Reviewerly/exHarmony huggingface dataset:
How Sincere is an Apology? Recovery Satisfaction in A Robot Service Failure Context

This study examined the effects of service recovery entities (i.e., robot-generated text, robot-generated voice, and human service employee) on recovery satisfaction through the mediation of perceived sincerity. This study also tested the moderating role of the need for human interaction. The results of this study suggest that people perceive service recovery provided by a human to be more sincere than that provided by a service robot, thereby leading to higher levels of satisfaction. Moreover, this effect is stronger for those with a stronger need for human interaction. However, differences in perceived sincerity and recovery satisfaction were not identified between the use of service robots generating either text or a voice to express an apology. This study contributes to the theoretical discussion on ways to enhance customer experiences in robot service environments and provides insights that can guide service management in the hospitality and tourism industry.
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models. Meanwhile, when training state-of-the-art personal recommendation models, which consume the highest number of compute cycles at our large-scale datacenters, the use of GPUs came with various challenges due to having both compute-intensive and memory-intensive components. GPU performance and efficiency of these recommendation models are largely affected by model architecture configurations such as dense and sparse features, MLP dimensions. Furthermore, these models often contain large embedding tables that do not fit into limited GPU memory. The goal of this paper is to explain the intricacies of using GPUs for training recommendation models, factors affecting hardware efficiency at scale, and learnings from a new scale-up GPU server design, Zion.
Accurate LPI Radar Waveform Recognition With CWD-TFA for Deep Convolutional Network

Automotive radars, with a widespread emergence in the last decade, have faced various jamming attacks. Utilizing low probability of intercept (LPI) radar waveforms, as one of the essential solutions, demands an accurate waveform recognizer at the intercept receiver. Numerous conventional approaches have been studied for LPI radar waveform recognition, but their performance is inadequate under channel condition deterioration. In this letter, by exploiting deep learning (DL) to capture intrinsic radio characteristics, we propose a convolutional neural network (CNN), namely LPI-Net, for automatic radar waveform recognition. In particular, radar signals are first analyzed by a time-frequency analysis using the Choi-Williams distribution. Subsequently, LPI-Net, primarily consisting of three sophisticated modules, is built to learn the representational features of time-frequency images, in which each module is constructed with a preceding maps collection to gain feature diversity and a skip-connection to maintain informative identity. Simulation results show that LPI-Net achieves the 13-waveform recognition accuracy of over 98% at 0 dB SNR and further performs better than other deep models.
Digital Pathology Initiatives and Experience of a Large Academic Institution During the Coronavirus Disease 2019 (COVID-19) Pandemic

Context.— Pathology practices have begun integrating digital pathology tools into their routine workflow. During 2020, the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged as a pandemic, causing a global health crisis that significantly affected the world population in several areas, including medical practice, and pathology was no exception. Objective.— To summarize our experience in implementing digital pathology for remote primary diagnosis, education, and research during this pandemic. Design.— We surveyed our pathologists (all subspecialized) and trainees to gather information about their use of digital pathology tools before and during the pandemic. Quality assurance and slide distribution data were also examined. Results.— During the pandemic, the widespread use of digital tools in our institution allowed a smooth transition of most clinical and academic activities into remote with no major disruptions. The number of pathologists using whole slide imaging (WSI) for primary diagnosis increased from 20 (62.5%) to 29 (90.6%) of a total of 32 pathologists, excluding renal pathology and hematopathology, during the pandemic. Furthermore, the number of pathologists exclusively using whole slide imaging for primary diagnosis also increased from 2 (6.3%) to 5 (15.6%) during the pandemic. In 35 (100%) survey responses from attending pathologists, 21 (60%) reported using whole slide imaging for remote primary diagnosis following the Centers for Medicare and Medicaid Services waiver. Of these 21 pathologists, 18 (86%) responded that if allowed, they will continue using whole slide imaging for remote primary diagnosis after the pandemic. Conclusions.— The pandemic served as a catalyst to pathologists adopting a digital workflow into their daily practice and realizing the logistic and technical advantages of such tools.
A Deeper Look at Machine Learning-Based Cryptanalysis

At CRYPTO’19, Gohr proposed a new cryptanalysis strategy based on the utilisation of machine learning algorithms. Using deep neural networks, he managed to build a neural based distinguisher that surprisingly surpassed state-of-the-art cryptanalysis efforts on one of the versions of the well studied NSA block cipher SPECK (this distinguisher could in turn be placed in a larger key recovery attack). While this work opens new possibilities for machine learning-aided cryptanalysis, it remains unclear how this distinguisher actually works and what information is the machine learning algorithm deducing. The attacker is left with a black-box that does not tell much about the nature of the possible weaknesses of the algorithm tested, while hope is thin as interpretability of deep neural networks is a well-known difficult task. In this article, we propose a detailed analysis and thorough explanations of the inherent workings of this new neural distinguisher. First, we studied the classified sets and tried to find some patterns that could guide us to better understand Gohr’s results. We show with experiments that the neural distinguisher generally relies on the differential distribution on the ciphertext pairs, but also on the differential distribution in penultimate and antepenultimate rounds. In order to validate our findings, we construct a distinguisher for SPECK cipher based on pure cryptanalysis, without using any neural network, that achieves basically the same accuracy as Gohr’s neural distinguisher and with the same efficiency (therefore improving over previous non-neural based distinguishers). Moreover, as another approach, we provide a machine learning-based distinguisher that strips down Gohr’s deep neural network to a bare minimum. We are able to remain very close to Gohr’s distinguishers’ accuracy using simple standard machine learning tools. In particular, we show that Gohr’s neural distinguisher is in fact inherently building a very good approximation of the Differential Distribution Table (DDT) of the cipher during the learning phase, and using that information to directly classify ciphertext pairs. This result allows a full interpretability of the distinguisher and represents on its own an interesting contribution towards interpretability of deep neural networks. Finally, we propose some method to improve over Gohr’s work and possible new neural distinguishers settings. All our results are confirmed with experiments we have been conducted on SPECK block cipher (source code available online).
Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist.A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance.On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39-42 s) to 36 s (95% CI = 35- 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115).Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system.• Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.
Adaptive course recommendation in MOOCs

In the process of course learning, users incline to change their interests with the improvements of their cognition. Existing course recommendation methods usually assume that users’ preferences are static. They fail to capture the user’s dynamic interests in sequential learning behaviors. In this respect, the recommendations show low accuracy and adaptivity, especially when users have diverse interests in many different courses. Thus, they may not be suitable for applying in the online course recommendation scenario. In this paper, we propose a novel course recommendation framework, named Dynamic Attention and hierarchical Reinforcement Learning (DARL), to improve the adaptivity of the recommendation model. DARL automatically captures the user’s preferences in each interaction between a profile reviser and a recommendation model, and thereby enhances the effectiveness of course recommendation. For tracking the changes in users’ preferences, DARL adaptively updates the attention weight of the corresponding course at different sessions to improve the recommendation accuracy. We perform empirical experiments on two real-world MOOCs (i.e., Massive Open Online Courses) datasets. Experimental results demonstrate that DARL significantly outperforms state-of-the-art course recommendation methods in terms of major evaluation metrics.
Heuristic Evaluation of Conversational Agents

Conversational interfaces have risen in popularity as businesses and users adopt a range of conversational agents, including chatbots and voice assistants. Although guidelines have been proposed, there is not yet an established set of usability heuristics to guide and evaluate conversational agent design. In this paper, we propose a set of heuristics for conversational agents adapted from Nielsen’s heuristics and based on expert feedback. We then validate the heuristics through two rounds of evaluations conducted by participants on two conversational agents, one chatbot and one voice-based personal assistant. We find that, when using our heuristics to evaluate both interfaces, evaluators were able to identify more usability issues than when using Nielsen’s heuristics. We propose that our heuristics successfully identify issues related to dialogue content, interaction design, help and guidance, human-like characteristics, and data privacy.
A Survey on Evolutionary Construction of Deep Neural Networks

Automated construction of deep neural networks (DNNs) has become a research hot spot nowadays because DNN’s performance is heavily influenced by its architecture and parameters, which are highly task-dependent, but it is notoriously difficult to find the most appropriate DNN in terms of architecture and parameters to best solve a given task. In this work, we provide an insight into the automated DNN construction process by formulating it into a multilevel multiobjective large-scale optimization problem with constraints, where the nonconvex, nondifferentiable, and black-box nature of this problem make evolutionary algorithms (EAs) to stand out as a promising solver. Then, we give a systematical review of existing evolutionary DNN construction techniques from different aspects of this optimization problem and analyze the pros and cons of using EA-based methods in each aspect. This work aims to help DNN researchers to better understand why, where, and how to utilize EAs for automated DNN construction and meanwhile, help EA researchers to better understand the task of automated DNN construction so that they may focus more on EA-favored optimization scenarios to devise more effective techniques.
GANs for Medical Image Synthesis: An Empirical Study

Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been trained to replicate. One recurrent theme in medical imaging, is whether GANs can also be as effective at generating workable medical data, as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study, to gauge the benefits of GANs in medical imaging. We tested various GAN architectures, from basic DCGAN to more sophisticated style-based GANs, on three medical imaging modalities and organs, namely: cardiac cine-MRI, liver CT, and RGB retina images. GANs were trained on well-known and widely utilized datasets, from which their FID scores were computed, to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images and the original data. The results reveal that GANs are far from being equal, as some are ill-suited for medical imaging applications, while others performed much better. The top-performing GANs are capable of generating realistic-looking medical images by FID standards, that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggest that no GAN is capable of reproducing the full richness of medical datasets.
Colloidal transport and flocculation are the cause of the hyperenrichment of gold in nature

Significance Hydrothermal veins supply much of the Earth’s gold. Their propensity to contain “bonanza” occurrences of gold that have concentrations millions of times greater than the concentration of gold in Earth’s crust makes them important targets for exploration and resource development. The mechanisms by which such hyperenrichment occurs are enigmatic. The accepted wisdom is that this enrichment reflects the saturation and precipitation of gold from hydrothermal fluids. Laboratory experiments and measurements of active hydrothermal systems, however, have shown that the solubility of this noble metal is exceptionally low. Here, we demonstrate that this issue is resolved by the physical transport of gold in the solid state as nanoparticles and their flocculated aggregates, thereby explaining the paradox of bonanza gold ore formation.
Gaze-Supported 3D Object Manipulation in Virtual Reality

This paper investigates integration, coordination, and transition strategies of gaze and hand input for 3D object manipulation in VR. Specifically, this work aims to understand whether incorporating gaze input can benefit VR object manipulation tasks, and how it should be combined with hand input for improved usability and efficiency. We designed four gaze-supported techniques that leverage different combination strategies for object manipulation and evaluated them in two user studies. Overall, we show that gaze did not offer significant performance benefits for transforming objects in the primary working space, where all objects were located in front of the user and within the arm-reach distance, but can be useful for a larger environment with distant targets. We further offer insights regarding combination strategies of gaze and hand input, and derive implications that can help guide the design of future VR systems that incorporate gaze input for 3D object manipulation.
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks

Machine learning techniques have been widely applied to various applications. However, they are potentially vulnerable to data poisoning attacks, where sophisticated attackers can disrupt the learning procedure by injecting a fraction of malicious samples into the training dataset. Existing defense techniques against poisoning attacks are largely attack-specific: they are designed for one specific type of attacks but do not work for other types, mainly due to the distinct principles they follow. Yet few general defense strategies have been developed. In this paper, we propose De-Pois, an attack-agnostic defense against poisoning attacks. The key idea of De-Pois is to train a mimic model the purpose of which is to imitate the behavior of the target model trained by clean samples. We take advantage of Generative Adversarial Networks (GANs) to facilitate informative training data augmentation as well as the mimic model construction. By comparing the prediction differences between the mimic model and the target model, De-Pois is thus able to distinguish the poisoned samples from clean ones, without explicit knowledge of any ML algorithms or types of poisoning attacks. We implement four types of poisoning attacks and evaluate De-Pois with five typical defense methods on different realistic datasets. The results demonstrate that De-Pois is effective and efficient for detecting poisoned data against all the four types of poisoning attacks, with both the accuracy and F1-score over 0.9 on average.
Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach

This paper compares the three levels of data fusion with the goal of determining the optimal level of data fusion for multi-sensor human activity data. Using the data processing pipeline, gyroscope and accelerometer data was fused at the sensor-level, feature-level and decision-level. For each level of data fusion four different techniques were used with varying levels of success. This analysis was performed on four human activity publicly-available datasets along with four well-known machine learning classifiers to validate the results. The decision-level fusion (Acc = 0.7443±0.0850) outperformed the other two levels of fusion in regards to accuracy, sensor level (Acc = 0.5934 ± 0.1110) and feature level (Acc = 0.6742 ± 0.0053), but, the processing time and computational power required for training and classification were far greater than practical for a HAR system. However, Kalman filter appear to be the more efficient method, since it exhibited both good accuracy (Acc = 0.7536 ± 0.1566) and short processing time (time = 61.71ms ± 63.85); properties that play a large role in real-time applications using wearable devices. The results of this study also serve as baseline information in the HAR literature to compare future methods of data fusion.
Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks

This paper addresses an under-explored problem of AI-assisted decision-making: when objective performance information of the machine learning model underlying a decision aid is absent or scarce, how do people decide their reliance on the model? Through three randomized experiments, we explore the heuristics people may use to adjust their reliance on machine learning models when performance feedback is limited. We find that the level of agreement between people and a model on decision-making tasks that people have high confidence in significantly affects reliance on the model if people receive no information about the model’s performance, but this impact will change after aggregate-level model performance information becomes available. Furthermore, the influence of high confidence human-model agreement on people’s reliance on a model is moderated by people’s confidence in cases where they disagree with the model. We discuss potential risks of these heuristics, and provide design implications on promoting appropriate reliance on AI.
Developing a modified total interpretive structural model (M-TISM) for organizational strategic cybersecurity management

Cybersecurity is a serious issue that many organizations face these days. Therefore, cybersecurity management is very important for any organization. Organizations should learn to deal with these cyber threats through effective management across all business functions. The main purpose of this study is to identify the factors that affect cybersecurity within an organization and analyze relationships among these factors. The modified total interpretive structural modeling (M-TISM) technique is used to build a hierarchical model and define the common interactions between the factors. This study presents the impact of collaboration, training, resources and capabilities, information flow, technology awareness, and technological infrastructure on effective cybersecurity management. In addition, the study also explains the interrelationships among the identified factors in the M-TISM model.
IoT-to-the-Rescue: A Survey of IoT Solutions for COVID-19-Like Pandemics

The atmospheric buoyancy and intangible nature of fatal communicable viruses lead to rapid transmissions among individuals, resulting in global pandemics. Strategic lockdowns and mandatory social distancing are immediate solutions in such scenarios. However, this leads to operational disruptions in education, manufacturing, economy, transportation, governance, and community. Although technological assistance is beneficial in overcoming such issues, the current Internet of Things (IoT) infrastructure has limitations. In this article, we provide a comprehensive review of the possible IoT-based solutions that have the capacity of combating the COVID-19-like viruses. We highlight the societal impacts due to pandemics and identify the specific lacunae in current IoT solutions. We also provide comprehensive detail on how to overcome the challenges along with directions toward the possible technological trends for future research. Compared to existing reviews, our work offers a holistic view of the cause, effects, and the possible solutions that are existing, along with already existing solutions that can be customized to serve the special needs during the pandemic.