Reviewerly/exHarmony huggingface dataset:
The service triad: an empirical study of service robots, customers and frontline employees

Purpose Recent service studies suggest focusing on the service triad consisting of technology-customer-frontline employee (FLE). This study empirically investigates the role of service robots in this service triad, with the aim to understand the augmentation or substitution role of service robots in driving utilitarian and hedonic value and ultimately customer repatronage. Design/methodology/approach In study 1, field data are collected from customers ( n = 108) who interacted with a service robot and FLE in a fast casual dining restaurant. Structural equation modeling (SEM) is used to test hypotheses about the impact of service robots' anthropomorphism, social presence, value perceptions and augmentation opportunities in the service triad. In study 2, empirical data from a scenario-based experimental design ( n = 361) complement the field study by further scrutinizing the interplay between the service robot and FLEs within the service triad. Findings The study provides three important contributions. First, the authors provide empirical evidence for the interplay between different actors in the “customer-FLE-technology” service triad resulting in customer repatronage. Second, the empirical findings advance the service management literature by unraveling the relationship between anthropomorphism and social presence and their effect on perceived value in the service triad. And third, the study identifies utilitarian value of service robots as a driver of customer repatronage in fast casual dining restaurants. Practical implications The results help service managers, service robot engineers and designers, and policy makers to better understand the implications of anthropomorphism, and how the utilitarian value of service robots can offer the potential for augmentation or substitution roles in the service triad. Originality/value Building on existing conceptual and laboratory studies on service robots, this is one of the first field studies on the service triad consisting of service robots – customers – frontline employees. The empirical study on service triads provides evidence for the potential of FLEs to augment service robots that exhibit lower levels of functional performance to achieve customer repatronage. FLEs can do this by demonstrating a high willingness to help and having excellent interactions with customers. This finding advocates the joint service delivery by FLE – service robot teams in situations where service robot technology is not fully optimized.
A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural Network

With the rapid development of deep learning technology, a variety of network models for classification have been proposed, which is beneficial to the realization of intelligent waste classification. However, there are still some problems with the existing models in waste classification such as low classification accuracy or long running time. Aimed at solving these problems, in this paper, a waste classification method based on a multilayer hybrid convolution neural network (MLH-CNN) is proposed. The network structure of this method is similar to VggNet but simpler, with fewer parameters and a higher classification accuracy. By changing the number of network modules and channels, the performance of the proposed model is improved. Finally, this paper finds the appropriate parameters for waste image classification and chooses the optimal model as the final model. The experimental results show that, compared with some recent works, the proposed method has a simpler network structure and higher waste classification accuracy. A large number of experiments in a TrashNet dataset show that the proposed method achieves a classification accuracy of up to 92.6%, which is 4.18% and 4.6% higher than that of some state-of-the-art methods, and proves the effectiveness of the proposed method.
HCovBi-Caps: Hate Speech Detection Using Convolutional and Bi-Directional Gated Recurrent Unit With Capsule Network

Adversaries and anti-social elements have exploited the rapid proliferation of computing technology and online social media in the form of novel security threats, such as fake profiles, hate speech, social bots, and rumors. The hate speech problem on online social networks (OSNs) is also widespread. The existing literature has machine learning approaches for hate speech detection on OSNs. However, the effectiveness of contextual information at different orientations is understudied. This study presents a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Convolutional</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BiGRU</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Capsule</i> network-based deep learning model, HCovBi-Caps, to classify the hate speech. The proposed model is evaluated over two Twitter-based benchmark datasets – DS1(balanced) and DS2(unbalanced) with the best performance of 0.90, 0.80, and 0.84 respectively considering <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precision</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f-score</i> over unbalanced dataset. In terms of training and validation accuracy, the proposed model shows the best performance of 0.93 and 0.90, respectively, over the unbalanced dataset. In comparative evaluation, HCovBi-Caps demonstrates a significantly better performance than state-of-the-art approaches. In addition, HCovBi-Caps shows comparatively better performance over the unbalanced dataset. We also investigate the impact of different hyperparameters on the efficacy of HCovBi-Caps to ascertain the selection of their values. We observed that a higher value of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">routing iterations</i> adversely affects the model performance, whereas a higher value of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">capsule dimension</i> improves the performance.
Scaling up silicon photonic-based accelerators: Challenges and opportunities

Digital accelerators in the latest generation of complementary metal–oxide–semiconductor processes support, multiply, and accumulate (MAC) operations at energy efficiencies spanning 10–100 fJ/Op. However, the operating speed for such MAC operations is often limited to a few hundreds of MHz. Optical or optoelectronic MAC operations on today’s SOI-based silicon photonic integrated circuit platforms can be realized at a speed of tens of GHz, leading to much lower latency and higher throughput. In this Perspective, we study the energy efficiency of integrated silicon photonic MAC circuits based on Mach–Zehnder modulators and microring resonators. We describe the bounds on energy efficiency and scaling limits for N × N optical networks with today’s technology based on the optical and electrical link budget. We also describe research directions that can overcome the current limitations.
Blockchain-Based Trust Management Framework for Cloud Computing-Based Internet of Medical Things (IoMT): A Systematic Review

The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.
Attention mechanism in neural networks: where it comes and where it goes

A long time ago in the machine learning literature, the idea of incorporating a mechanism inspired by the human visual system into neural networks was introduced. This idea is named the attention mechanism, and it has gone through a long development period. Today, many works have been devoted to this idea in a variety of tasks. Remarkable performance has recently been demonstrated. The goal of this paper is to provide an overview from the early work on searching for ways to implement attention idea with neural networks until the recent trends. This review emphasizes the important milestones during this progress regarding different tasks. By this way, this study aims to provide a road map for researchers to explore the current development and get inspired for novel approaches beyond the attention.
Self-Instruct: Aligning Language Models with Self-Generated Instructions

Large "instruction-tuned" language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks.Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model.We introduce SELF-INSTRUCT, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations.Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model.Applying our method to the vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on SUPER-NATURALINSTRUCTIONS, on par with the performance of InstructGPT 001 , 1 which was trained with private user data and human annotations.For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with SELF-INSTRUCT outperforms using existing public instruction datasets by a large margin, leaving only a 5% absolute gap behind InstructGPT 001 .SELF-INSTRUCT provides an almost annotation-free method for aligning pretrained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning. 2
Large language models are few-shot clinical information extractors

A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes.However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations.In this work, we show that large language models, such as InstructGPT (Ouyang et al., 2022), perform well at zero-and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain.Whereas text classification and generation performance have already been studied extensively in such models, here we additionally demonstrate how to leverage them to tackle a diverse set of NLP tasks which require more structured outputs, including span identification, token-level sequence classification, and relation extraction.Further, due to the dearth of available data to evaluate these systems, we introduce new datasets for benchmarking fewshot clinical information extraction based on a manual re-annotation of the CASI dataset (Moon et al., 2014) for new tasks 1 .On the clinical extraction tasks we studied, the GPT-3 systems significantly outperform existing zeroand few-shot baselines.
Automatic Design of Balanced Board Games

AI techniques are already widely used in game software to provide computer-controlled opponents for human players. However, game design is a more-challenging problem than game play. Designers typically expend great effort to ensure that their games are balanced and challenging. Dynamic game-balancing techniques have been developed to modify a game-engine&amp;rsquo;s parameters in response to user play. In this paper we describe a first attempt at using AI techniques to design balanced board games like checkers and Go by modifying the rules of the game, not just the rule parameters. Our approach involves the use of a commercial general game-playing (GGP) engine that plays according to rules that are specified in a general game-definition language. We use a genetic algorithm (GA) to search the space of game rules, looking for turn-based board games that are well balanced, i.e., those that the GGP engine in self-play finds equally hard to win from either side and rarely draws. The GA finds better games than a random-search strategy that uses equivalent computational effort.
Multi-Agent Pathfinding with Simultaneous Execution of Single-Agent Primitives

Multi-agent pathfinding is a challenging combinatorial problem that involves multiple agents moving on a graph from a set of initial nodes to a set of desired goals without inter-agent collisions. Searching the composite space of all agents has exponential complexity and does not scale well. Decoupled methods are more efficient but are generally incomplete. There are, however, polynomial time algorithms, which utilize single or few-agents primitives with completeness guarantees. One limitation of these alternatives is that the resulting solution is sequential, where only one agent moves at a time. Such solutions are of low quality when compared to methods where multiple agents can move simultaneously. This work proposes an algorithm for multi-agent pathfinding that utilizes similar single-agent primitives but allows all agents to move in parallel. The paper describes the algorithm and its properties. Experimental comparisons suggest that the resulting paths are considerably better than sequential ones, even after a post-processing, parallelization step, as well as solutions returned by decoupled and coupled alternatives. The experiments also suggest good scalability and competitive computational performance.
Reliability vs. granularity in discourse annotation: What is the trade-off?

Abstract We report on the results of an annotation experiment comparing naïve and expert coders in a sense disambiguation task consisting in the assignment of function labels to discourse markers (e.g. well, but, I mean ) in spoken French and English using a taxonomy specifically designed for speech. Our qualitative-quantitative assessment of its reliability led us to suggest fundamental revisions of the structure of the taxonomy, striving to find a better balance between reliability and granularity. The resulting model articulates two independent levels of annotation (domains and functions) which, once combined, provide a robust tool for the analysis of discourse markers and relate them to more general functions of spoken language.
Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from the cross-sectional area (CSA) of the internal jugular vein (IJV) from ultrasound images. However, accurate segmentation and tracking of the IJV in ultrasound imaging is a challenging task and is significantly influenced by a number of parameters such as the image quality, shape, and temporal variation. In this paper, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm is applied to 65 ultrasound videos captured from 13 healthy subjects, with each video containing 450 frames. The results show that spatial and temporal adaptation of the energy function significantly improves segmentation performance when compared to current state-of-the-art active contour algorithms.
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates

The stochastic gradient descent (SGD) optimization algorithm is one of the central tools used to approximate solutions of stochastic optimization problems arising in machine learning and, in particular, deep learning applications. It is therefore important to analyze the convergence behavior of SGD. In this article we consider a simple quadratic stochastic optimization problem and establish for every γ,ν∈(0,∞) essentially matching lower and upper bounds for the mean square error of the associated SGD process with learning rates (γnν)n∈N. This allows us to precisely quantify the mean square convergence rate of the SGD method in dependence on the choice of the learning rates.
Probabilistic solving of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:math> -hard problems with bistable nonlinear optical networks

We study theoretically a lattice of locally bistable driven-dissipative nonlinear cavities. The system is found to resemble the classical Ising model and enables its effective simulation. First, we benchmark the performance of driven-dissipative nonlinear cavities for spin-glass problems, and study the scaling of the ground-state-energy deviation and success probability as a function of system size. Next, we show how an effective bias field can be included in an optical model and use it for probabilistic solving of optimization problems. As particular examples we consider NP-hard problems embedded in the Ising model, namely graph partitioning and the knapsack problem. Finally, we confirm that locally bistable polariton networks act as classical optimizers and can potentially provide an improvement within the exponential complexity class.
Confidence Polytopes in Quantum State Tomography

Quantum State Tomography is the task of inferring the state of a quantum system from measurement data. A reliable tomography scheme should not only report an estimate for that state, but also well-justified error bars. These may be specified in terms of confidence regions, i.e., subsets of the state space which contain the system's state with high probability. Here, building upon a quantum generalisation of Clopper-Pearson confidence intervals--a notion known from classical statistics--we present a simple and reliable scheme for generating confidence regions. These have the shape of a polytope and can be computed efficiently. We provide several examples to demonstrate the practical usability of the scheme in experiments.
Adaptive Motion Planning Based on Vehicle Characteristics and Regulations for Off-Road UGVs

In this paper, we propose a novel motion planning method for off-road unmanned ground vehicles, based on three-dimensional (3-D) terrain map information. Previous studies on the motion planning of a vehicle traveling on rough terrain dealt only with a relatively small environment. Furthermore, unique vehicle characteristics were not considered, and it was also impossible to incorporate regulations, such as maintaining driving speed and suppressing posture change. The proposed method enables vehicles to adaptively generate a path by considering vehicle characteristics and the regulations, in a large-scale environment, with rough terrain. A random sampling based scheme was applied to carrying out global path planning, based on a 3-D environmental model. Experimental results showed that the proposed off-road motion planner could generate an appropriate path, which satisfies vehicle characteristics and predefined regulations.
Universal speeded-up adiabatic geometric quantum computation in three-level systems via counterdiabatic driving

Universal speeded-up adiabatic geometric quantum computation~(SAGQC) is studied in $\Lambda$-type three-level system with different coupling cases, i.e., time-dependent detuning, large detuning and one-photon resonance couplings, respectively. In these cases, the counterdiabatic driving method is used to speed up the universal quantum computation. These schemes in $\Lambda$-type three-level system are feasible in experiment because additional unaccessible ground-state-coupling is not needed. Only the shapes and phases of the initial adiabatic classical-field pulse are modified with the aid of effective two-level systems based on the counterdiabatic driving. The speed and robustness against decay of these schemes are discussed and compared. In addition, our work enriches the study of the speeded-up geometric computation in $\Lambda$-type three-level system and can be applied to various experimental platforms with different coupling features.
Unsupervised Learning Helps Supervised Neural Word Segmentation

By exploiting unlabeled data for further performance improvement for Chinese word segmentation, this work makes the first attempt at exploring adding unsupervised segmentation information into neural supervised segmenter. We survey various effective strategies, including extending the character embedding, augmenting the word score and applying multi-task learning, for leveraging unsupervised information derived from abundant unlabeled data. Experiments on standard data sets show that the explored strategies indeed improve the recall rate of out-of-vocabulary words and thus boost the segmentation accuracy. Moreover, the model enhanced by the proposed methods outperforms state-of-theart models in closed test and shows promising improvement trend when adopting three different strategies with the help of a large unlabeled data set. Our thorough empirical study eventually verifies the proposed approach outperforms the widelyused pre-training approach in terms of effectively making use of freely abundant unlabeled data.