Reviewerly/exHarmony huggingface dataset:
A Tutorial on Internet of Behaviors: Concept, Architecture, Technology, Applications, and Challenges

In his blogs of 2012, Dr. Göte Nyman coined Internet of Behaviors (IoB). In his idea, people’s behaviors are very good predictors of their needs, and hence technology companies can network human behaviors and construct behavior-based systems to deliver more timely, responsive, and intelligent services with privacy protection. In 2020, Gartner ranked IoB first among its top nine strategic technology trends and suggested that IoB would touch almost half of the world population. Built on his core idea of IoB, this paper is the first one to comprehensively, systematically, and deeply introduce the IoB concept, essential features, architecture, enabling technologies, applications, and open research issues. In this paper, we first give a formal definition and classification of IoB and reveal its fundamental difference from Internet of Things (IoT). Then, we propose a five-layer IoB architecture (consisting of behavior perception, behavior networking, behavior computing, service provision, and security/privacy) and provide the in-depth analysis of IoB enabling technologies. Particularly, we discuss functional requirements and possible fields of an IoB address (i.e., a human behavior identifier), present the networking and maintenance approaches to behaviors, explore four important implications of behavior computing, i.e., intention inference, behavior derivation, behavior programming, and behavior-chain optimization, as well as give a decentralized privacy-protection solution to Dr. Nyman’s behavior-identity-separation idea. Next, we deeply investigate potential IoB applications in smart home/transportation/healthcare/business, and human-robot interaction. Finally, we provide insightful discussions on open research issues. This paper should help researchers and practitioners understand IoB quickly and promote future IoB development.
GRID: Gradient Routing With In-Network Aggregation for Distributed Training

As the scale of distributed training increases, it brings huge communication overhead in clusters. Some works try to reduce the communication cost through gradient compression or communication scheduling. However, these methods either downgrade the training accuracy or do not reduce the total transmission amount. One promising approach, called in-network aggregation, is proposed to mitigate the bandwidth bottleneck in clusters by aggregating gradients in programmable hardware (e.g., Intel Tofino switches). However, existing solutions mainly implement in-network aggregation through fixed (or default) routing paths, resulting in load imbalancing and long communication time. To deal with this issue, we propose GRID, the first-of-its-kind work on Gradient Routing with In-network Aggregation for Distributed Training. In the control plane, we present an efficient gradient routing algorithm based on randomized rounding and formally analyze the approximation performance. In the data plane, we realize in-network aggregation by carefully designing the logic of workers and programmable switches. We implement GRID and evaluate its performance on a small-scale testbed consisting of 3 Intel Tofino switches and 9 commodity servers. With a combination of testbed experiments and large-scale simulations, we show that GRID can reduce the communication time by 38.4%–60.1% and speed up distributed training by 17.4%–52.7% compared with state-of-the-art solutions.
Exploiting Explicit and Implicit Item relationships for Session-based Recommendation

The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in [email protected], and 49.10% and 115.29% improvement in [email protected] on Diginetica and LastFM datasets.
SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation

Contrastive-learning-based neural networks have recently been introduced to recommender systems, due to their unique advantage of injecting collaborative signals to model deep representations, and the self-supervision nature in the learning process. Existing contrastive learning methods for recommendations are mainly proposed through introducing augmentations to the user-item (U-I) bipartite graphs. Such a contrastive learning process, however, is susceptible to bias towards popular items and users, because higher-degree users/items are subject to more augmentations and their correlations are more captured. In this paper, we advocate a <u>S</u>iamese <u>G</u>raph <u>C</u>ontrastive <u>C</u>onsensus <u>L</u>earning (SGCCL) framework, to explore intrinsic correlations and alleviate the bias effects for personalized recommendation. Instead of augmenting original U-I networks, we introduce siamese graphs, which are homogeneous relations of user-user (U-U) similarity and item-item (I-I) correlations. A contrastive consensus optimization process is also adopted to learn effective features for user-item ratings, user-user similarity, and item-item correlation. Finally, we employ the self-supervised learning coupled with the siamese item-item/user-user graph relationships, which ensures unpopular users/items are well preserved in the embedding space. Different from existing studies, SGCCL performs well on both overall and debiasing recommendation tasks resulting in a balanced recommender. Experiments on four benchmark datasets demonstrate that SGCCL outperforms state-of-the-art methods with higher accuracy and greater long-tail item/user exposure.
Leveling the Playing Field: A Comparative Reevaluation of Unmodified Eye Tracking as an Input and Interaction Modality for VR

In this study, we establish a much-needed baseline for evaluating eye tracking interactions using an eye tracking enabled Meta Quest 2 VR headset with 30 participants. Each participant went through 1098 targets using multiple conditions representative of AR/VR targeting and selecting tasks, including both traditional standards and those more aligned with AR/VR interactions today. We use circular white world-locked targets, and an eye tracking system with sub-1-degree mean accuracy errors running at approximately 90Hz. In a targeting and button press selection task, we, by design, compare completely unadjusted, cursor-less, eye tracking with controller and head tracking, which both had cursors. Across all inputs, we presented targets in a configuration similar to the ISO 9241-9 reciprocal selection task and another format with targets more evenly distributed near the center. Targets were laid out either flat on a plane or tangent to a sphere and rotated toward the user. Even though we intended this to be a baseline study, we see unmodified eye tracking, without any form of a cursor, or feedback, outperformed the head by 27.9% and performed comparably to the controller (5.63% decrease) in throughput. Eye tracking had improved subjective ratings relative to head in Ease of Use, Adoption, and Fatigue (66.4%, 89.8%, and 116.1% improvements, respectively) and had similar ratings relative to the controller (reduction by 4.2%, 8.9%, and 5.2% respectively). Eye tracking had a higher miss percentage than controller and head (17.3% vs 4.7% vs 7.2% respectively). Collectively, the results of this baseline study serve as a strong indicator that eye tracking, with even minor sensible interaction design modifications, has tremendous potential in reshaping interactions in next-generation AR/VR head mounted displays.
Single-Photon-Memory Measurement-Device-Independent Quantum Secure Direct Communication—Part II: A Practical Protocol and its Secrecy Capacity

In Part I of this two-part letter on single-photonmemory measurement-device-independent quantum secure direct communication (SPMQC), we reviewed the fundamentals and evolution of quantum secure direct communication (QSDC).In this Part II, we propose a practical protocol and analyze its secrecy capacity.In order to eliminate the security loopholes resulting from practical detectors, the measurementdevice-independent (MDI) QSDC protocol has been proposed.However, block-based transmission of quantum states is utilized in MDI-QSDC, which requires practical quantum memory that is still unavailable at the time of writing.For circumventing this impediment, we propose the SPMQC protocol for dispensing with high-performance quantum memory.The performance of the proposed protocol is characterized by simulations considering realistic experimental parameters, and the results show that it is feasible to implement SPMQC by relying on existing technology.
How Virtual Hand Representations Affect the Perceptions of Dynamic Affordances in Virtual Reality

User representations are critical to the virtual experience, and involve both the input device used to support interactions as well as how the user is virtually represented in the scene. Inspired by previous work that has shown effects of user representations on the perceptions of relatively static affordances, we attempt to investigate how end-effector representations affect the perceptions of affordances that dynamically change over time. Towards this end, we empirically evaluated how different virtual hand representations affect users' perceptions of dynamic affordances in an object retrieval task wherein users were tasked with retrieving a target from a box for a number of trials while avoiding collisions with its moving doors. We employed a 3 (virtual end-effector representation) X 13 (frequency of moving doors) X 2 (target object size) multi-factorial design, manipulating the input modality and its concomitant virtual end-effector representation as a between-subjects factor across three experimental conditions: (1) Controller (using a controller represented as a virtual controller); (2) Controller-hand (using a controller represented as a virtual hand); (3) Glove (using a hand tracked hi-fidelity glove represented as a virtual hand). Results indicated that the controller-hand condition produced lower levels of performance than both the other conditions. Furthermore, users in this condition exhibited a diminished ability to calibrate their performance over trials. Overall, we find that representing the end-effector as a hand tends to increase embodiment but can also come at the cost of performance, or an increased workload due to a discordant mapping between the virtual representation and the input modality used. It follows that VR system designers should carefully consider the priorities and target requirements of the application being developed when choosing the type of end-effector representation for users to embody in immersive virtual experiences.
The Impact of Avatar and Environment Congruence on Plausibility, Embodiment, Presence, and the Proteus Effect in Virtual Reality

Many studies show the significance of the Proteus effect for serious virtual reality applications. The present study extends the existing knowledge by considering the relationship (congruence) between the self-embodiment (avatar) and the virtual environment. We investigated the impact of avatar and environment types and their congruence on avatar plausibility, sense of embodiment, spatial presence, and the Proteus effect. In a 2×2 between-subjects design, participants embodied either an avatar in sports- or business wear in a semantic congruent or incongruent environment while performing lightweight exercises in virtual reality. The avatar-environment congruence significantly affected the avatar's plausibility but not the sense of embodiment or spatial presence. However, a significant Proteus effect emerged only for participants who reported a high feeling of (virtual) body ownership, indicating that a strong sense of having and owning a virtual body is key to facilitating the Proteus effect. We discuss the results assuming current theories of bottom-up and top-down determinants of the Proteus effect and thus contribute to understanding its underlying mechanisms and determinants.
Lossy Compression With Universal Distortion

We consider a novel variant of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -semifaithful lossy coding in which the distortion measure is revealed only to the encoder and only at run-time, as well as an extension of it in which the distortion constraint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is also revealed at run-time. Two forms of rate redundancy are used to analyze the performance, and achievability results of both a pointwise and minimax nature are demonstrated. The first coding scheme uses ideas from VC dimension and growth functions, the second uses appropriate quantization of the space of distortion measures, and the third relies on a random coding argument.
Collaborative Differentially Private Federated Learning Framework for the Prediction of Diabetic Retinopathy

The diagnosis of diabetic retinopathy may be streamlined and expedited with the help of deep learning, which is an efficient way to help an eye specialist examine the enormous amount of retinal images. For these strategies to be effective, big datasets must be consolidated and used for training. Medical data privacy laws frequently make it impossible to gather and share patient data on a single system. In this paper, we introduce a collaborative differentially private federated learning system that enables deep learning image analysis without transferring patient data between healthcare organizations. We investigated four different machine learning algorithms—AlexNet, ResNet50, SqueezeNet1.1, and VGG16—for varying amounts of noise using a dataset of 35120 retina images divided into five classes—No Diabetic Retinopathy, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). Our ResNet50 model outperformed the state-of-the-art diabetic retinopathy prediction models with an accuracy 83.05 % when we added no noise, and with an accuracy 79.35% with a noise multiplier of 8.0. By including our checkpoint techniques, we have reduced the total communication overhead by 49 % when compared to federated learning without checkpoints.
Rule Based Fuzzy Computing Approach on Self-Supervised Sentiment Polarity Classification with Word Sense Disambiguation in Machine Translation for Hindi Language

With increasing globalization, communication among people of diverse cultural backgrounds is also taking place to a very large extent in the present era. Issues like language diversity in various parts of the world can lead to hindrance in communication. The usage of social media and user-generated material has grown at an exponential rate and existing supervised sentiment polarity classification techniques need labelling for the training dataset. In this study, two problems have been analyzed. First, sentiment analysis of the Twitter dataset and sense disambiguation of morphologically rich Hindi language. A rule-based fuzzy logics-based system for self-supervised sentiment classification was used to compute and analyze the self-supervised or completely unsupervised sentiment categorization of a social-media dataset using three types of lexicons. The combination of fuzzy with three different types of lexicons gives sentiment analysis a new path. The unsupervised fuzzy rules integrate the fuzziness of both negative as well as positive scores, and fuzzy logic-based systems can cope with ambiguity and vagueness. The fuzzy-system uses an unsupervised/self-supervised fuzzy rule-based technique to identify text using natural language processing (NLP) and sense of word. We compared the results of fuzzy rule based self-supervised sentiment classification by using three types of lexicons on five different datasets, with unsupervised as well as supervised sentiment classification techniques. Second, using cross-lingual sense embedding rather than cross-lingual word embedding resolves the ambiguity issue. The word sense embeddings are produced for the source languages to learn multiple or various senses of the words. Different evaluation metrics depict an improved performance for English-Hindi language.
Exploring the potential of blockchain-enabled lean automation in supply chain management: a systematic literature review, classification taxonomy, and future research agenda

The purpose of this study is to evaluate how Blockchain Technology (BCT) can support the implementation of Lean Automation. We conducted a systematic literature review to understand how BCT is being implemented in the supply chain management (SCM) domain and to evaluate how this technology can be used to reduce inefficiencies in supply chains. Firstly, we developed a holistic taxonomy of wastes to identify the most common non-value activities. Then, both inductive and deductive content analyses were performed, the latter being coded using the taxonomy. Our findings identified the most common BCT-based application themes in SCM and ways that this technology can be used to support future implementation of Blockchain-enabled Lean Automation (B-eLA). Additionally, we proposed a future research agenda. The study provides important contributions at the intersection between BCT, lean production, and Industry 4.0 within the context of SCM and seeks to exploit BCT’s potential to improve businesses’ efficiency, effectiveness and productivity.
Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

Despite its dominance over the past three decades, model-centric AI has recently come under heavy criticism in favor of data-centric AI. Indeed, both promise to improve the performance of AI systems, yet with converse points of focus. While the former successively upgrades a devised model (algorithm/code), holding the amount and type of data used in model training fixed, the latter enhances the quality of deployed data continuously, paying less attention to further model upgrades. Rather than favoring either of the two approaches, this paper reconciles data-centric AI with model-centric AI. In so doing, we connect current AI to the field of cybersecurity and natural language inference, and through the phenomena of ‘adversarial samples’ and ‘hypothesis-only biases’, respectively, showcase the limitations of model-centric AI in terms of algorithmic stability and robustness. Further, we argue that overcoming the alleged limitations of model-centric AI may well require paying extra attention to the alternative data-centric approach. However, this should not result in reducing interest in model-centric AI. Our position is supported by the notion that successful ‘problem solving’ requires considering both the way we act upon things (algorithm) as well as harnessing the knowledge derived from data of their states and properties.
A semantics of face emoji in discourse

Abstract This paper presents an analysis of face emoji (disc-shaped pictograms with stylized facial expressions) that accompany written text. We propose that there is a use of face emoji in which they comment on a target proposition expressed by the accompanying text, as opposed to making an independent contribution to discourse. Focusing on positively valenced and negatively valenced emoji (which we gloss as happy and unhappy , respectively), we argue that the emoji comment on how the target proposition bears on a contextually provided discourse value endorsed by the author. Discourse values embody what an author desires, aspires to, wishes for, or hopes for. Our analysis derives a range of non-trivial generalizations, including (i) ordering restrictions with regards to the placement of emoji and text, (ii) cases of apparent mixed emotions, and (iii) cases where the lexical content of the accompanying text influences the acceptability of a face emoji.
3inGAN: Learning a 3D Generative Model from Images of a Self-similar Scene

We introduce 3 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IN</inf> GAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D “remixes” of a given scene, by mapping spatial latent codes into a 3D volumetric representation, which can subsequently be rendered from arbitrary views using physically based volume rendering. By construction, the generated scenes remain view-consistent across arbitrary camera configurations, without any flickering or spatio-temporal artifacts. During training, we employ a combination of 2D, obtained through differentiable volume tracing, and 3D Generative Adversarial Network (GAN) losses, across multiple scales, enforcing realism on both its 2D renderings and its 3D structure. We show results on semi-stochastic scenes of varying scale and complexity, obtained from real and synthetic sources. We demonstrate, for the first time, the feasibility of learning plausible view-consistent 3D scene variations from a single exemplar scene and provide qualitative and quantitative comparisons against two recent related methods. Code and data for the paper are available at https://geometry.cs.ucl.ac.uk/group_website/projects/2022/3inGAN/.
The affective, behavioural and cognitive outcomes of agile project management: A preliminary <scp>meta‐analysis</scp>

Abstract Agile project management (APM) refers to a set of project management frameworks that are increasingly common in a range of industries. Even though the positive effects of APM for employees and organizations are widely assumed, no systematic account of affective, behavioural and cognitive outcomes appears in the academic literature. In this pre‐registered meta‐analysis, we examine the effects of APM based on K = 40 records (with k = 41 independent studies, N = 73,825). Our analyses indicate the beneficial effects of APM across outcomes: For the affective outcomes of job satisfaction, affective strain and organizational commitment, the effect sizes were, on average, small. For the behavioural outcomes of performance and innovative behaviour, the effect sizes were medium to large. For the cognitive outcome of psychological empowerment, the results of our meta‐analysis suggest a medium effect. Furthermore, we explore the moderating effects of contextual (team size, occupational groups, culture) and methodological boundary conditions (publication status, study design, levels of analysis, study quality). Compared to studies conducted with software developers, the effect sizes are stronger in other occupations, such as manufacturing, health care and logistics. Moreover, stronger effect sizes are found in published than in unpublished records. We conclude by discussing the state of quantitative APM research and highlight avenues for future study.