Reviewerly/exHarmony huggingface dataset:
A Design Inquiry into Introspective AI: Surfacing Opportunities, Issues, and Paradoxes

Introspection is the practice of looking inward and examining our ideas, thoughts, and feelings. It involves considering past experiences and asking questions about the future. We report on a design research inquiry that explores Artificial Intelligence (AI), combined with personal data, as a resource for introspection. We investigate how AI might offer possibilities for generating alternative perspectives on one's life to support introspection and paradoxes that this might raise. We describe our design-led inquiry, motivate fi approaches to introspective practice as opportunities for potential Introspective AI interventions, and explore them through seven design proposals. Taken together, our proposals provoke questions around how introspective AI might be critiqued, imagined, and designed. We conclude with a reflection on our work and the opportunities it suggests for future research and practice.
SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation

Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). However, conventional methods still resort to some predetermined integer scaling factors, e.g., ×2 or ×4. Thus, they are difficult to be applied when arbitrary target resolutions are required. Recent approaches ex-tend the scope to real-valued upsampling factors, even with varying aspect ratios to handle the limitation. In this pa-per, we propose the SRWarp framework to further generalize the SR tasks toward an arbitrary image transformation. We interpret the traditional image warping task, specifically when the input is enlarged, as a spatially-varying SR problem. We also propose several novel formulations, including the adaptive warping layer and multiscale blending, to reconstruct visually favorable results in the transformation process. Compared with previous methods, we do not con-strain the SR model on a regular grid but allow numerous possible deformations for flexible and diverse image editing. Extensive experiments and ablation studies justify the necessity and demonstrate the advantage of the proposed SRWarp method under various transformations.
Blockchain-Enabled and Data-Driven Smart Healthcare Solution for Secure and Privacy-Preserving Data Access

The major advances in body-mounted sensors and wireless technologies have been revolutionizing the healthcare industry, where patient’s conditions can be remotely monitored by medical staff. Such a model is gaining broad support due to its economic and social advantages. However, the wealth of sensor measurements pose major technical challenges on where to store the collected data, how to ensure its integrity, who control access permissions, and how to enable secure interaction between patients and medical facilities and professionals. This article aspires to provide a holistic solution based on blockchain technology. Our solution puts the patient in charge for granting and revoking access permissions and makes it easy for healthcare organizations and providers to meet privacy regulations. The sensor data are to reside on cloud storage, while access control and session logs are maintained on blockchain. In addition, a novel data-driven authentication and secure communication protocol is proposed to mitigate the risk of fraud and identity theft. In order to enforce such a protocol, all interactions between the cloud and patients and healthcare providers are regulated through smart contracts. The security properties of our solution are analyzed using AVISPA; it is also shown to be computationally efficient.
Bangla numerical sign language recognition using convolutional neural networks (CNNs)

&lt;p&gt;The amount of deaf and mute individuals on the earth is rising at an alarmingrate. Bangladesh has about 2.6 million people who are unable to interact with the community using language. Hearing-impaired citizens in Bangladesh use Bangladeshi sign language (BSL) as a means of communication. In this article,we propose a new method for Bengali sign language recognition based on deep convolutional neural networks. Our framework employs convolutional neural networks (CNN) to learn from the images in our dataset and interpret hand signs from input images. Checking their collections of ten indications (we usedten sets of images with 31 distinct signs) for a total of 310 images. The proposed system takes snap shots from a video by using a webcam with applying a computer vision-based approach. After that, it compares those photos to a previously trained dataset generated with CNN and displays the Bengali numbers (০-৯). After estimating the model on our dataset, weobtained an overall accuracy of 99.8%. We want to streng then things as far aswe can to make silent contact with the majority of society as simple asprobable.&lt;/p&gt;
A Multi-AUV Path Planning System Based on the Omni-Directional Sensing Ability

Following the development of autonomous underwater vehicles (AUVs), multiple trajectory-based submarine target information collection constitutes one of the key technologies that significantly influence underwater information collection ability and deployment efficiency. In this paper, we propose an underwater information collection AUV, O-AUV, that can perceive the omnidirectional area and could detect a larger area than the traditional AUV. A 3D sensing model for the O-AUV is proposed to describe the complex underwater information collection spaces. Thereafter, a cube-based environment model involving candidate observation point calculation methods are suggested to adapt the O-AUV model. A voyage cost map is also built according to the multi-AUV path planning for a common submarine mission that must traverse numerous mission targets in complex environments through the R-Dijkstra algorithm. Specifically, the voyage planning problem is solved through a critical algorithm called ANSGA (accelerated NSGA-II algorithm), which in turn, is developed by modifying the non-dominated sorting genetic algorithm (NSGA-II) to accelerate the optimization rate for the Pareto solution. Experiments are carried out in MATLAB, and the results verify the validity of the proposed O-AUV+ANSGA algorithm framework.
SemEval-2021 Task 12: Learning with Disagreements

Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision.However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization.The aim of the SemEval-2021 shared task on Learning with Disagreements (L --D ) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images.In this paper we describe the shared task and its results.
Blockchain Technology in Healthcare Big Data Management: Benefits, Applications and Challenges

Healthcare transformation has become a major priority in a world where extraordinary technological breakthroughs are occurring. Current healthcare data management systems are centralized, posing the possibility of a single point of failure in the event of a natural disaster. Blockchain has rapidly risen to become one of the most talked-about innovations that can tackle many of current data management challenges in the healthcare industry. The use of blockchain technologies for the delivery of safe and secure healthcare data has sparked a lot of interest. Blockchain has the potential to reduce or eliminate many of the most significant challenges of today's healthcare big data management such as patient consents, data ownership, information governance, data auditing, privacy, security, traceability, immutability, flexible access and the reconciliation of conflicting changes from multiple sources. With these potential benefits of blockchain, there are few previously published researches have focused on the role of blockchain in healthcare big data management. In this paper, we aim at enriching the body of literature by exploring the potentials of blockchain for improving healthcare big data management systems. The paper provides an expanded view of the important aspects of the blockchain including the main features, the opportunities of implementation in the management of healthcare big data, as well as the challenges that impedes its acceptance in healthcare context
Policies for elementary links in a quantum network

Distributing entanglement over long distances is one of the central tasks in quantum networks. An important problem, especially for near-term quantum networks, is to develop optimal entanglement distribution protocols that take into account the limitations of current and near-term hardware, such as quantum memories with limited coherence time. We address this problem by initiating the study of quantum network protocols for entanglement distribution using the theory of decision processes, such that optimal protocols (referred to as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:math> in the context of decision processes) can be found using dynamic programming or reinforcement learning algorithms. As a first step, in this work we focus exclusively on the elementary link level. We start by defining a quantum decision process for elementary links, along with figures of merit for evaluating policies. We then provide two algorithms for determining policies, one of which we prove to be optimal (with respect to fidelity and success probability) among all policies. Then we show that the previously-studied memory-cutoff protocol can be phrased as a policy within our decision process framework, allowing us to obtain several new fundamental results about it. The conceptual developments and results of this work pave the way for the systematic study of the fundamental limitations of near-term quantum networks, and the requirements for physically realizing them.
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate

Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition.Most recent approaches have attempted to solve this problem by implicitly modeling dependencies between outputs.In this paper, we introduce AligNART, which leverages full alignment information to explicitly reduce the modality of the target distribution.AligNART divides the machine translation task into (i) alignment estimation and (ii) translation with aligned decoder inputs, guiding the decoder to focus on simplified one-to-one translation.To alleviate the alignment estimation problem, we further propose a novel alignment decomposition method.Our experiments show that AligNART outperforms previous non-iterative NART models that focus on explicit modality reduction on WMT14 En↔De and WMT16 Ro→En.Furthermore, AligNART achieves BLEU scores comparable to those of the state-of-the-art connectionist temporal classification based models on WMT14 En↔De.We also observe that AligNART effectively addresses the token repetition problem even without sequence-level knowledge distillation.
Revealing the Reciprocal Relations between Self-Supervised Stereo and Monocular Depth Estimation

Current self-supervised depth estimation algorithms mainly focus on either stereo or monocular only, neglecting the reciprocal relations between them. In this paper, we propose a simple yet effective framework to improve both stereo and monocular depth estimation by leveraging the underlying complementary knowledge of the two tasks. Our approach consists of three stages. In the first stage, the proposed stereo matching network termed StereoNet is trained on image pairs in a self-supervised manner. Second, we introduce an occlusion-aware distillation (OA Distillation) module, which leverages the predicted depths from StereoNet in non-occluded regions to train our monocular depth estimation network named SingleNet. At last, we design an occlusion-aware fusion module (OA Fusion), which generates more reliable depths by fusing estimated depths from StereoNet and SingleNet given the occlusion map. Furthermore, we also take the fused depths as pseudo labels to supervise StereoNet in turn, which brings StereoNet’s performance to a new height. Extensive experiments on KITTI dataset demonstrate the effectiveness of our proposed framework. We achieve new SOTA performance on both stereo and monocular depth estimation tasks.
Enriching query semantics for code search with reinforcement learning

Code search is a common practice for developers during software implementation. The challenges of accurate code search mainly lie in the knowledge gap between source code and natural language (i.e., queries). Due to the limited code-query pairs and large code-description pairs available, the prior studies based on deep learning techniques focus on learning the semantic matching relation between source code and corresponding description texts for the task, and hypothesize that the semantic gap between descriptions and user queries is marginal. In this work, we found that the code search models trained on code-description pairs may not perform well on user queries, which indicates the semantic distance between queries and code descriptions. To mitigate the semantic distance for more effective code search, we propose QueCos, a Query-enriched Code search model. QueCos learns to generate semantic enriched queries to capture the key semantics of given queries with reinforcement learning (RL). With RL, the code search performance is considered as a reward for producing accurate semantic enriched queries. The enriched queries are finally employed for code search. Experiments on the benchmark datasets show that QueCos can significantly outperform the state-of-the-art code search models.
Learning Domain Adaptation with Model Calibration for Surgical Report Generation in Robotic Surgery

Generating a surgical report in robot-assisted surgery, in the form of natural language expression of surgical scene understanding, can play a significant role in document entry tasks, surgical training, and post-operative analysis. Despite the state-of-the-art accuracy of the deep learning algorithm, the deployment performance often drops when applied to the Target Domain (TD) data. For this purpose, we develop a multi-layer transformer-based model with the gradient reversal adversarial learning to generate a caption for the multi-domain surgical images that can describe the semantic relationship between instruments and surgical Region of Interest (ROI). In the gradient reversal adversarial learning scheme, the gradient multiplies with a negative constant and updates adversarially in backward propagation, discriminating between the source and target domains and emerging domain-invariant features. We also investigate model calibration with label smoothing technique and the effect of a well-calibrated model for the penultimate layer&#x2019;s feature representation and Domain Adaptation (DA). We annotate two robotic surgery datasets of MICCAI robotic scene segmentation and Transoral Robotic Surgery (TORS) with the captions of procedures and empirically show that our proposed method improves the performance in both source and target domain surgical reports generation in the manners of unsupervised, zero-shot, one-shot, and few-shot learning.
Toward QoS-Awareness and Improved Utilization of Spatial Multitasking GPUs

Datacenters use GPUs to provide the significant computing throughput required by emerging user-facing services. The diurnal user access pattern of user-facing services provides a strong incentive to co-located applications for better GPU utilization, and prior work has focused on enabling co-location on multicore processors and traditional non-preemptive GPUs. However, current GPUs are evolving towards spatial multitasking and introduce a new set of challenges to eliminate QoS violations. To address this open problem, we explore the underlying causes of QoS violation on spatial multitasking GPUs. In response to these causes, we propose C-Laius, a runtime system that carefully allocates the computation resource to co-located applications for maximizing the throughput of batch applications while guaranteeing the required QoS of user-facing services. C-Laius not only allows co-locating one user-facing application with multiple batch applications, but also supports the co-location of multiple user-facing applications with batch applications. In the case of a single co-located user-facing application, our evaluation on an Nvidia RTX 2080Ti GPU shows that C-Laius improves the utilization of spatial multitasking GPUs by 20.8 percent, while achieving the 99%-ile latency target for user-facing services. As to the case of multiple co-located user-facing applications, C-Laius ensures no violation of QoS while improving the accelerator utilization by 35.9 percent on average.
High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting—implications for sediment fingerprinting

Abstract Purpose This study tests the feasibility of using a submersible spectrophotometer as a novel method to trace and apportion suspended sediment sources in situ and at high temporal frequency. Methods Laboratory experiments were designed to identify how absorbance at different wavelengths can be used to un-mix artificial mixtures of soil samples (i.e. sediment sources). The experiment consists of a tank containing 40 L of water, to which the soil samples and soil mixtures of known proportions were added in suspension. Absorbance measurements made using the submersible spectrophotometer were used to elucidate: (i) the effects of concentrations on absorbance, (ii) the relationship between absorbance and particle size and (iii) the linear additivity of absorbance as a prerequisite for un-mixing. Results The observed relationships between soil sample concentrations and absorbance in the ultraviolet visible (UV–VIS) wavelength range (200–730 nm) indicated that differences in absorbance patterns are caused by soil-specific properties and particle size. Absorbance was found to be linearly additive and could be used to predict the known soil sample proportions in mixtures using the MixSIAR Bayesian tracer mixing model. Model results indicate that dominant contributions to mixtures containing two and three soil samples could be predicted well, whilst accuracy for four-soil sample mixtures was lower (with respective mean absolute errors of 15.4%, 12.9% and 17.0%). Conclusion The results demonstrate the potential for using in situ submersible spectrophotometer sensors to trace suspended sediment sources at high temporal frequency.
Conceptual Origins, Technological Advancements, and Impacts of Using Virtual Reality Technology in Education

Educators of the 21st century have to be techno-friendly to cater to students' needs who embrace the latest technologies. Using visual-based methods like PowerPoint presentations, videos, animation, etc., has helped the students retain information compared to the traditional methods. One of the breath-taking advancements in visualizing technology is Virtual Reality (VR). This paper provides an overview of Virtual Reality (VR) technology and its applications in education. During this study, to better understand VR's contributions to education, a systematic review of the literature was conducted from Scopus, IEEE, and Google Scholar databases. The paper aims to highlight the core concepts, the origination of the technology, its impact, associated problems, and future directions of VR concerning education and the methods considered by notorious researchers. Identified gaps in the chosen literature works were also highlighted, and suggestions to overcome the identified gaps were summarized.
An Improved Physical ZKP for Nonogram

Nonogram is a logic puzzle consisting of a rectangular grid with an objective to color every cell black or white such that the lengths of blocks of consecutive black cells in each row and column are equal to the given numbers. In 2010, Chien and Hon developed the first physical zero-knowledge proof for Nonogram, which allows a prover to physically show that he/she knows a solution of the puzzle without revealing it. However, their protocol requires special tools such as scratch-off cards and a machine to seal the cards, which are difficult to find in everyday life, making the protocol impractical. Their protocol also has a nonzero soundness error. In this paper, we propose a more practical physical zero-knowledge proof for Nonogram that uses only a deck of regular paper cards and also has perfect soundness.
A novel approach for spam detection based on association rule mining and genetic algorithm

• A supervised learning method is proposed for detecting spam in social networks. • The proposed method selects the most significant features using Association Rule Mining and Genetic Algorithm. • Performance evaluation of proposed method on benchmark datasets gives promising results. Spam detection is considered to be one of the most challenging issues in Online Social Networks (OSNs). In this paper, a supervised method is used to detect spam on these platforms. The accuracy of supervised methods depended on two factors: (i) the desired feature selection and (ii) the use of an appropriate classifier. An innovative method is also used for the first factor. This method is a combination of association rule mining and genetic algorithm with the aim of the desired feature selection from a variety of features. On the other hand, the second factor uses a large number of popular classifiers. The proposed method is assessed on three datasets, and the results show the effectiveness of the proposed feature selection method on the accuracy of the classifiers. The average accuracy for both approaches compared to the basic methods is 87.99% and 95.24%, respectively.
Object Tracking in Satellite Videos Based on Correlation Filter with Multi-Feature Fusion and Motion Trajectory Compensation

As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's surface. Inspired by the capabilities of video satellites, this paper presents a novel method to track fast-moving objects in satellite videos based on the kernelized correlation filter (KCF) embedded with multi-feature fusion and motion trajectory compensation. The contributions of the suggested algorithm are multifold. First, a multi-feature fusion strategy is proposed to describe an object comprehensively, which is challenging for the single-feature approach. Second, a subpixel positioning method is developed to calculate the object’s position and overcome the poor tracking accuracy difficulties caused by inaccurate object localization. Third, introducing an adaptive Kalman filter (AKF) enables compensation and correction of the KCF tracker results and reduces the object’s bounding box drift, solving the moving object occlusion problem. Based on the correlation filtering tracking framework, combined with the above improvement strategies, our algorithm improves the tracking accuracy by at least 17% on average and the success rate by at least 18% on average compared to the KCF algorithm. Hence, our method effectively solves poor object tracking accuracy caused by complex backgrounds and object occlusion. The experimental results utilize satellite videos from the Jilin-1 satellite constellation and highlight the proposed algorithm's appealing tracking results against current state-of-the-art trackers regarding success rate, precision, and robustness metrics.