Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptive Graph Convolutional Networks

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges in both graph representation learning and domain adaptation over graph structures. In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs. To enable effective graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global consistency for feature aggregation. An attention mechanism is further used to produce a unified representation for each node in different graphs. To facilitate knowledge transfer between graphs, we propose a domain adaptive learning module to optimize three different loss functions, namely source classifier loss, domain classifier loss, and target classifier loss as a whole, thus our model can differentiate class labels in the source domain, samples from different domains, the class labels from the target domain, respectively. Experimental results on real-world datasets in the node classification task validate the performance of our method, compared to state-of-the-art graph neural network algorithms.
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based behavior prediction leverage powerful representation learning approaches to encode items’ sequential relevance in a low-dimensional space, they suffer from several limitations. Firstly, they focus on only utilizing the same type of user behavior for prediction, but ignore the potential of taking other behavior data as auxiliary information. This is particularly crucial when the target behavior is sparse but important (e.g., buying or sharing an item). Secondly, item-to-item relations are modeled separately and locally in one behavior sequence, and they lack a principled way to globally encode these relations more effectively. To overcome these limitations, we propose a novel Multi-relational Graph Neural Network model for Session-based target behavior Prediction, namely MGNN-SPred for short. Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types. Based on MRIG, MGNN-SPred learns global item-to-item relations and further obtains user preferences w.r.t. current target and auxiliary behavior sequences, respectively. In the end, MGNN-SPred leverages a gating mechanism to adaptively fuse user representations for predicting next item interacted with target behavior. The extensive experiments on two real-world datasets demonstrate the superiority of MGNN-SPred by comparing with state-of-the-art session-based prediction methods, validating the benefits of leveraging auxiliary behavior and learning item-to-item relations over MRIG.
Adversarial attacks on deep-learning-based SAR image target recognition

Synthetic aperture radar (SAR) image target recognition has consistently been a research hotspot in the field of radar image interpretation. Compared with traditional target recognition algorithms, SAR target recognition algorithms based on deep learning offer end-to-end feature learning, which can effectively improve the target recognition rate, making them an important method for radar target recognition. However, recent research shows that optical image recognition methods based on deep learning are vulnerable to adversarial examples. In SAR image target recognition, whether adversarial examples exist for deep learning algorithms is still an open question. This paper uses three mainstream algorithms to generate adversarial examples to attack three classical deep learning algorithms for SAR image target recognition. The experiments involve publicly real SAR images for white-box and black-box attacks. The results show that SAR target recognition algorithms based on deep learning are potentially vulnerable to adversarial examples.
AR-Net: Adaptive Attention and Residual Refinement Network for Copy-Move Forgery Detection

In copy-move forgery, the illumination and contrast of tampered and genuine regions are highly consistent, which poses a greater challenge in copy-move forgery detection. In this article, an end-to-end neural network is proposed based on adaptive attention and residual refinement network (AR-Net). Specifically, position and channel attention features are fused by the adaptive attention mechanism to fully capture context information and enrich the representation of features. Second, deep matching is adopted to compute the self-correlation between feature maps, and atrous spatial pyramid pooling fuses the scaled correlation maps to generate the coarse mask. Finally, the coarse mask is optimized through the residual refinement module, which retains the structure of object boundaries. Extensive experiments, evaluated on CASIAII, COVERAGE, and CoMoFoD datasets, demonstrate that the AR-Net has superior performance than state-of-the-art algorithms and can locate tampered and corresponding genuine regions at the pixel level. In addition, AR-Net has high robustness on postprocessing operations, such as noise, blur, and JPEG recompression.
Virtual experience, real consequences: the potential negative emotional consequences of virtual reality gameplay

Abstract As virtual reality (VR) technology enters mainstream markets, it is imperative that we understand its potential impacts on users, both positive and negative. In the present paper, we build on the extant literature’s focus on the physical side effects of VR gameplay (e.g., cybersickness) by focusing on VR’s potential to intensify users’ experiences of negative emotions. We first conducted a preliminary survey to assess users’ emotional responses during VR gameplay, with the results suggesting that certain VR situations can in fact produce intense negative emotional experiences. We then designed an interactive scenario intended to elicit low to moderate amounts of negative emotion, wherein participants played out the scenario in either VR (using the HTC Vive) or on a laptop computer. Compared to the participants who enacted the scenario on the laptop, those in the VR condition reported higher levels of absorption, which in turn increased the intensity of their negative emotional response to the scenario. A follow-up questionnaire administered several hours later revealed that the intensified negative emotions resulting from VR had a significant positive correlation with negative rumination (i.e., harmful self-related thoughts related to distress). These results show that VR gameplay has the potential to elicit strong negative emotional responses that could be harmful for users if not managed properly. We discuss the practical and policy implications of our findings.
An Identity Management and Authentication Scheme Based on Redactable Blockchain for Mobile Networks

More and more users are eager to obtain more comprehensive network services without revealing their private information. Traditionally, in order to access a network, a user is authorized with an identity and corresponding keys, which are generated and managed by the network operator. All users' personally identifying information are centralized stored by the network operator. However, this approach makes users lose the control of their personally identifying information. Users are concerned about who can access these sensitive data and whether they have been compromised. In this paper, we propose a blockchain-based identity management and authentication scheme for mobile networks, where users' identifying information are controlled by the users themselves. Our scheme let users generate their self-sovereign identities (SSIs) and corresponding public keys and private keys. The private key used to authenticate the user's identifying information is only known to the user. We use blockchain to record SSIs and public keys of legitimate user, and adopt chameleon hash to delete illegal users' information on the blockchain, while keeping the block head unchanged. Furthermore, other service providers can obtain the user's SSI and public key and authenticate users by querying the blockchain. Experimental results confirm that our scheme can greatly reduce the revocation overhead and communication overhead.
Smallholder farmers' use of mobile phone services in central Kenya

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Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment

With the advance of mobile edge computing (MEC), the number of edge services running on mobile devices grows explosively. In this situation, it is becoming a necessity to recommend the most suitable edge services to a mobile user from massive candidates, based on the historical quality of service (QoS) data. However, historical QoS is a kind of private data for users, which needs to be protected from privacy disclosure. Currently, researchers often use the Locality-Sensitive Hashing (LSH) technique to achieve the goal of privacy-aware recommendations. However, existing LSH-based methods are only applied to the recommendation scenarios with a single QoS dimension (e.g., response time or throughput), without considering the multi-dimensional QoS (e.g., response time and throughput) ensemble, which narrow the application scope of LSH in privacy-preserving recommendations significantly. Considering this drawback, this paper proposes a multi-dimensional quality ensemble-driven recommendation approach named RecLSH-TOPSIS based on LSH and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) techniques. First, the traditional single-dimensional LSH recommendation approach is extended to be a multi-dimensional one, through which we can obtain a set of candidate services that a user may prefer. Second, we use TOPSIS technique to rank the derived multiple candidate services and return the user an optimal one. At last, a case study is presented to illustrate the feasibility of our proposal to make privacy-preserving edge service recommendations with multiple QoS dimensions.
Accelerating recurrent Ising machines in photonic integrated circuits

Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks such as the Ising problem, which requires finding the ground state spin configuration of an arbitrary Ising graph. Physical Ising machines have recently been developed as an alternative to conventional exact and heuristic solvers; however, these machines typically suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS), using a hybrid scheme combining electronics and silicon-on-insulator photonics, that is capable of converging to the ground state of various four-spin graphs with high probability. The INPRIS results indicate that noise may be used as a resource to speed up the ground state search and to explore larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.
A novel hybrid feature selection method based on dynamic feature importance

Feature selection aims to eliminate unimportant and redundant features or to select effective and interacting features. It is a challenging task to accurately measure the relationships of candidate features, the selected features and categories in the selection process, especially for high-dimensional and small-sample-size data. To this end, a new measure named Dynamic Feature Importance (DFI) is proposed, as well as its corresponding feature selection algorithm named Dynamic Feature Importance based Feature Selection (DFIFS). In order to obtain higher classification accuracy with smaller number of features, a newly Modified-Dynamic Feature Importance based Feature Selection (M-DFIFS) algorithm is developed by combining DFIFS with classical filters. Based on experiments with 14 public high-dimensional datasets, the lately M-DFIFS algorithm shows significantly better performance than five typical filter algorithms in terms of their average accuracy with acceptable computing time. When using random forest as the classifier, M-DFIFS brings a great advantage in the number decrease of selected features. Hence the new feature selection framework “Filter + DFIFS” is verified very effective to solve problems of obtaining high accuracy with a few features.
Frontline Cyborgs at Your Service: How Human Enhancement Technologies Affect Customer Experiences in Retail, Sales, and Service Settings

Human enhancement technology (HET) is advancing a host of industries, yet it remains limited in its retail, sales, and service applications. Soon, however, these technologies appear likely to have notable impacts on customer experiences. To address their potential influences on customer–employee interactions and the customer experience, this article leverages insights about the human–robot continuum to predict that a mid-range approach may be realistic for retail, sales, and service encounters (cf. pure robot range), at least in the near term. Furthermore, the authors detail the likelihood of both beneficial (elevated warmth and competence) and detrimental (dehumanization) mediating effects of HET on customer experiences. They also propose four moderators: information about the HET, gain/loss frames, persuasion ability, and strategic fit between HET and firm orientations. These insights suggest various implications and research directions pertaining to HET, as well as ways that these technologies might be used effectively in different industries.
A comparative assessment of sentiment analysis and star ratings for consumer reviews

Electronic word of mouth (eWOM) is prominent and abundant in consumer domains. Both consumers and product/service providers need help in understanding and navigating the resulting information spaces, which are vast and dynamic. The general tone or polarity of reviews, blogs or tweets provides such help. In this paper, we explore the viability of automatic sentiment analysis (SA) for assessing the polarity of a product or a service review. To do so, we examine the potential of the major approaches to sentiment analysis, along with star ratings, in capturing the true sentiment of a review. We further model contextual factors (specifically, product type and review length) as two moderators affecting SA accuracy. The results of our analysis of 900 reviews suggest that different tools representing the main approaches to SA display differing levels of accuracy, yet overall, SA is very effective in detecting the underlying tone of the analyzed content, and can be used as a complement or an alternative to star ratings. The results further reveal that contextual factors such as product type and review length, play a role in affecting the ability of a technique to reflect the true sentiment of a review.
From Human-Human Collaboration to Human-AI Collaboration

Artificial Intelligent (AI) and Machine Learning (ML) algorithms are coming out of research labs into the real-world applications, and recent research has focused a lot on Human-AI Interaction (HAI) and Explainable AI (XAI). However, Interaction is not the same as Collaboration. Collaboration involves mutual goal understanding, preemptive task co-management and shared progress tracking. Most of human activities today are done collaboratively, thus, to integrate AI into the already-complicated human workflow, it is critical to bring the Computer-Supported Cooperative Work (CSCW) perspective into the root of the algorithmic research and plan for a Human-AI Collaboration future of work. In this panel we ask: Can this future for trusted human-AI collaboration be realized? If so, what will it take? This panel will bring together HCI experts who work on human collaboration and AI applications in various application contexts, from industry and academia and from both the U.S. and China. Panelists will engage the audience through discussion of their shared and diverging visions, and through suggestions for opportunities and challenges for the future of human-AI collaboration.
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

In static monitoring cameras, useful contextual information can stretch far beyond the few seconds typical video understanding models might see: subjects may exhibit similar behavior over multiple days, and background objects remain static. Due to power and storage constraints, sampling frequencies are low, often no faster than one frame per second, and sometimes are irregular due to the use of a motion trigger. In order to perform well in this setting, models must be robust to irregular sampling rates. In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera. Specifically, we propose an attention-based approach that allows our model, Context R-CNN, to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame. We apply Context R-CNN to two settings: (1) species detection using camera traps, and (2) vehicle detection in traffic cameras, showing in both settings that Context R-CNN leads to performance gains over strong baselines. Moreover, we show that increasing the contextual time horizon leads to improved results. When applied to camera trap data from the Snapshot Serengeti dataset, Context R-CNN with context from up to a month of images outperforms a single-frame baseline by 17.9% mAP, and outperforms S3D (a 3d convolution based baseline) by 11.2% mAP.
iTAML: An Incremental Task-Agnostic Meta-learning Approach

Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new task. In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks. In this pursuit, we introduce a novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks. This is ensured by a new meta-update rule which avoids catastrophic forgetting. In comparison to previous meta-learning techniques, our approach is task-agnostic. When presented with a continuum of data, our model automatically identifies the task and quickly adapts to it with just a single update. We perform extensive experiments on five datasets in a class-incremental setting, leading to significant improvements over the state of the art methods (e.g., a 21.3% boost on CIFAR100 with 10 incremental tasks). Specifically, on large-scale datasets that generally prove difficult cases for incremental learning, our approach delivers absolute gains as high as 19.1% and 7.4% on ImageNet and MS-Celeb datasets, respectively.
PaStaNet: Toward Human Activity Knowledge Engine

Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g. <hand, hold, something>, which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/.
Counterfactual Vision and Language Learning

The ongoing success of visual question answering methods has been somwehat surprising given that, at its most general, the problem requires understanding the entire variety of both visual and language stimuli. It is particularly remarkable that this success has been achieved on the basis of comparatively small datasets, given the scale of the problem. One explanation is that this has been accomplished partly by exploiting bias in the datasets rather than developing deeper multi-modal reasoning. This fundamentally limits the generalization of the method, and thus its practical applicability. We propose a method that addresses this problem by introducing counterfactuals in the training. In doing so we leverage structural causal models for counterfactual evaluation to formulate alternatives, for instance, questions that could be asked of the same image set. We show that simulating plausible alternative training data through this process results in better generalization.
Spectre attacks

Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access the victim's memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim's confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side-channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim's process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, such as operating system process separation, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing and side-channel attacks. These attacks represent a serious threat to actual systems because vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. Although makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.