Reviewerly/exHarmony huggingface dataset:
The history and future of AI

Abstract The standard model for developing AI systems assumes a fixed, known objective that the AI system is required to optimize through its actions. Systems developed within the standard model have been increasingly successful. I briefly summarize the state of the art and its likely evolution over the next decade. Substantial breakthroughs leading to general-purpose AI are much harder to predict, but they will have an enormous impact on the global economy and on human roles therein. At the same time, I expect that the standard model will become increasingly untenable in real-world applications because of the difficulty of specifying objectives completely and correctly. I propose a new model for AI development in which the machine’s uncertainty about the true objective leads to qualitatively new modes of behaviour that are more robust, controllable, and deferential.
Multi-label enhancement based self-supervised deep cross-modal hashing

Deep cross-modal hashing which integrates deep learning and hashing into cross-modal retrieval, achieves better performance than traditional cross-modal retrieval methods. Nevertheless, most previous deep cross-modal hashing methods only utilize single-class labels to compute the semantic affinity across modalities but overlook the existence of multiple category labels, which can capture the semantic affinity more accurately. Additionally, almost all existing cross-modal hashing methods straightforwardly employ all modalities to learn hash functions but neglect the fact that original instances in all modalities may contain noise. To avoid the above weaknesses, in this paper, a novel multi-label enhancement based self-supervised deep cross-modal hashing (MESDCH) approach is proposed. MESDCH first propose a multi-label semantic affinity preserving module, which uses ReLU transformation to unify the similarities of learned hash representations and the corresponding multi-label semantic affinity of original instances and defines a positive-constraint Kullback–Leibler loss function to preserve their similarity. Then this module is integrated into a self-supervised semantic generation module to further enhance the performance of deep cross-modal hashing. Extensive evaluation experiments on four well-known datasets demonstrate that the proposed MESDCH achieves state-of-the-art performance and outperforms several excellent baseline methods in the application of cross-modal hashing retrieval. Code is available at: https://github.com/SWU-CS-MediaLab/MESDCH.
Doing Better Data Visualization

Methods in data visualization have rapidly advanced over the past decade. Although social scientists regularly need to visualize the results of their analyses, they receive little training in how to best design their visualizations. This tutorial is for individuals whose goal is to communicate patterns in data as clearly as possible to other consumers of science and is designed to be accessible to both experienced and relatively new users of R and ggplot2. In this article, we assume some basic statistical and visualization knowledge and focus on how to visualize rather than what to visualize. We distill the science and wisdom of data-visualization expertise from books, blogs, and online forum discussion threads into recommendations for social scientists looking to convey their results to other scientists. Overarching design philosophies and color decisions are discussed before giving specific examples of code in R for visualizing central tendencies, proportions, and relationships between variables.
Pose-guided model for driving behavior recognition using keypoint action learning

The number of traffic accidents caused by distracted driving is increasing worldwide. There is an urgent need for a system for detection and warning of distracted driving. Therefore, we propose a pose-guided model using the keypoint action features to recognize driving behaviors in a single image. Our work differs from the previous methods in two points. First, we integrate the heatmaps of the driver’s head and hands with the color image to extract the pose guided features. Second, we use keypoint action classification to facilitate driving behavior recognition. Besides, we introduce the keypoint gating module to reweigh the keypoint features and extract more discriminative representations. Our method has superior performance on State Farm dataset and R-DA dataset, reaching the state-of-the art level.
Improved Attacks on GIFT-64

One of the well-known superiorities of GIFT-64 over PRESENT lies in the correction of the strong linear hull effect. However, apart from the investigation of the 9-round linear hull effect in the design document, we find no linear attack result on GIFT-64. Although we do not doubt the security of GIFT-64 regarding the linear cryptanalysis, the actual resistance of the cipher to the linear attack should be evaluated since it promotes a comprehensive perception of the soundness of GIFT-64. Motivated by this observation, we implement an automatic search and find a 12-round linear distinguisher whose dominating trail is an optimal linear characteristic. Following that, the first 19-round linear attack is launched by utilising the newly identified distinguisher. On the other side, we notice that the previous differential attack of GIFT-64 covering 20 rounds claims the entire codebook. To reduce the data complexity of the 20-round attack, we apply the automatic method to exhaustively check 13-round differential trails with probabilities no less than $$2^{-64}$$ and identify multiple 13-round differentials facilitating 20-round attacks without using the full codebook. One of the candidate differentials with the maximum probability and the minimum number of guessed subkey bits is then employed to realise the first 20-round differential attack without relying on the complete codebook. Given the newly obtained results, we conjecture that the resistances of GIFT-64 against differential and linear attacks do not have a significant gap. Also, we note that the attack results in this paper are far from threatening the security of GIFT-64.
PCSG: Pattern-Coverage Snippet Generation for RDF Datasets

For reusing an RDF dataset, understanding its content is a prerequisite. To support the comprehension of its large and complex structure, existing methods mainly generate an abridged version of an RDF dataset by extracting representative data patterns as a summary. As a complement, recent attempts extract a representative subset of concrete data as a snippet. We extend this line of research by injecting the strength of summary into snippet. We propose to generate a pattern-coverage snippet that best exemplifies the patterns of entity descriptions and links in an RDF dataset. Our approach incorporates formulations of group Steiner tree and set cover problems to generate compact snippets. This extensible approach is also capable of modeling query relevance to be used with dataset search. Experiments on thousands of real RDF datasets demonstrate the effectiveness and practicability of our approach.
Detection of Turkish Sign Language Using Deep Learning and Image Processing Methods

Sign language is a physical language that enables people with disabilities to communicate using hand and facial gestures. For this reason, it is very important for people with disabilities to express themselves freely in society and to make the sign language understandable to everyone. In this study, the data set was created by taking 10223 images for 29 letters in the Turkish Sign Language Alphabet. Images are made suitable for education by using image enhancement techniques. In the final stage of the study, classification processes on images were carried out by using CapsNet, AlexNet and ResNet-50, DenseNet, VGG16, Xception, InceptionV3, NasNet, EfficentNet, Hitnet, Squeezenet architectures and TSLNet, which was designed for the study. When the deep learning models were examined, it was found that CapsNet and TSLNet models were the most successful models with 99.7% and 99.6% accuracy rates, respectively.
Human Activity Recognition Based on Acceleration Data From Smartphones Using HMMs

Smartphones are among the most popular wearable devices to monitor human activities.Several existing methods for Human Activity Recognition (HAR) using data from smartphones are based on conventional pattern recognition techniques, but they generate handcrafted feature vectors.This drawback is overcome by deep learning techniques which unfortunately require lots of computing resources, while generating less interpretable feature vectors.The current paper addresses these limitations through the proposal of a Hidden Markov Model (HMM)-based technique for HAR.More formally, the sequential variations of spatial locations within the raw data vectors are initially captured in Markov chains, which are later used for the initialization and the training of HMMs.Meta-data extracted from these models are then saved as the components of the feature vectors.The meta-data are related to the overall time spent by the model observing every symbol for a long time span, irrespective of the state from which this symbol is observed.Classification experiments involving four classification tasks have been carried out on the recently constructed UniMiB SHAR database which contains 17 classes, including 9 types of activities of daily living and 8 types of falls.As a result, the proposed approach has shown best accuracies between 92% and 98.85% for all the classification tasks.This performance is more than 10% better than prior work for 2 out of 4 classification tasks.
Social Reactions to Socially Interactive Agents and Their Ethical Implications

chapter Social Reactions to Socially Interactive Agents and Their Ethical Implications Share on Authors: Nicole Krämer View Profile , Arne Manzeschke View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 1: Methods, Behavior, CognitionSeptember 2021 Pages 77–104https://doi.org/10.1145/3477322.3477326Online:02 October 2021Publication History 1citation5DownloadsMetricsTotal Citations1Total Downloads5Last 12 Months5Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep Convolutional Graph Rough Variational Auto-Encoder for Short-Term Photovoltaic Power Forecasting

Photovoltaic (PV) power is considered as one of the most promising sustainable energy resources in recent years. However, the existing intermittency in the nature of solar energy is a significant problem for the optimization of smart grids. In this paper, to overcome PV generation uncertainty and provide an accurate spatio-temporal (ST) PV forecast, we propose a novel deep generative convolutional graph rough variational autoencoder (CGRVAE) that captures each PV site's probability distribution functions (PDFs) of future PV generation in a modeled weighted graph. Having the learned PDFs enables CGRVAE to accurately generate the future values of PV power time series. To train and evaluate our model, we used the measurements of a set of PV sites in California, US. The sites are modeled as a weighted graph where each node represents PV measurements at each site while edges reflect their correlations. Using graph spectral convolutions the proposed model extracts the most relevant information of the graph to estimate the future PV given the historical time series for each node in the modeled graph. Experimental results show the superiority of CGRVAE over state-of-the-art forecasting approaches in terms of the root mean square error (RMSE) and mean absolute error (MAE) metric.
A Hierarchical Approach for Mobile Robot Exploration in Pedestrian Crowd

Autonomous exploration is a fundamental task for mobile robots. However, a major limitation is that in practical applications, robots usually face dynamic obstacles such as pedestrian crowds. These dynamic objects create significant challenges for both collision-free navigation and accurate localization/mapping, which can compromise the safety and exploration performance of the robot. In this work, a hierarchical approach is proposed for both effective exploration and collision-free navigation in crowded environments. The central idea of our approach is to combine local and global information to ensure the safety and efficiency of the exploration planner. Besides, our planning method utilizes a reinforcement learning (RL)-based obstacle avoidance algorithm that allows the robot to safely follow the exploration planner's path through the pedestrian crowd. The proposed system is thoroughly evaluated in simulation environments, and the results show that it outperforms existing methods in terms of not only the exploration efficiency but also the localization and mapping accuracy.
An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes

Clinical research networks (CRNs), made up of multiple healthcare systems each with patient data from several care sites, are beneficial for studying rare outcomes and increasing generalizability of results. While CRNs encourage sharing aggregate data across healthcare systems, individual systems within CRNs often cannot share patient-level data due to privacy regulations, prohibiting multi-site regression which requires an analyst to access all individual patient data pooled together. Meta-analysis is commonly used to model data stored at multiple institutions within a CRN but can result in biased estimation, most notably in rare-event contexts. We present a communication-efficient, privacy-preserving algorithm for modeling multi-site zero-inflated count outcomes within a CRN. Our method, a one-shot distributed algorithm for performing hurdle regression (ODAH), models zero-inflated count data stored in multiple sites without sharing patient-level data across sites, resulting in estimates closely approximating those that would be obtained in a pooled patient-level data analysis. We evaluate our method through extensive simulations and two real-world data applications using electronic health records: examining risk factors associated with pediatric avoidable hospitalization and modeling serious adverse event frequency associated with a colorectal cancer therapy. In simulations, ODAH produced bias less than 0.1% across all settings explored while meta-analysis estimates exhibited bias up to 12.7%, with meta-analysis performing worst in settings with high zero-inflation or low event rates. Across both applied analyses, ODAH estimates had less than 10% bias for 18 of 20 coefficients estimated, while meta-analysis estimates exhibited substantially higher bias. Relative to existing methods for distributed data analysis, ODAH offers a highly accurate, computationally efficient method for modeling multi-site zero-inflated count data.
High success standard quantum teleportation using entangled coherent state and two-level atoms in cavities

We propose here a new idea for quantum teleportation of superposed coherent state which circumvents the problem of performing non-unitary $$Z_c=|\alpha \rangle \langle \alpha |-|-\alpha \rangle \langle -\alpha |$$ operation on coherent state qubit. The receiver only requires to perform either no unitary operation or a $$\pi $$ phase shift in order to recreate the information state. We use entangled resource $$\sim |\alpha ,\frac{\alpha }{\sqrt{2}}\rangle -|-\alpha ,-\frac{\alpha }{\sqrt{2}}\rangle $$ in contrast with the usual $$\sim |\alpha ,\alpha \rangle -|-\alpha ,-\alpha \rangle $$ -(both states unnormalized). Bob receives state which is then superposition of the states $$|\pm \frac{\alpha }{\sqrt{2}}\rangle $$ . Bob mixes these with even or odd coherent states involving superposition of states $$|\pm \frac{\alpha }{\sqrt{2}}\rangle $$ to obtain a two-mode state which is one of $$\sim |I,0\rangle \pm |0,I\rangle $$ , $$|I\rangle $$ being the information state. Bob then obtains the teleported information by using the interaction of one of these modes in two cavities with resonant two-level atoms. This scheme results in average fidelity of $$\simeq 0.95$$ for $$|\alpha |^2 \simeq 10$$ , which increases with $$|\alpha |^2$$ and tends to 1 asymptotically, varying as $$1-\frac{\pi ^2}{16|\alpha |^2}+\frac{\pi ^2(\pi ^2+8)}{256|\alpha |^4}$$ for large values of $$|\alpha |^2$$ .
Using a balanced scorecard to identify opportunities to improve code review effectiveness: an industrial experience report

Peer code review is a widely adopted software engineering practice to ensure code quality and ensure software reliability in both the commercial and open-source software projects. Due to the large effort overhead associated with practicing code reviews, project managers often wonder, if their code reviews are effective and if there are improvement opportunities in that respect. Since project managers at Samsung Research Bangladesh (SRBD) were also intrigued by these questions, this research developed, deployed, and evaluated a production-ready solution using the Balanced SCorecard (BSC) strategy that SRBD managers can use in their day-to-day management to monitor individual developer’s, a particular project’s or the entire organization’s code review effectiveness. Following the four-step framework of the BSC strategy, we– 1) defined the operation goals of this research, 2) defined a set of metrics to measure the effectiveness of code reviews, 3) developed an automated mechanism to measure those metrics, and 4) developed and evaluated a monitoring application to inform the key stakeholders. Our automated model to identify useful code reviews achieves 7.88% and 14.39% improvement in terms of accuracy and minority class F1 score respectively over the models proposed in prior studies. It also outperforms human evaluators from SRBD, that the model replaces, by a margin of 25.32% and 23.84% respectively in terms of accuracy and minority class F1 score. In our post-deployment survey, SRBD developers and managers indicated that they found our solution as useful and it provided them with important insights to help their decision makings.
Skeleton2Mesh: Kinematics Prior Injected Unsupervised Human Mesh Recovery

In this paper, we decouple unsupervised human mesh recovery into the well-studied problems of unsupervised 3D pose estimation, and human mesh recovery from estimated 3D skeletons, focusing on the latter task. The challenges of the latter task are two folds: (1) pose failure (i.e., pose mismatching – different skeleton definitions in dataset and SMPL , and pose ambiguity – endpoints have arbitrary joint angle configurations for the same 3D joint coordinates). (2) shape ambiguity (i.e., the lack of shape constraints on body configuration). To address these issues, we propose Skeleton2Mesh, a novel lightweight framework that recovers human mesh from a single image. Our Skeleton2Mesh contains three modules, i.e., Differentiable Inverse Kinematics (DIK), Pose Refinement (PR) and Shape Refinement (SR) modules. DIK is designed to transfer 3D rotation from estimated 3D skeletons, which relies on a minimal set of kinematics prior knowledge. Then PR and SR modules are utilized to tackle the pose ambiguity and shape ambiguity respectively. All three modules can be incorporated into Skeleton2Mesh seamlessly via an end-to-end manner. Furthermore, we utilize an adaptive joint regressor to alleviate the effects of skeletal topology from different datasets. Results on the Human3.6M dataset for human mesh recovery demonstrate that our method improves upon the previous unsupervised methods by 32.6% under the same setting. Qualitative results on in-the-wild datasets exhibit that the recovered 3D meshes are natural, realistic. Our project is available at https://sites.google.com/view/skeleton2mesh.
Visage

Analytics with three-dimensional imagery from drones are driving the next generation of remote monitoring applications. Today, there is an unmet need in providing such analytics in an interactive manner, especially over weak Internet connections, to quickly diagnose and solve problems in the commercial industry space of monitoring assets using drones in remote parts of the world. Existing mechanisms either compromise on the quality of insights by not building 3D images and analyze individual 2D images in isolation, or spend tens of minutes building a 3D image before obtaining and uploading insights. We present Visage, a system that accelerates 3D image analytics by identifying smaller parts of the data that can actually benefit from 3D analytics and prioritizing building, and uploading the localized 3D images for those parts. To achieve this, Visage uses a graph to represent raw 2D images and their relative content overlap, and then identifies the various subgraphs using application knowledge that are good candidates for localized 3D image based insights. We evaluate Visage using data from multiple real deployments and show that it can reduce analytics-latency by up to four orders of magnitude.
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation

Estimating the motion of the camera together with the 3D structure of the scene from a monocular vision system is a complex task that often relies on the so-called scene rigidity assumption. When observing a dynamic environment, this assumption is violated which leads to an ambiguity between the ego-motion of the camera and the motion of the objects. To solve this problem, we present a self-supervised learning framework for 3D object motion field estimation from monocular videos. Our contributions are two-fold. First, we propose a two-stage projection pipeline to explicitly disentangle the camera ego-motion and the object motions with dynamics attention module, called DAM. Specifically, we design an integrated motion model that estimates the motion of the camera and object in the first and second warping stages, respectively, controlled by the attention module through a shared motion encoder. Second, we propose an object motion field estimation through contrastive sample consensus, called CSAC, taking advantage of weak semantic prior (bounding box from an object detector) and geometric constraints (each object respects the rigid body motion model). Experiments on KITTI, Cityscapes, and Waymo Open Dataset demonstrate the relevance of our approach and show that our method outperforms state-of-the-art algorithms for the tasks of self-supervised monocular depth estimation, object motion segmentation, monocular scene flow estimation, and visual odometry.
Complete b-symbol weight distribution of some irreducible cyclic codes

Recently, b-symbol codes are proposed to protect against b-symbol errors in b-symbol read channels. It is an interesting subject of study to consider the complete b-symbol weight distribution of cyclic codes since b-symbol metric is a generalization for Hamming metric. The complete b-symbol Hamming weight distribution of irreducible cyclic codes is known in only a few cases. In this paper, we give a complete b-symbol Hamming weight distribution of a class of irreducible cyclic codes with two nonzero b-symbol Hamming weights.