Reviewerly/exHarmony huggingface dataset:
Inductive learning of answer set programs for autonomous surgical task planning

Abstract The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot’s operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones; specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery.
Settling the robust learnability of mixtures of Gaussians

This work represents a natural coalescence of two important lines of work – learning mixtures of Gaussians and algorithmic robust statistics. In particular we give the first provably robust algorithm for learning mixtures of any constant number of Gaussians. We require only mild assumptions on the mixing weights (bounded fractionality) and that the total variation distance between components is bounded away from zero. At the heart of our algorithm is a new method for proving dimension-independent polynomial identifiability through applying a carefully chosen sequence of differential operations to certain generating functions that not only encode the parameters we would like to learn but also the system of polynomial equations we would like to solve. We show how the symbolic identities we derive can be directly used to analyze a natural sum-of-squares relaxation.
Reversible Privacy-Preserving Recognition

In this paper, we propose a novel reversible face privacy-preserving scheme. Before uploading facial images onto the cloud, we first cover the facial region with mosaic and train an encoder to generate protected images with original facial information embedded. We train another classifier with protected images for facial expression recognition and a decoder for recovering original facial images. On the cloud service, protected images provide little identity information to malicious attackers. For low-privileged users, they can use the provided classifier to do computer vision tasks with protected images. For authorized users, after content recovery, the nor-mal usage of the facial images will not be affected. Experimental results show that the proposed method is effective in facial images recovery. In addition, the protected images can maintain similar accuracy on typical computer vision tasks compared to the original images.
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation

Panoptic segmentation aims to partition an image to object instances and semantic content for thing and stuff categories, respectively. To date, learning weakly supervised panoptic segmentation (WSPS) with only image-level labels remains unexplored. In this paper, we propose an efficient jointly thing-and-stuff mining (JTSM) framework for WSPS. To this end, we design a novel mask of interest pooling (MoIPool) to extract fixed-size pixel-accurate feature maps of arbitrary-shape segmentations. MoIPool enables a panoptic mining branch to leverage multiple instance learning (MIL) to recognize things and stuff segmentation in a unified manner. We further refine segmentation masks with parallel instance and semantic segmentation branches via self-training, which collaborates the mined masks from panoptic mining with bottom-up object evidence as pseudo-ground-truth labels to improve spatial coherence and contour localization. Experimental results demonstrate the effectiveness of JTSM on PASCAL VOC and MS COCO. As a by-product, we achieve competitive results for weakly supervised object detection and instance segmentation. This work is a first step towards tackling challenge panoptic segmentation task with only image-level labels.
LBCF: A Link-Based Collaborative Filtering for Overfitting Problem in Recommender System

Recommender system (RS) suggests relevant objects to generate personalized service and minimize information overload issue. User-based collaborative filtering (UBCF) plays a dominant role in practical RSs. However, traditional UBCF suffers from a recommendation overfitting problem, i.e., recommendations generated by UBCF usually concentrate on popular items, resulting in lower diversity. In addition, UBCF cannot maintain a reasonable tradeoff between the accuracy and diversity of recommendations because raising the diversity is often accompanied by a decrease in accuracy. In this article, we propose a novel approach, namely link-based collaborative filtering, to enhance the recommendation accuracy and diversity simultaneously without employing additional complex information. First, a user–item bipartite network is constructed based on the user–item rating matrix of RSs. Then, a global–local weighted bipartite modularity is presented to conduct link partition so that links with the same community can not only be relatively denser but also own the same characteristic. Furthermore, redundant links are removed from each community by utilizing a link reduction algorithm so that neighborhood of a target user can be selected according to the more efficient nonredundant links. Finally, rating prediction is executed based on the rating information of neighborhood. Also, items owning the highest predicted rating scores will be recommended to the target user. Experimental results from three real datasets of RSs suggest that, without taking advantage of special additional data, our proposed approach outperforms the state-of-the-art studies and is able to generate personalized recommendations with satisfying accuracy and diversity simultaneously.
Learning Content and Context with Language Bias for Visual Question Answering

Visual Question Answering (VQA) is a challenging multi-modal task to answer questions about an image. Many works concentrate on how to reduce language bias which makes models answer questions ignoring visual content and language context. However, reducing language bias also weakens the ability of VQA models to learn context prior. To address this issue, we propose a novel learning strategy named CCB, which forces VQA models to answer questions relying on Content and Context with language Bias. Specifically, CCB establishes Content and Context branches on top of a base VQA model and forces them to focus on local key content and global effective context respectively. Moreover, a joint loss function is proposed to reduce the importance of biased samples and retain their beneficial influence on answering questions. Experiments show that CCB outperforms the state-of-the-art methods on VQA-CP v2.
Dual Pricing Optimization for Live Video Streaming in Mobile Edge Computing With Joint User Association and Resource Management

Mobile live video streaming is expected to become mainstream in the fifth generation (5G) mobile networks. To boost the Quality of Experience (QoE) of streaming services, the integration of Scalable Video Coding (SVC) with Mobile Edge Computing (MEC) becomes a natural candidate due to its scalability and the reliable transmission supports for real-time interactions. However, it still takes efforts to integrate MEC into video streaming services to exploit its full potentials. We find that the efficiency of the MEC-enabled cellular system can be significantly improved when the requests of users can be redirected to proper MEC servers through optimal user associations. In light of this observation, we jointly address the caching placement, video quality decision, and user association problem in the live video streaming service. Since the proposed nonlinear integer optimization problem is NP-hard, we first develop a two-step approach from a Lagrangian optimization under the dual pricing specification. Further, to have a computation-efficient solution and less performance loss, we provide a one-step Lagrangian dual pricing algorithm by the convex transformation of non-convex constraints. The simulations show that the service quality of live video streaming can be remarkably enhanced by the proposed algorithms in the MEC-enabled cellular system.
A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.
Terminal-Side Data Rate Prediction For High-Mobility Users

The possibility of predicting Quality of Service, and particularly data rates, in mobile networks will enable new applications for future automated and connected mobility, such as teleoperated driving. Since network data is difficult to acquire and usually of low granularity, robust prediction approaches are required that need to be trained with data sets and measurements generated by end devices. In this paper, we present uplink and downlink data sets, measured in extensive drive tests, that are made available for the evaluation of machine learning methods. Based on this data, we compare the data rate prediction performance in uplink and downlink for neural network, random forest and gradient boosting approaches. Our results show a significantly higher achievable accuracy in uplink than in downlink, and that, even with reduced feature sets, gradient boosting is particularly suited for the prediction task. Moreover, we investigate the use of quantile estimation methods for predicting bounds on the achievable data rate. We show that conformalized approaches, both based on neural networks and random forests, can predict quantiles with very high accuracy.
Designing “Safer Water.” A Virtual Reality Tool for the Safety and the Psychological Well-Being of Citizens Exposed to the Risk of Natural Disasters

Virtual Reality (VR) is a popular technology to recreate reality-like scenarios, including dangerous ones, in a realistic but safe way. Because of this potential, VR based research has been applied in psychology studies to provide training and education about how to behave in emergencies such as fires, earthquakes, floods, or typhoons. All these different virtual scenarios have been built to observe how people react to emergencies, what behaviors they adopted, what level of stress is generated, and finally, how to increase citizens' safety. However, there is still little research that shows how Virtual Environment (VE) should be designed to convey appropriate social and psychological "cues" to participants. In this work, we present the result of a series of co-design sessions aiming to bring experts to collaborate in setting up virtual scenarios to increase the quality of life, safety perception, and risk awareness in people living in the proximity of a river. Floods are one of the most threatening climate events, and because of climate change, they are expected to become even more frequent. These disasters have a devastating impact on communities, increasing anxiety and stress levels in citizens living close to rivers. We involved relevant stakeholders to design "Safer Water," an immersive, interactive, virtual experience to support citizens in psychologically and behaviorally managing pre and post riverbank breakdown situations. HCI experts, hydrogeological and hydraulic engineers, psychologists, and VEs designers took part in affinity diagram and brainstorming activities. Results show how the adopted method was able to generate suitable virtual scenarios, to highlight and classify relevant design requirements, and to find strategies that could improve the quality of life and psychological well-being in "risk-exposed citizens." The discussion includes a set of open-access guidelines derived from the co-design activities, to support the design of VE for the purposes discussed in the paper.
Ensemble dimension reduction based on spectral disturbance for subspace clustering

The feature distribution of high dimension, small sample size (HDSS) data is sparse, resulting in unsatisfactory clustering results. Dimension reduction methods play an inevitable role in analyzing and visualizing high-dimensional data. It is likely to cause the matrix singularity for subspace clustering when directly reduce the dimension of HDSS dataset. Therefore, we construct multiple data subsets from the original HDSS dataset for ensemble dimension reduction. Projection least square regression subspace clustering (PLSR) which combines projection technique with least-square regression is used as a base dimension reducer for ensemble dimension reduction, called EPLSR. Considering the spectral properties of spectral clustering, we propose the ensemble dimension reduction for subspace clustering based on spectral disturbance (SD-EPLSR) method. According to the theory of spectral disturbance, the weight coefficients are learned according to two principles: 1. The clustering results on each data subset should be close to the consensus clustering result. 2. Data subsets with similar clustering results should have approximate weights. Experiments on eight HDSS datasets demonstrate that our method is effective.
Political speech acts in contrast: The case of calls to condemn in news interviews

This study takes a contrastive pragmatics approach to examine the ethical and moral discourses constructed around calls to condemn realized by journalists in two political linguacultures: Israel and the US. By studying the contents and the contexts within which Israeli and American news interviewers formulated 215 variations of do you condemn questions between 2006 and 2018, and the negotiations that followed interviewees' answers, the study aims to identify moral and ethical similarities and differences in the two political linguacultures. At the moral level, the study identified variations in the object of the calls. News interviewers in both linguacultures were mainly concerned with disloyalty to the nation, threats to national security, and incivility in public discourse. Interviewers from the two political linguacultures differed in their emphasis on moral issues: Americans framed incivility as the most imminent threat to society, while Israelis emphasized national security and loyalty. At the ethical level, American interviewers showed greater tolerance to non-mainstream views. Israeli interviewers tended to put greater pressure on their interviewees to condemn by formulating more follow-up questions, thus expressing less tolerance for alternative views. I conclude by discussing the benefits of studying political speech acts in contrast.
A Shallow Deep Neural Network for Selection of Migration Candidate Virtual Machines to Reduce Energy Consumption

In recent years, the widespread growth of cloud computing has surprisingly increased the energy consumption in data centers. In this regard, employing energy reduction techniques is changed to one of the prominent challenges for cloud service providers and includes both dynamic and static techniques. Although by utilizing static techniques along with creating data centers energy consumption is relatively reduced, the rapid growth of cloud computing due to the increasing demands of users for these resources has changed energy consumption to a potential challenge. Utilizing dynamic energy reduction techniques which can be possible through the integration of the virtual machine into at least one physical server can be considered as an effective solution to this problem. This is done through live virtual machine migration and selecting the migration candidate virtual machine is a key step in this technique. In this paper, the combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is used to choose the appropriate migration candidate virtual machine which leads to the diagnosis of whether a virtual machine is sensitive to latency or not. The proposed model was validated on the workload of Microsoft Azure virtual machines as a dataset. According to the empirical results, the proposed model has higher classification accuracy compared to other existing models for selecting the migration candidate virtual machines.
Estimating depth information of vascular models: A comparative user study between a virtual reality and a desktop application

Vascular structures are assessed, e.g., in tumor surgery to understand the influence of a planned resection on the vascular supply and venous drainage. The understanding of complex branching vascular structures may benefit from immersive virtual reality (VR) visualization. Therefore, the estimation of distance, depth and shape information is a crucial task to support diagnosis and therapy decisions. Depending on the visualization techniques used, perceptual issues can influence this process and may thus lead to false conclusions. Many studies were carried out to study depth perception for different variants of vessel visualization. However, these studies are restricted to desktop applications. Since VR exhibits specific perceptual problems, we aim at an understanding of the appropriateness of vessel visualization techniques in VR. Therefore, this paper presents a user study that investigates the effects of three commonly used visualization techniques on depth perception. The set of visualization techniques comprises Phong shading, pseudo-chromadepth and fog shading. An immersive VR setup of the study using a head-mounted display (HMD) was compared to a traditional desktop setup. Results suggest that depth judgments are less error-prone and more certain in VR than in desktop environments. Moreover, depth-enhancing visualization techniques had greater effects in the desktop study compared to the VR study.
Exploiting BERT to Improve Aspect-Based Sentiment Analysis Performance on Persian Language

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it provides more thorough and useful information. However, there are few language-specific researches on Persian language. The present research aims to improve the ABSA on the Persian Pars-ABSA dataset. This research shows the potential of using pre-trained BERT model and taking advantage of using sentence-pair input on an ABSA task. The results indicate that employing Pars-BERT pre-trained model along with natural language inference auxiliary sentence (NLI-M) could boost the ABSA task accuracy up to 91% which is 5.5% (absolute) higher than state-of-the-art studies on Pars-ABSA dataset.
Norm Adjusted Proximity Graph for Fast Inner Product Retrieval

Efficient inner product search on embedding vectors is often the vital stage for online ranking services, such as recommendation and information retrieval. Recommendation algorithms, e.g., matrix factorization, typically produce latent vectors to represent users or items. The recommendation services are conducted by retrieving the most relevant item vectors given the user vector, where the relevance is often defined by inner product. Therefore, developing efficient recommender systems often requires solving the so-called maximum inner product search (MIPS) problem. In the past decade, there have been many studies on efficient MIPS algorithms. This task is challenging in part because the inner product does not follow the triangle inequality of metric space.
Proximal operator and optimality conditions for ramp loss SVM

Support vector machines with ramp loss ( $$L_r$$ -SVM) have attracted considerable attention due to the robustness of the ramp loss. However, the corresponding optimization problem is non-convex, and the given Karush–Kuhn–Tucker (KKT) conditions are only first-order necessary conditions. To enrich the optimality theory of $$L_r$$ -SVM, we first introduce and analyze the proximal operator for the ramp loss, and then establish a stronger optimality condition: P-stationarity, which is proved to be the first-order necessary and sufficient conditions for the local minimizer of $$L_r$$ -SVM. Finally, we define the P-support vectors based on the P-stationary point and show that under mild conditions, all of the P-support vectors for $$L_r$$ -SVM are on the two support hyperplanes.
Nothing to Hide? On the Security and Privacy Threats Beyond Open Data

People’s online activity continuously dumps personally identifiable information on the web. Alarmingly, this public information becomes a dangerous cyberweapon in the era of finely targeted cyberattacks. This article explores today’s cyberthreats around open data, from traditional ones to AI empowering, thus unveiling a range of vulnerabilities to which end-users are exposed.
Users’ perception of cybersecurity, trust and cloud computing providers’ performance

Purpose This study aims to develop a scale to measure the cloud provider’s performance and it investigates the factors that impact that performance from the users’ perspective. Design/methodology/approach This paper proposes a research framework, develops hypotheses and conducts a survey to test the framework. Findings The results from both ordinary least square regression and structural equation modeling analyzes indicate that information technology complexity negatively and significantly affects users’ perception of the cloud computing providers’ performance. Additionally, the trust in the supervisor significantly enhances the otherwise insignificant positive relationship between providers’ cybersecurity capability and users’ perception of their providers’ performance. Originality/value The research makes important contributions to the cloud computing literature, as it measures users’ perception of the cloud computing provider’s performance and links it with cybersecurity, technical complexity and incorporates both the trust in the client firm’s supervisor and the strength of cybersecurity offered by cloud computing provider.