memristance and short‐term plasticity with two strength‐dependent modes of response are studied: facilitation and facilitation‐then‐depression (i.e., SA). Their ability to perform RC tasks including digit recognition, nonlinear function learning, and aerodynamic gust classification via combination of model‐based device simulations and physical experiments where SA presence is controlled is studied. Simulations exhibiting moderate SA achieve significantly higher accuracy classifying a custom 5 × 5 binary digit set, with experimental validation achieving maximum testing accuracies of 90%. Classifications of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset achieve a maximum testing accuracy of 94.34% in devices with SA. Fitting error of the Mackey–Glass time series is also significantly reduced by SA. Experimentally obtained pressure distributions representing gusts on an airfoil in a wind tunnel are classified by MzBS reservoirs. Reservoirs exhibiting SA achieve 100% accuracy, unlike MzBS reservoirs without SA and comparable static neural networks.
Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects

Moving object segmentation (MOS), aiming at segmenting moving objects from video frames, is an important and challenging task in computer vision and with various applications. With the development of deep learning (DL), MOS has also entered the era of deep models toward spatiotemporal feature learning. This paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three years. Specifically, we present a more up-to-date categorization based on model characteristics, then compare and discuss each category from feature learning (FL), and model training and evaluation perspectives. For FL, the methods reviewed are divided into three types: spatial FL, temporal FL, and spatiotemporal FL, then analyzed from input and model architectures aspects, three input types, and four typical preprocessing subnetworks are summarized. In terms of training, we discuss ideas for enhancing model transferability. In terms of evaluation, based on a previous categorization of scene dependent evaluation and scene independent evaluation, and combined with whether used videos are recorded with static or moving cameras, we further provide four subdivided evaluation setups and analyze that of reviewed methods. We also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of technology. Finally, based on the above comparisons and discussions, we present research prospects and future directions.
Semi-Passive RFID Electronic Devices With On-Chip Sensor Fusion Capabilities for Motion Capture and Biomechanical Analysis

This article presents the development and testing of a novel electronic device for wireless motion capture (W-MoCap), a technology that allows the reconstruction of movements of objects and body parts with many potential applications in various contexts. The device integrates UHF radio frequency identification (RFID) technology with sensors for low-power backscattering communication. It consists of a battery-assisted passive (BAP) UHF RFID chip, an inertial measurement unit (IMU), an ultralow power microcontroller, and a custom-designed edge-fed body-tolerant antenna operating at 866 MHz. The proper matching between the RFID chip and antenna is ensured through a well-designed L-match unbalanced network, and the separation of RF and dc signals is achieved with a meandered microstrip quarter-wavelength transformer, choke inductor, and decoupling capacitor. The designed and realized RFID W-MoCap sensor tag has been thoroughly evaluated in terms of current consumption, front-end sensitivity, and sensing accuracy. Finally, five prototypes have been applied to specific segments of a human subject and successfully tested in a practical scenario for real-time reconstruction of human movements.
Improving BERT model for requirements classification by bidirectional LSTM-CNN deep model

In the last decade, requirements classification has emerged as hot research topic in Requirements Engineering (RE). Early identification of software requirements helps the development team in the design of software systems. Manual identification and classification of these requirements is time-consuming and labor-intensive task. To address this issue, machine and deep learning techniques have been studied for automatic classification of requirements. Furthermore, an efficient word embedding representation of the input data is a major concern in automatic approaches. This research work presents a novel requirements classification model that integrates Bidirectional Encoder Representations from Transformers (BERT) with Bidirectional Long-short Term Memory (BiLSTM) with Convolutional Neural Network (CNN) layer called BERT-BiCNN. In BERT-BiCNN, BERT is used as word embedding layer that extract the full combination of contextual semantics. BiLSTM obtains contextual information. The CNN mechanism has been employed to reduce the dimensionality of feature space by selecting the important features. The effectiveness of BERT-BiCNN is evaluated on the PROMISE dataset. On comparative analysis, it can be concluded that proposed approach outperforms six recent deep learning based architectures in binary and multi-class classification.
Study on the new implementation mode of cross-docking based on blockchain technology

Cross-docking is a logistics operation in which goods are sorted and reloaded without entering a warehouse. It can significantly shorten the transportation time of goods, improve the efficiency of goods turnover, and reduce inventory backlogs and operational risks. Therefore, it has received considerable attention and has widespread applications. However, in the current operational mode of logistics centers, it is difficult to synchronize the transportation of goods from suppliers to logistics centers and the replenishment transportation from logistics centers to convenience stores, resulting in a low cross-docking utilization rate. In this study, blockchain technology was used to address the problem of low cross-docking. We proposed a new mode of cross-docking implementation to promote large–scale applications. We designed the application of smart contracts, which is the key technology of this mode. We implemented it by establishing a bilevel programming model for negotiation transactions between suppliers and convenience stores and a queuing model for cross-docking operation scheduling. Additionally, we proposed an improvement scheme for particle swarm optimization (PSO) algorithm of the solving model to improve computational accuracy. Finally, we used the simulation data to obtain a feasible price range of cross-docking operation in the new mode by analyzing its impact on the transaction status, utility of all participants, and status of cross-docking operations.
ADS_UNet: A nested UNet for histopathology image segmentation

The UNet model consists of fully convolutional network (FCN) layers arranged as contracting encoder and upsampling decoder maps. Nested arrangements of these encoder and decoder maps give rise to extensions of the UNet model, such as UNete and UNet++. Other refinements include constraining the outputs of the convolutional layers to discriminate between segment labels when trained end to end, a property called deep supervision. This reduces feature diversity in these nested UNet models despite their large parameter space. Furthermore, for texture segmentation, pixel correlations at multiple scales contribute to the classification task; hence, explicit deep supervision of shallower layers is likely to enhance performance. In this paper, we propose ADS_UNet, a stage-wise additive training algorithm that incorporates resource-efficient deep supervision in shallower layers and takes performance-weighted combinations of the sub-UNets to create the segmentation model. We provide empirical evidence on three histopathology datasets to support the claim that the proposed ADS_UNet reduces correlations between constituent features and improves performance while being more resource efficient. We demonstrate that ADS_UNet outperforms state-of-the-art Transformer-based models by 1.08 and 0.6 points on CRAG and BCSS datasets, and yet requires only 37% of GPU consumption and 34% of training time as that required by Transformers. The source code is available at: .
You spin me right round, baby, right round: Examining the Impact of Multi-Sensory Self-Motion Cues on Motion Sickness During a VR Reading Task

Motion sickness is a problem for many in everyday travel and will become more prevalent with the rise of automated vehicles. Virtual Reality (VR) headsets have shown significant promise in-transit, enabling passengers to engage in immersive entertainment and productivity experiences. In a controlled multi-session motion sickness study using an actuated rotating chair, we examine the potential of multi-sensory visual and auditory motion cues, presented during a VR reading task, for mitigating motion sickness. We found that visual cues are most efficient in reducing symptoms, with auditory cues showing some beneficial effects when combined with the visual. Motion sickness had negative effects on presence as well as task performance, and despite the cognitive demand and multi-sensory cues, motion sickness still reached problematic levels. Our work emphasises the need for effective mitigations and the design of stronger multi-sensory motion cues if VR is to fulfil its potential for passengers.
Tailor Twist: Assessing Rotational Mid-Air Interactions for Augmented Reality

Mid-air gestures, widely used in today's Augmented Reality (AR) applications, are prone to the "gorilla arm" effect, leading to discomfort with prolonged interactions. While prior work has proposed metrics to quantify this effect and means to improve comfort and ergonomics, these works usually only consider simplistic, one-dimensional AR interactions, like reaching for a point or pushing a button. However, interacting with AR environments also involves far more complex tasks, such as rotational knobs, potentially impacting ergonomics. This paper advances the understanding of the ergonomics of rotational mid-air interactions in AR. For this, we contribute the results of a controlled experiment exposing the participants to a rotational task in the interaction space defined by their arms' reach. Based on the results, we discuss how novel future mid-air gesture modalities benefit from our findings concerning ergonomic-aware rotational interaction.
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making

In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only based on AI confidence indicating AI's correctness likelihood (CL) but ignored humans' CL, hindering optimal team decision-making. To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level. We first modeled humans' CL by approximating their decision-making models and computing their potential performance in similar instances. We demonstrated the feasibility and effectiveness of our model via two preliminary studies. Then, we proposed three CL exploitation strategies to calibrate users' trust explicitly/implicitly in the AI-assisted decision-making process. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence. We further provided practical implications for more human-compatible AI-assisted decision-making.
Multi-objects Recognition and Self-Explosion Defect Detection Method for Insulators Based on Lightweight GhostNet-YOLOV4 model deployed onboard UAV

In order to meet the application requirements on unmanned aerial vehicle (UAV) for daily inspection of insulators in overhead grid transmission lines, a lightweight GhostNet-YOLOV4 model is proposed to identify the insulators objects and detect their self-explosion defect meanwhile. In the proposed model, the lightweight GhostNet with full convolutional attention module (C-SE) model is embedded into the YOLOV4 as backbone feature extraction network, and the ordinary convolution and Mish activation function of enhanced feature extraction network in GhostNet-YOLOV4 are replaced by the depth separable convolution and ReLU6 activation function respectively. Meanwhile, the combination methods of data augmentation, label smoothing, K-Means algorithm, adaptive cosine annealing learning rate, and Focal Loss function are adopted to optimize the model. Finally, the class activation mapping (CAM) is used to visually analyze for the identification process of the proposed model. The example analysis results show that the object recognition accuracy mAP of the proposed model is 99.50% for insulator objects and self-explosion defect, and the image processing speed FPS is 2.53 frames/s. The proposed model meets the requirements of lightweight, detect accuracy and speed for UAV application.
iFAD Gestures: Understanding Users’ Gesture Input Performance with Index-Finger Augmentation Devices

We examine gestures performed with a class of input devices with distinctive quality properties in the wearables landscape, which we call "index-Finger Augmentation Devices" (iFADs). We introduce a four-level taxonomy to characterize the diversity of iFAD gestures, evaluate iFAD gesture articulation on a dataset of 6,369 gestures collected from 20 participants, and compute recognition accuracy rates. Our findings show that iFAD gestures are fast (1.84s on average), easy to articulate (1.52 average rating on a difficulty scale from 1 to 5), and socially acceptable (81% willingness to use them in public places). We compare iFAD gestures with gestures performed using other devices (styli, touchscreens, game controllers) from several public datasets (39,263 gestures, 277 participants), and report that iFAD gestures are two times faster than whole-body gestures and as fast as stylus and finger strokes performed on touchscreens.