Reviewerly/exHarmony huggingface dataset:
When Machine Learning Meets Privacy in 6G: A Survey

The rapid-developing Artificial Intelligence (AI) technology, fast-growing network traffic, and emerging intelligent applications (e.g., autonomous driving, virtual reality, etc.) urgently require a new, faster, more reliable and flexible network form. At this time, researchers in both industry and academia have turned their attention to the sixth generation (6G) communication networks. In the 6G vision, various intelligent application scenarios that utilize Machine Learning (ML) technology (the most important branch of AI) will bring rich heterogeneous connections, as well as massive information storage and operations. When ML meets 6G, new opportunities will emerge along with numerous privacy challenges. On one hand, a secure ML structure, or the correct application of ML, can protect privacy in 6G. On the other hand, ML may be attacked or abused, resulting in privacy violation. It is worth noting that the alliance between 6G and ML may also be a double-edged sword in many cases, rather than absolutely infringe or protect privacy. Therefore, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of ML and privacy in 6G, with a view to further promoting the development of 6G and privacy protection technologies.
Reducto

To cope with the high resource (network and compute) demands of real-time video analytics pipelines, recent systems have relied on frame filtering. However, filtering has typically been done with neural networks running on edge/backend servers that are expensive to operate. This paper investigates on-camera filtering, which moves filtering to the beginning of the pipeline. Unfortunately, we find that commodity cameras have limited compute resources that only permit filtering via frame differencing based on low-level video features. Used incorrectly, such techniques can lead to unacceptable drops in query accuracy. To overcome this, we built Reducto, a system that dynamically adapts filtering decisions according to the time-varying correlation between feature type, filtering threshold, query accuracy, and video content. Experiments with a variety of videos and queries show that Reducto achieves significant (51-97% of frames) filtering benefits, while consistently meeting the desired accuracy.
Systematic Review: Trust-Building Factors and Implications for Conversational Agent Design

Minjin Rheua*, Ji Youn Shina, Wei Penga & Jina Huh-Yooba Media and Information, Michigan State University, East Lansing, Michigan, USAb Department of Information Science, Drexel University, Philadelphia, Pennsylvania, USAMinjin Rheu is a Ph.D. candidate in the Department of Media and Information at Michigan State University. Her research delves into how specific designs or features of communication technology influences users’ motivational and cognitive processes through which people change or adopt their health and prosocial behaviors.Ji Youn Shin is a Ph.D. student in the Department of Media and Information at Michigan State University. Her research includes human-computer interaction, human-centered design, and health communication. She designs and evaluates technologies for supporting individuals’ health and wellness in various health contexts, including family caregiving and pediatric chronic illnesses.Wei Peng is a Professor in the Department of Media and Information, Michigan State University. Her research focuses on the psychological and social mechanisms of behavior change and their application in the design of interactive media for health and wellness promotion.Jina Huh-Yoo is an Assistant Professor of Human-Computer Interaction at Drexel University's College of Computing & Informatics. Her research areas include human-computer interaction, health informatics, and research ethics of health informatics technologies. She was a PI of NIH and NSF-funded projects in promoting health and wellness through consumer-facing technologies.
Technology-related knowledge, skills, and attitudes of pre- and in-service teachers: The current situation and emerging trends

This is the introductory article for the special issue "Technology-related knowledge, skills, and attitudes of pre- and in-service teachers". It (1) specifies the concept of technology-related knowledge, skills, and attitudes (KSA) of teachers, (2) presents how these KSA are currently assessed, and (3) outlines ways of fostering them among pre- and in-service teachers. The eight articles in the special issue are structured accordingly, and we demonstrate how they contribute to knowledge in these three areas. Moreover, we show how the afterword to the special issue widens the perspective on technology integration by taking into account systems and cultures of practice. Due to their quantitative empirical nature, the eight articles investigate technology at the current state of the art. However, the potential of artificial intelligence has not yet been fully exploited in education. We provide an outlook on potential developments and their implications on teachers' technology-related KSA. To this end, we introduce the concept of augmentation strategies.
Blending the real world and the virtual world: Exploring the role of flow in augmented reality experiences

This study examines the ‘sweet spot’ of augmented reality (AR) through the lens of flow theory and has two primary objectives. First, the study seeks to determine whether investment in AR technologies is warranted by exploring flow in both an AR and a traditional shopping context. Second, the study examines the unique capabilities of AR to facilitate an enhanced state of flow and its positive influence across several consumer outcomes. To achieve these objectives, a commercially available AR app was utilized to conduct an online, between-subjects experiment with 500 participants. Partial least squares structural equation modeling was used to analyze the predictor variables of consumer flow, as well as the impact of flow across several consumer outcomes. Managerial and practical conclusions for marketers and designers are provided to support the creation and execution of AR technology within consumer contexts.
Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module

Security inspection often deals with a piece of baggage or suitcase where objects are heavily overlapped with each other, resulting in an unsatisfactory performance for prohibited items detection in X-ray images. In the literature, there have been rare studies and datasets touching this important topic. In this work, we contribute the first high-quality object detection dataset for security inspection, named Occluded Prohibited Items X-ray (OPIXray) image benchmark. OPIXray focused on the widely-occurred prohibited item "cutter", annotated manually by professional inspectors from the international airport. The test set is further divided into three occlusion levels to better understand the performance of detectors. Furthermore, to deal with the occlusion in X-ray images detection, we propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors. Despite the heavy occlusion in X-ray imaging, shape appearance of objects can be preserved well, and meanwhile different materials visually appear with different colors and textures. Motivated by these observations, our DOAM simultaneously leverages the different appearance information of the prohibited item to generate the attention map, which helps refine feature maps for the general detectors. We comprehensively evaluate our module on the OPIXray dataset, and demonstrate that our module can consistently improve the performance of the state-of-the-art detection methods such as SSD, FCOS, etc, and significantly outperforms several widely-used attention mechanisms. In particular, the advantages of DOAM are more significant in the scenarios with higher levels of occlusion, which demonstrates its potential application in real-world inspections. The OPIXray benchmark and our model are released at https://github.com/OPIXray-author/OPIXray.
Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction

Head pose estimation is an important way to understand human attention in the human-computer interaction. In this paper, we propose a novel anisotropic angle distribution learning (AADL) network for head pose estimation task. Firstly, two key findings are revealed as following: 1) Head pose image variations are different at the yaw and pitch directions with the same pose angle increasing on a fixed central pose; 2) With the fixed angle interval increasing, the image variations increase firstly and then decrease in yaw angle direction. Then, the maximum a posterior technology is employed to construct the head pose estimation network, which includes three parts, such as convolutional layer, covariance pooling layer and output layer. In the output layer, the labels are constructed as the anisotropic angle distributions on the basis of two key findings. And the anisotropic angle distributions are fitted by the 2D Gaussian-like distributions (groundtruth labels). Furthermore, the Kullback-Leibler divergence is selected to measure the predication label and the groundtruth one. The features of head pose images are perceived at the AADL-based convolutional neural network in an end-to-end manner. Experimental results demonstrate that the developed AADL-based labels have several advantages, such as robustness for head pose image missing, insensitivity for the motion blur. Moreover, the proposed method has achieved good performance compared to several state-of-the-art methods on the Pointing’04 and CAS_PEAL_R1 databases.
Superconducting Nanowires for Single‐Photon Detection: Progress, Challenges, and Opportunities

Abstract Single‐photon detectors and nanoscale superconducting devices are two major candidates for realizing quantum technologies. Superconducting‐nanowire single‐photon detectors (SNSPDs) comprise these two solid‐state and optic aspects enabling high‐rate (1.3 Gb s −1 ) quantum key distribution over long distances (&gt;400 km), long‐range quantum communication (&gt;1200 km), as well as space communication (239 000 miles). The attractiveness of SNSPDs stems from competitive performance in the four single‐photon relevant characteristics at wavelengths ranges from UV to the mid‐IR: high detection efficiency, low false‐signal rate, low uncertainty in photon time arrival, and fast reset time. However, to date, these characteristics cannot be optimized simultaneously. In this review, the mechanisms that govern these four characteristics are presented, and it is demonstrated how they are affected by material properties and device design as well as by the operating conditions, allowing aware optimization of SNSPDs. Based on the evolution in the existing literature and state of the art, it is proposed how to choose or design the material and device for optimizing SNSPD performance, while possible future opportunities in the SNSPD technology are also highlighted.
BigNAS: Scaling up Neural Architecture Search with Big Single-Stage Models

Neural architecture search (NAS) has shown promising results discovering models that are both accurate and fast. For NAS, training a one-shot model has become a popular strategy to rank the relative quality of different architectures (child models) using a single set of shared weights. However, while one-shot model weights can effectively rank different network architectures, the absolute accuracies from these shared weights are typically far below those obtained from stand-alone training. To compensate, existing methods assume that the weights must be retrained, finetuned, or otherwise post-processed after the search is completed. These steps significantly increase the compute requirements and complexity of the architecture search and model deployment. In this work, we propose BigNAS, an approach that challenges the conventional wisdom that post-processing of the weights is necessary to get good prediction accuracies. Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs. Our discovered model family, BigNASModels, achieve top-1 accuracies ranging from 76.5% to 80.9%, surpassing state-of-the-art models in this range including EfficientNets and Once-for-All networks without extra retraining or post-processing. We present ablative study and analysis to further understand the proposed BigNASModels.
Privacy and Security Issues in Deep Learning: A Survey

Deep Learning (DL) algorithms based on artificial neural networks have achieved remarkable success and are being extensively applied in a variety of application domains, ranging from image classification, automatic driving, natural language processing to medical diagnosis, credit risk assessment, intrusion detection. However, the privacy and security issues of DL have been revealed that the DL model can be stolen or reverse engineered, sensitive training data can be inferred, even a recognizable face image of the victim can be recovered. Besides, the recent works have found that the DL model is vulnerable to adversarial examples perturbed by imperceptible noised, which can lead the DL model to predict wrongly with high confidence. In this paper, we first briefly introduces the four types of attacks and privacy-preserving techniques in DL. We then review and summarize the attack and defense methods associated with DL privacy and security in recent years. To demonstrate that security threats really exist in the real world, we also reviewed the adversarial attacks under the physical condition. Finally, we discuss current challenges and open problems regarding privacy and security issues in DL.
A Unified Form of Fuzzy C-Means and K-Means algorithms and its Partitional Implementation

This paper proposes as an element of novelty the Unified Form (UF) clustering algorithm, which treats Fuzzy C-Means (FCM) and K-Means (KM) algorithms as a single configurable algorithm. UF algorithm was designed to facilitate the FCM and KM algorithms software implementation by offering a solution to implement a single algorithm, which can be configured to work as FCM or KM. The second element of novelty of this paper is the Partitional Implementation of Unified Form (PIUF) algorithm, which is built upon the UF algorithm and designed to solve in an elegant manner the challenges of processing large datasets in a sequential manner and the scalability of the UF algorithm for processing datasets of any size. PIUF algorithm has the advantage of overcoming any possible hardware limitations that can occur if large volumes of data are processed (required to be stored, loaded in memory and processed by a certain specified computational system). PIUF algorithm is designed and formulated to be used on a single machine if the processed dataset is very big and it cannot be entirely loaded in the memory; at the same time it can be scaled to multiple processing nodes for reducing the processing time required to find the optimal solution. UF and PIUF algorithms are implemented and validated in BigTim platform, which is a distributed platform developed by the authors, and offers support for processing various datasets in a parallel manner but they can be implemented in any other data processing platforms. The Iris dataset is considered and next modified to obtain different datasets of different sizes in order to test the algorithms implementations in BigTim platform in different configurations. The analysis of PIUF algorithm and the comparison with FCM, KM and DBSCAN clustering algorithms are carried out using two performance indices; three performance indices are employed to evaluate the quality of the obtained clusters.
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our reinforcement learning approach, which we call BADGR, is an end-to-end learning-based mobile robot navigation system that can be trained with autonomously-labeled off-policy data gathered in real-world environments, without any simulation or human supervision. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://sites.google.com/view/badgr.
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due to the vague definition o f a nomalies and said data's frequent lack of labels and highly complex temporal correlations. Current state-of-the-art unsupervised machine learning methods for anomaly detection suffer from scalability and portability issues, and may have high false positive rates. In this paper, we propose TadGAN, an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs). To capture the temporal correlations of time series distributions, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. TadGAN is trained with cycle consistency loss to allow for effective time-series data reconstruction. We further propose several novel methods to compute reconstruction errors, as well as different approaches to combine reconstruction errors and Critic outputs to compute anomaly scores. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one. We compare our approach to 8 baseline anomaly detection methods on 11 datasets from multiple reputable sources such as NASA, Yahoo, Numenta, Amazon, and Twitter. The results show that our approach can effectively detect anomalies and outperform baseline methods in most cases (6 out of 11). Notably, our method has the highest averaged F1 score across all the datasets. Our code is open source and is available as a benchmarking tool.
Fertile LAND: Pricing non-fungible tokens

The current popularity of non-fungible token (NFT) markets is one of the most notable public successes of blockchain technology. NFTs are blockchain-traded rights to any digital asset; including images, videos, music, even the parts of virtual worlds. As a first study of NFT pricing, we explore the pricing of parcels of virtual real estate in the largest blockchain virtual world, Decentraland; an NFT simply termed LAND. We show a LAND price series characterised by both inefficiency and a steady rise in value.
Estimating the Impact of “Humanizing” Customer Service Chatbots

In this work, we investigate how applying human-like characteristics to customer service chatbots can influence retail outcomes. This is an important managerial question as creating effective chatbot experiences through messaging platforms has proven difficult for organizations. Often, chatbot developers apply characteristics such as giving a chatbot a human name, adding humor, and so on, without knowing how these features influence end user behavior. Implementing a field experiment in collaboration with a dual channel clothing retailer based in the United States, we automate a used clothing buy-back process, such that individuals engage with the retailer's autonomous chatbot to describe the used clothes they wish to sell, obtain a cash offer, and (if they accept) print a shipping label to finalize the transaction. We provide evidence that, in this retail setting, anthropomorphism is beneficial for transaction outcomes, but that it also leads to significant increases in consumers’ sensitivity to the offer amount. We argue that the latter effect occurs because, as a chatbot becomes more human-like, consumers shift to a fairness evaluation or negotiating mindset.
Realization of High-Fidelity CZ and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Z</mml:mi><mml:mi>Z</mml:mi></mml:math> -Free iSWAP Gates with a Tunable Coupler

High-fidelity two-qubit gates at scale are a key requirement to realize the full promise of quantum computation and simulation. The advent and use of coupler elements to tunably control two-qubit interactions has improved operational fidelity in many-qubit systems by reducing parasitic coupling and frequency crowding issues. Nonetheless, two-qubit gate errors still limit the capability of near-term quantum applications. The reason, in part, is the existing framework for tunable couplers based on the dispersive approximation does not fully incorporate three-body multi-level dynamics, which is essential for addressing coherent leakage to the coupler and parasitic longitudinal ($ZZ$) interactions during two-qubit gates. Here, we present a systematic approach that goes beyond the dispersive approximation to exploit the engineered level structure of the coupler and optimize its control. Using this approach, we experimentally demonstrate CZ and $ZZ$-free iSWAP gates with two-qubit interaction fidelities of $99.76 \pm 0.07$% and $99.87 \pm 0.23$%, respectively, which are close to their $T_1$ limits.
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth

Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of this extra signal, thus ignoring valuable information that could be used to improve the predicted depth. Those that do, either use computationally expensive test-time refinement techniques or off-the- shelf recurrent networks, which only indirectly make use of the geometric information that is inherently available.We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. Taking inspiration from multi-view stereo, we propose a deep end-to-end cost volume based approach that is trained using self-supervision only. We present a novel consistency loss that encourages the network to ignore the cost volume when it is deemed unreliable, e.g. in the case of moving objects, and an augmentation scheme to cope with static cameras. Our detailed experiments on both KITTI and Cityscapes show that we outperform all published self-supervised baselines, including those that use single or multiple frames at test time.