Reviewerly/exHarmony huggingface dataset:
A large scale randomized controlled trial on herding in peer-review discussions

Peer review is the backbone of academia and humans constitute a cornerstone of this process, being responsible for reviewing submissions and making the final acceptance/rejection decisions. Given that human decision-making is known to be susceptible to various cognitive biases, it is important to understand which (if any) biases are present in the peer-review process, and design the pipeline such that the impact of these biases is minimized. In this work, we focus on the dynamics of discussions between reviewers and investigate the presence of herding behaviour therein. Specifically, we aim to understand whether reviewers and discussion chairs get disproportionately influenced by the first argument presented in the discussion when (in case of reviewers) they form an independent opinion about the paper before discussing it with others. In conjunction with the review process of a large, top tier machine learning conference, we design and execute a randomized controlled trial that involves 1,544 papers and 2,797 reviewers with the goal of testing for the conditional causal effect of the discussion initiator's opinion on the outcome of a paper. Our experiment reveals no evidence of herding in peer-review discussions. This observation is in contrast with past work that has documented an undue influence of the first piece of information on the final decision (e.g., anchoring effect) and analyzed herding behaviour in other applications (e.g., financial markets). Regarding policy implications, the absence of the herding effect suggests that the current status quo of the absence of a unified policy towards discussion initiation does not result in an increased arbitrariness of the resulting decisions.
Speech2Video Synthesis with 3D Skeleton Regularization and Expressive Body Poses

In this paper, we propose a novel approach to convert given speech audio to a photo-realistic speaking video of a specific person, where the output video has synchronized, realistic, and expressive rich body dynamics. We achieve this by first generating 3D skeleton movements from the audio sequence using a recurrent neural network (RNN), and then synthesizing the output video via a conditional generative adversarial network (GAN). To make the skeleton movement realistic and expressive, we embed the knowledge of an articulated 3D human skeleton and a learned dictionary of personal speech iconic gestures into the generation process in both learning and testing pipelines. The former prevents the generation of unreasonable body distortion, while the later helps our model quickly learn meaningful body movement through a few recorded videos. To produce photo-realistic and high-resolution video with motion details, we propose to insert part attention mechanisms in the conditional GAN, where each detailed part, e.g. head and hand, is automatically zoomed in to have their own discriminators. To validate our approach, we collect a dataset with 20 high-quality videos from 1 male and 1 female model reading various documents under different topics. Compared with previous SoTA pipelines handling similar tasks, our approach achieves better results by a user study.
From wearables to THINKables: artificial intelligence-enabled sensors for health monitoring

Wearable sensors are being used in clinical settings to monitor the condition of patients as well as in recreational environments for routine health monitoring. Some of the most advanced clinical applications include monitoring patients with Parkinson's disease through wearable inertial measuring units (IMUs) and patients with diabetes by means of wearable glucose sensors. Prominent examples of wearable sensors in routine use are fitness trackers, step-and calorie counters. Recently, wearables have evolved to being capable of running artificial intelligence algorithms in real-time at the point of sensing which allows to gain analytical insights directly from measurement data. We call such intelligent wearables with AI-at-the-edge functionality THINKables. First use cases for THINKables have emerged in both clinical and nonclinical applications: real-time seizure prediction or detection systems for epilepsy patients, or digital coaches providing real-time feedback to athletes on performance and injury risks. Technological and regulatory challenges of developing and deploying THINKables are multifold: data privacy and security of monitoring data needs to be ensured at all times, analytical AI models need to be transparent, explainable and fair, and all these features need to be implemented taking the limited computing power of point-of-sensing processors into account. In order for THINKables to become integrated into clinical workflows, all stakeholders in the Health AI ecosystem (regulators, clinicians, biomedical device technologists, pharma and biotech sectors, data scientists, and patients) need to work together to create frameworks for responsible and meaningful use.
CloTH-VTON: Clothing Three-Dimensional Reconstruction for Hybrid Image-Based Virtual Try-ON

Virtual clothing try-on, transferring a clothing image onto a target person image, is drawing industrial and research attention. Both 2D image-based and 3D model-based methods proposed recently have their benefits and limitations. Whereas 3D model-based methods provide realistic deformations of the clothing, it needs a difficult 3D model construction process and cannot handle the non-clothing areas well. Image-based deep neural network methods are good at generating disclosed human parts, retaining the unchanged area, and blending image parts, but cannot handle large deformation of clothing. In this paper, we propose CloTH-VTON that utilizes the high-quality image synthesis of 2D image-based methods and the 3D model-based deformation to the target human pose. For this 2D and 3D combination, we propose a novel 3D cloth reconstruction method from a single 2D cloth image, leveraging a 3D human body model, and transfer to the shape and pose of the target person. Our cloth reconstruction method can be easily applied to diverse cloth categories. Our method produces final try-on output with naturally deformed clothing and preserving details in high resolution.
Identification model based on latent variables

Abstract The paper proposes a model for assessing the degree of conformity of an object to a standard type. Evaluation is based on criteria based on identification signs. The model is based on the Rasch’s method of estimating latent variables. This is due to the fact that the degree of belonging of the evaluated objects to the sample is a latent variable. An original method is proposed, which assumes that indicators that allow identification are distributed according to the normal law. This allows us to estimate the average deviations of indicators from a given value and, taking into account the variance, to assess the degree of belonging of the object to the original. The obtained estimates are compared with the estimates obtained by traditional methods. Using computational experiments, it is shown that the model provides adequate estimates and can be used for identification. The method of implementation of computational procedures in MS Excel is described. The advantages of the described identification model are given in comparison with traditional models that solve this problem.
ECHO: Extended Convolution Histogram of Orientations for Local Surface Description

Abstract This paper presents a novel, highly distinctive and robust local surface feature descriptor. Our descriptor is predicated on a simple observation: instead of describing the points in the vicinity of a feature point relative to a reference frame at the feature point, all points in the region describe the feature point relative to their own frames. Isometry invariance is a byproduct of this construction. Our descriptor is derived relative to the extended convolution – a generalization of the standard convolution that allows the filter to adaptively transform as it passes over the domain. As such, we name our descriptor the Extended Convolution Histogram of Orientations (ECHO). It exhibits superior performance compared to popular surface descriptors in both feature matching and shape correspondence experiments. In particular, the ECHO descriptor is highly stable under near‐isometric deformations and remains distinctive under significant levels of noise, tessellation, complex deformations and the kinds of interference commonly found in real data.
Visualizing Color-Wise Saliency of Black-Box Image Classification Models

Image classification based on machine learning is being commonly used. However, a classification result given by an advanced method, including deep learning, is often hard to interpret. This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems. Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called a saliency map, that explains the significance of each pixel. We propose MC-RISE (Multi-Color RISE), which is an enhancement of RISE to take color information into account in an explanation. Our method not only shows the saliency of each pixel in a given image as the original RISE does, but the significance of color components of each pixel; a saliency map with color information is useful especially in the domain where the color information matters (e.g., traffic-sign recognition). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.
Multi-Regional Coverage Path Planning for Robots with Energy Constraint

Coverage path planning (CPP) has been extensively studied in the literature, which is a key step to realize robotic applications that require complete coverage of a region, such as lawn mowing, room cleaning, land assessment, search and rescue. However, CPP for multiple regions has gained much less attention, which arises in many real scenarios. This multiregional CPP problem can be considered as a variant of the traveling salesman problem (TSP) enhanced with CPP, namely TSP-CPP. In this paper, we extend our previous investigation on the TSP-CPP problem to further consider the energy constraint of the robots. As the constrained TSP-CPP problem has an NP-Hard computational complexity, a stepwise selection based heuristic algorithm is developed to solve the problem. Simulation experiments and comparison studies show the good performance of the proposed algorithm in balancing optimality and efficiency.
Falcon

We present Falcon, a novel scheduler design for large scale data analytics workloads. To improve the quality of the scheduling decisions, Falcon uses a single central scheduler. To scale the central scheduler to support large clusters, Falcon offloads the scheduling operation to a programmable switch. The core of the Falcon design is a novel pipeline-based scheduling logic that can schedule tasks at line-rate. Our prototype evaluation on a cluster with a Barefoot Tofino switch shows that the proposed approach can reduce scheduling overhead by 26 times and increase the scheduling throughput by 25 times compared to state-of-the-art centralized and decentralized schedulers.
Autonomous Mobile Robot Navigation Based on PSO Algorithm with Inertia Weight Variants for Optimal Path Planning

Abstract Motion planning is an important domain since its performance can significantly affect the utilization of robots. This paper addresses our work to developing a path planner for wheeled a mobile robot using a swarm Intelligence technique for optimal path planning within a short computational time to get better path planning results. Through this technique, we developed particle swarm optimization (PSO) for generating fast and optimal path planning. Inertia weight technique is used for performance comparison of PSO Algorithms to get optimal path planning within a complex environment, PSO with a time-varying mechanism for the inertia weight values (TV-IWPSO), to analyze the performance proposed approach on the of PSO algorithm performance. Finally, the comparison has been done in between TV-IWPSO with both particle swarm optimization with constant inertia weight (B-PSO), and standard particle swarm optimization (S-PSO), in two different maps to performing analysis for algorithms through various environments. The simulation results, which carried out using Matlab 2018a showed that the PSO algorithm with inertia-weight strategy made good results for generating optimal path planning and efficiently than (S-PSO) and (B-PSO) in terms of path distance, execution-time
RSF: A Novel Saliency Fusion Framework for Image Saliency Detection

Image saliency detection has become a hot topic in computer vision tasks. A large number of saliency models have been developed in recent years. Considering that these models use different prior knowledge, features, and theoretical methods, they have their own strengths and weaknesses in different images. Based on this observation, some works aim to fuse multiple weak saliency models based on different fusion strategies (Each saliency model in fusion framework is called a weak saliency model). However, these fusion methods lose effectiveness when image content is very complex, because they ignore the fact that various regions in an image have different characteristics in saliency fusion. Different with them, we propose a novel Region-level Saliency Fusion framework (RSF) by exploring the relationship between weak saliency models and image regions. For the input image and J weak saliency models, we firstly segment input image into N regions. Then, our goal is to learn to infer the reliability of using each weak saliency model to predict each image region saliency value. This way, we can select more reliable weak saliency models for each region to predict its saliency value. Finally, we use smoothness prior to further smooth the saliency map obtained by RSF. Experimental results on three datasets demonstrate the superiority of the proposed method than other state-of-the-art methods.
Synthetic Data in The Civil Service

Abstract In a data-driven economy, access to data is vital. So, what happens when data are not available due to disclosure issues or other restrictions? As Ioannis Kaloskampis, Chaitanya Joshi, Catrin Cheung, David Pugh and Louisa Nolan explain, in such situations it may still be possible to draw useful conclusions based on carefully manufactured data
Fault-tolerant Quantum Implementation of Priority Encoder Circuit using Clifford+T-group

The high potential quantum computer has promised to solve classically intractable problems. In practicability of such high scalable general purpose quantum computer, the foremost challenge is to protect fragile quantum state from inherent noise sources. In this regards, the most promising surface code has been used for continuous encoding and decoding of quantum information so as to achieve fault tolerance. Besides, each code has threshold level within which the said code can detect and correct the error. The fault-tolerant quantum logic is used to contain error rate below threshold which needs transversal quantum operators. On the other hands, due to much difference the Boolean logic is no more useful in quantum computing. However, the quantum computer has to perform both classical solvable as well as classically intractable problem. In this conjecture, we focus on fault tolerant quantum implementation of Priority Encoder Circuit using Clifford+T gate library which is an essential component information processor.
Benchmarking Adversarial Attacks and Defenses for Time-Series Data

The adversarial vulnerability of deep networks has spurred the interest of researchers worldwide. Unsurprisingly, like images, adversarial examples also translate to time-series data as they are an inherent weakness of the model itself rather than the modality. Several attempts have been made to defend against these adversarial attacks, particularly for the visual modality. In this paper, we perform detailed benchmarking of well-proven adversarial defense methodologies on time-series data. We restrict ourselves to the $$L_{\infty }$$ threat model. We also explore the trade-off between smoothness and clean accuracy for regularization-based defenses to better understand the trade-offs that they offer. Our analysis shows that the explored adversarial defenses offer robustness against both strong white-box as well as black-box attacks. This paves the way for future research in the direction of adversarial attacks and defenses, particularly for time-series data.
Scalable Detection of Concept Drift: A Learning Technique Based on Support Vector Machines

The issue of concept drift describes how static machine-learning models build on historical data can become unreliable over time and pose a significant challenge to many applications. Although, there is a growing body of literature investigating concept drift existing solutions are often limited to a small number of samples or features and do not work well in Industry 4.0 scenarios. We are proposing a novel algorithm that extends the existing concept drift algorithm FLORA3 by utilizing support vector machines for the classification process. Through this combination of dynamic and static approaches the algorithm is capable of effectively analyzing data streams of high volume. For evaluation, we tested our algorithm on the publicly available data set ‘elec2’, which is based on the energy market in Australia. Our results show that the proposed algorithm needs less computational resources compared to other algorithms while maintaining a high level of accuracy.
Anonymous End to End Encryption Group Messaging Protocol Based on Asynchronous Ratchet Tree

Double ratchet protocol was first proposed and used in Signal’s end to end encryption and later widely applied by WhatsApp, Facebook and other popular applications. Asynchronous Ratchet Tree (ART) is the new group messaging protocol based on ratchet and is the first protocol that applied forward secrecy (FS) and post-compromised-security (PCS) in group key exchange. However, anonymity is not considered which is crucial for privacy preserving solutions. Thus, it is meaningful to provide anonymous features while applying FS and PCS. In this paper we propose “Anonymous Asynchronous Ratchet Tree (AART)” to improve the structure of ART to achieve anonymity in group messaging while retaining FS and PCS. Also, we formalize the definitions of anonymity as Internal Group Anonymity (IGA) and External Group Anonymity (EGA). We prove that our AART satisfies IGA and EGA as well as FS and PCS.
Recursive tree grammar autoencoders

Abstract Machine learning on trees has been mostly focused on trees as input. Much less research has investigated trees as output, which has many applications, such as molecule optimization for drug discovery, or hint generation for intelligent tutoring systems. In this work, we propose a novel autoencoder approach, called recursive tree grammar autoencoder (RTG-AE), which encodes trees via a bottom-up parser and decodes trees via a tree grammar, both learned via recursive neural networks that minimize the variational autoencoder loss. The resulting encoder and decoder can then be utilized in subsequent tasks, such as optimization and time series prediction. RTG-AEs are the first model to combine three features: recursive processing, grammatical knowledge, and deep learning. Our key message is that this unique combination of all three features outperforms models which combine any two of the three. Experimentally, we show that RTG-AE improves the autoencoding error, training time, and optimization score on synthetic as well as real datasets compared to four baselines. We further prove that RTG-AEs parse and generate trees in linear time and are expressive enough to handle all regular tree grammars.
Exploring the Performance of Graphically Designed AR Markers

The design of graphical augmented reality (AR) markers requires compromise between the aesthetic appearance and tracking reliability. To investigate the topic, we created a virtual reality (VR) pipeline to evaluate marker performance, and validated it against real-world performance for a set of graphical AR markers. We report that, with the well known Vuforia framework and typical smartphone hardware, well designed 20 × 20 cm markers can be tracked at distances of up to 68 cm. We note that the number of feature points is particularly important to a marker's angular performance.
A nudge-based smart system for hand hygiene promotion in private organizations

In response to the Coronavirus 2019 (COVID-19) pandemic, the World Health Organization (WHO) has published preventive measures such as performing hand hygiene frequently, wearing a medical mask, trying to avoid touching face and so on. This paper presents a nudge-based system to promote hand hygiene in a private organization. The proposed system consists of a hand sanitizer station equipped with a magnetic sensor to sense user presses. We conducted 4 case studies to compare the effects of nudging on the frequency of hand sanitizer use: no nudging, traditional nudging, non-personalized nudging, and personalized nudging. The results reveal that using nudge-based methods offer a significant increase in the frequency hand sanitizer use.
Sentiment Analysis of Online Transportation Service using the Naïve Bayes Methods

Abstract Sentiment analysis is a computational study of opinions and emotions expressed textually. Sentiment analysis will group text in sentences or documents to find out the opinions expressed in the sentence or document, whether negative or positive. This sentiment analysis research was conducted on the online taxi transportation service. Gojek uses a lot of social media to communicate with its customers, one of the social media used is Instagram. This research takes 1000 comments from the Instagram of the Gojek page which is used to see the public opinion of the online Gojek transportation services. Comments from the page are processed by doing text preprocessing and then classified using the Naive Bayes Classifier (NBC) method to obtain the value of the public value for online transportation services. The results of this study using the Naive Bayes method resulted in an accuracy value of 81.00%, which means that from all the comments on the Instagram page, the subject of the NBC method could be accurately classified by 81.00% whether the comments were negative or positive comments.
Virtual Machine Dynamic Deployment Scheme Based on Double-Cursor Mechanism

In accordance with the dynamically changing characteristics of user application load demand, the optimization of energy consumption in the process of virtual machine automatic deployment on the basis of the effective integration of user personalized resource demand in cloud platforms and the dynamic migration technology of virtual machines is studied in this work. Aiming at the problem of energy consumption optimization, an energy-efficient virtual machine deployment framework, which can be dynamically adjusted in accordance with the user's actual resource needs, is designed. In the virtual machine deployment framework, this paper presents a virtual machine deployment selection algorithm based on a double-cursor control mechanism for the resource usage state of CPU and memory, which achieves a binocular optimization balance to a certain extent. Simulation experiment results show that when the virtual machine deployment framework proposed in this paper and the virtual machine deployment selection algorithm based on a double-cursor control mechanism are combined, the number of necessary active physical nodes in the cloud data center can be effectively controlled, and the frequency of virtual machine migration synchronization can be reduced. Consequently, the cloud data center can maintain low energy costs.