Reviewerly/exHarmony huggingface dataset:
Sentence Meaning Representations Across Languages: What Can We Learn from Existing Frameworks?

This article gives an overview of how sentence meaning is represented in eleven deep-syntactic frameworks, ranging from those based on linguistic theories elaborated for decades to rather lightweight NLP-motivated approaches. We outline the most important characteristics of each framework and then discuss how particular language phenomena are treated across those frameworks, while trying to shed light on commonalities as well as differences.
Multi-UAV Target-Finding in Simulated Indoor Environments using Deep Reinforcement Learning

Searching indoor environments in the presence of unknown obstacles with multiple UAV agents remains a challenge. This paper presents a framework for target-finding using a combination of traditional POMDP based planning and Deep Reinforcement Learning. The implementation breaks the problem into two separate stages of planning and control, with both stages modelled as a Partially Observable Markov Decision Process (POMDP). Global decentralised planning is provided using a modern online POMDP solver, while a modern Deep Reinforcement Learning algorithm is used to provide a policy for local control. Our results indicate that such a framework is capable of target-finding within a simulated indoor test environment in the presence of unknown obstacles, and once extended to real-world operation could enable UAVs to be applied in an increasing number of applications.
Secondary studies in the academic context: A systematic mapping and survey

Several researchers have reported their experiences in applying secondary studies (Systematic Literature Reviews — SLRs and Systematic Mappings — SMs) in Software Engineering (SE). However, there is still a lack of studies discussing the value of performing secondary studies in an academic context. The main goal of this study is to provide an overview on the use of secondary studies in an academic context. Two empirical research methods were used. Initially, we conducted a SM to identify the available and relevant studies on the use of secondary studies as a research methodology for conducting SE research projects. Secondly, a survey was performed with 64 SE researchers to identify their perception related to the value of performing secondary studies to support their research projects. Our results show benefits of using secondary studies in the academic context, such as providing an overview of the literature as well as identifying relevant research literature on a research area enabling to find reasons to explain why a research project should be approved for a grant and/or supporting decisions made in a research project. Difficulties faced by SE graduate students with secondary studies are that they tend to be conducted by a team and it demands more effort than a traditional review. Secondary studies are valuable to graduate students. They should consider conducting a secondary study for their research project due to the benefits and contributions provided to develop the overall project. However, the advice of an experienced supervisor is essential to avoid bias. In addition, the acquisition of skills can increase student’s motivation to pursue their research projects and prepare them for both academic or industrial careers.
Image Morphing in Deep Feature Spaces: Theory and Applications

Abstract This paper combines image metamorphosis with deep features. To this end, images are considered as maps into a high-dimensional feature space and a structure-sensitive, anisotropic flow regularization is incorporated in the metamorphosis model proposed by Miller and Younes (Int J Comput Vis 41(1):61–84, 2001) and Trouvé and Younes (Found Comput Math 5(2):173–198, 2005). For this model, a variational time discretization of the Riemannian path energy is presented and the existence of discrete geodesic paths minimizing this energy is demonstrated. Furthermore, convergence of discrete geodesic paths to geodesic paths in the time continuous model is investigated. The spatial discretization is based on a finite difference approximation in image space and a stable spline approximation in deformation space; the fully discrete model is optimized using the iPALM algorithm. Numerical experiments indicate that the incorporation of semantic deep features is superior to intensity-based approaches.
AI Crimes: A Classification

Intelligent and machine learning systems have infiltrated cyber-physical systems and smart cities with technologies such as internet of things, image processing, robotics, speech recognition, self-driving, and predictive maintenance. To gain user trust, such systems must be transparent and explainable. Regulations are required to control crimes associated with these technologies. Such regulations and legislations depend on the severity of the artificial intelligence (AI) crimes subject to these regulations, and on whether humans and/or intelligent systems are responsible for committing such crimes, and therefore can benefit from a classification tree of AI crimes. The aim of this paper to review prior work in ethics for AI, and classify AI crimes by producing a classification tree to assist in AI crime investigation and regulation.
Human Detection Based on Improved Mask R-CNN

Abstract Human detection has been widely concerned by academic and industrial circles. The gradual maturity of deep learning framework further improves the accuracy and speed of detection. However, the relatively mature human detection methods cannot get accurate detection results because of the complex background, shooting angle and many limitations of human behaviour. Aiming at solving the problems of human detection in complex scenes, a novel human detection algorithm based on the improved mask R-CNN framework is proposed by implementing the leading research results of object detection through deep learning. The algorithm combines ResNet and FPN to extract the features of the image, and then takes advantage of RoIAlign and fine-grained Slic to proofread the pixels. In the experiments that compared with the original mask R-CNN algorithm on the same data set was carried out to verify the effectiveness of the proposed algorithm. The mAP and AR value of the improved mask R-CNN algorithm is greater than that of the mask R-CNN algorithm when the IoU is 0.5-0.95, showing that the improved mask R-CNN framework was able to detect human from video better.
Metro Maps on Octilinear Grid Graphs

Abstract Schematic transit maps (often called “metro maps” in the literature) are important to produce comprehensible visualizations of complex public transit networks. In this work, we investigate the problem of automatically drawing such maps on an octilinear grid with an arbitrary (but optimal) number of edge bends. Our approach can naturally deal with obstacles that should be respected in the final drawing (points of interest, rivers, coastlines) and can prefer grid edges near the real‐world course of a line. This allows our drawings to be combined with existing maps, for example as overlays in map services. We formulate an integer linear program which can be used to solve the problem exactly. We also provide a fast approximation algorithm which greedily calculates shortest paths between node candidates on the underlying octilinear grid graph. Previous work used local search techniques to update node positions until a local optimum was found, but without guaranteeing octilinearity. We can thus calculate nearly optimal metro maps in a fraction of a second even for complex networks, enabling the interactive use of our method in map editors.
Novel Container Cloud Elastic Scaling Strategy based on Kubernetes

Kubernetes is a cluster management system based on container technology, which is responsible for managing large-scale containers. However, the existing responsive scaling strategy of the Kubernetes platform has a response delay, and the resource indicators cannot meet the web system's requirements for service quality. In order to solve this problem, this paper proposes a new container cloud elastic scaling strategy, which combines a prediction model and a queuing network to automatically predict the arrival rate of requests for web applications in the future and find the optimal number of Kubernetes service replica sets. Finally, in order to prove that our strategy is effective, a Kubernetes cluster is set up in a laboratory environment, and experiments are designed to verify it. The results show that our proposed strategy can achieve predictive scaling and avoid response delay caused by peak requests. At the same time, our elastic scaling strategy can also improve resource utilization to a certain extent.
NewsViz: Depicting and Controlling Preference Profiles Using Interactive Treemaps in News Recommender Systems

News articles are increasingly consumed digitally and recommender systems (RS) are widely used to personalize news feeds for their users. Thereby, particular concerns about possible biases arise. When RS filter news articles opaquely, they might "trap" their users in filter bubbles. Additionally, user preferences change frequently in the domain of news, which is challenging for automated RS. We argue that both issues can be mitigated by depicting an interactive version of the user's preference profile inside an overview of the entire domain of news articles. To this end, we introduce NewsViz, a RS that visualizes the domain space of online news as treemap, which can interactively be manipulated to personalize a feed of suggested news articles. In a user study (N=63), we compared NewsViz to an interface based on sliders. While both prototypes yielded high results in terms of transparency, recommendation quality and user satisfaction, NewsViz outperformed its counterpart in the perceived degree of control. Structural equation modeling allows us to further uncover hitherto underestimated influences between quality aspects of RS. For instance, we found that the degree of overview of the item domain influenced the perceived quality of recommendations.
A survey of cyber security in the Swedish manufacturing industry

In this paper we explore cyber security practices in Swedish manufacturing firms. Manufacturing is being transformed by new technologies under the label of smart industry or industry 4.0. Most of these technologies are either digital themselves or depend on digital connectivity. Their use is made possible by electronic sensors, actuators, and other devices as well as by data-driven analysis. This technological change entails a fundamental shift in risk and security as devices become interconnected, making information and control transmissible both within and to varying degree outside the firm's organization. These issues must be addressed to prevent both unintentional and intentional security incidents. Thus, there will be no smart industry without cyber security. Based on a sector-wide survey with 649 respondents (17% response rate) carried out in collaboration with the Association of Swedish Engineering Industries, we map risk perception and the controls put in place to address these risks across firms. We present three primary findings: (i) Compared to how firms value further investments in digitalization, risk perception related to cyber security issues is fairly low and business interruption is a greater cause for worry than data breach, (ii) there is a gap between the anticipated impact of digitalization and the perceived need for cyber security measures across business functions within firms, and (iii) the implementation of cyber security measures is still in its infancy with a significant bias towards technological measures, leaving organizational and social cyber security measures underrepresented. The paper is concluded with the identification of a few interesting follow-up questions for future work.
Differentially Private Learning of Distributed Deep Models

This study presents an optimal differential privacy framework for learning of distributed deep models. The deep models, consisting of a nested composition of mappings, are learned analytically in a private setting using variational optimization methodology. An optimal (ε,δ)-differentially private noise adding mechanism is used and the effect of added data noise on the utility is alleviated using a rule-based fuzzy system. The private local data is separated from globally shared data through a privacy-wall and a fuzzy model is used to aggregate robustly the local deep fuzzy models for building the global model.
QUESTO: Interactive Construction of Objective Functions for Classification Tasks

Abstract Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta‐information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain‐specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.
What can we expect from the future? The impact of Artificial Intelligence on Society

The impact of the digital revolution has influenced society significantly in many ways, including with Artificial Intelligence (AI). The advantages and disadvantages of AI and what can be and should be done in order to influence that in a positive way are discussed. The study is based on interviews (ten) and survey answers (from 100 respondents). The survey results show that there is a general concern about the impact of AI in the future (the negative impact on work and related to a general loss of control). Furthermore, more than 50% of the answers lead to the thought that “Humans will learn to use the power of computers to improve their own skills and be ahead of AI”. As concerns the interviews, it was interesting to realize that practical courses, such as students studying engineering, were the ones who were afraid of AI instead of the social ones. This may be because the engineering students are the ones who know more about AI so they better realize the possible implications of AI on their future jobs. Another possible reason is that non-engineering students believe that human sensibility needed in their fields of study is more difficult to reproduce by AI machines than technical skills present in other fields of study/jobs.
<scp>High‐Resolution</scp> Breast <scp>MRI</scp> Reconstruction Using a Deep Convolutional Generative Adversarial Network

A generative adversarial network could be used for high-resolution (HR) medical image synthesis with reduced scan time.To evaluate the potential of using a deep convolutional generative adversarial network (DCGAN) for generating HRpre and HRpost images based on their corresponding low-resolution (LR) images (LRpre and LRpost ).This was a retrospective analysis of a prospectively acquired cohort.In all, 224 subjects were randomly divided into 200 training subjects and an independent 24 subjects testing set.Dynamic contrast-enhanced (DCE) MRI with a 1.5T scanner.Three breast radiologists independently ranked the image datasets, using the DCE images as the ground truth, and reviewed the image quality of both the original LR images and the generated HR images. The BI-RADS category and conspicuity of lesions were also ranked. The inter/intracorrelation coefficients (ICCs) of mean image quality scores, lesion conspicuity scores, and Breast Imaging Reporting and Data System (BI-RADS) categories were calculated between the three readers.Wilcoxon signed-rank tests evaluated differences among the multireader ranking scores.The mean overall image quality scores of the generated HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (4.77 ± 0.41 vs. 3.27 ± 0.43 and 4.72 ± 0.44 vs. 3.23 ± 0.43, P < 0.0001, respectively, in the multireader study). The mean lesion conspicuity scores of the generated HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (4.18 ± 0.70 vs. 3.49 ± 0.58 and 4.35 ± 0.59 vs. 3.48 ± 0.61, P < 0.001, respectively, in the multireader study). The ICCs of the image quality scores, lesion conspicuity scores, and BI-RADS categories had good agreements among the three readers (all ICCs >0.75).DCGAN was capable of generating HR of the breast from fast pre- and postcontrast LR and achieved superior quantitative and qualitative performance in a multireader study.3 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1852-1858.
Survey of Attacks and Defenses against SGX

The great abundance of information brings us convenient life. At the same time, how to prevent malicious attackers from stealing user data or privacy is a hot and difficult issue in the current research of information security. In 2013, Intel proposed a new processor security technology SGX(Software Guard Extensions) at the HASP conference, which can provide a trusted zone named enclave on the computing platform to protect the confidentiality and integrity of user code and data. SGX is a breakthrough research achievement in the field of information security, which is of great significance to individual users and tenants of cloud computing platforms. In order to better study the attacks and defense technologies against SGX, the threat model against SGX attacks is introduced, the types of attacks against SGX are summarized, the defense measures against SGX attacks are analyzed, and the possible future attacks against SGX and the Defense technology was explored.
Multilevel Cross-Aware RGBD Indoor Semantic Segmentation for Bionic Binocular Robot

Scene understanding is one of the foundations for robots to achieve true artificial intelligence. Semantic segmentation that imitates the mechanism of human visual system can effectively promote the correctness of scene understanding. It conforms to the basic principle of human environment perception and enables robots to better serve human society. In this paper, we propose a multilevel cross-aware network (MCA-Net) for RGBD semantic segmentation. It utilizes basic residual structure to encode texture information and depth geometric information respectively. In view of the flow pattern of different visual features in the visual pathway of human brain, our MCA-Net attempts to jointly reason about 2D appearance and depth geometric information. Therefore, multilevel cross-aware fusion modules are designed to fuse multi-scale complementary features extracted from RGB and depth images. As the depth and color information are independent, the reasonable combination of depth and RGB images can improve the quality of semantic labeling. Experiments conducted on ScanNetv2 dataset show that the proposed network produces high quality segmentation results of RGBD images and outperforms state-of-the-art methods. Furthermore, the semantic labeling results on the bionic binocular robot in real-office scenes further demonstrate the effectiveness of the proposed MCA-Net.
Accuracy weighted diversity-based online boosting

Target distributional change occurring in a data stream known as concept drift, causes a challenging task for an online learning method, as the accuracy of an online learning method may decrease due to these changes. In this paper, the Accuracy Weighted Diversity-based Online Boosting (AWDOB) method has been proposed, which is based on Adaptable Diversity-based Online Boosting (ADOB) and, other modifications. More precisely, AWDOB uses the proposed accuracy weighting scheme which is based on previous expert’s results of the sums of correctly classified and incorrectly classified instances to calculate the weight of current expert, which improved the overall accuracy of the AWDOB. Experiments were conducted to compare the accuracy results of AWDOB against other methods using ten real-world datasets and thirty-two artificial datasets. Artificial datasets were generated by the four artificial data generators which included gradual and abrupt concept drifts within them. Experimental results suggest that AWDOB beats the accuracy results of other tested methods.
Marking Essays Automatically

We propose an automatic marking scheme for grading essay type assessment. The scheme is built on top of the BERT model. First, it obtains the meaning of the sentences in the students' answers and the specimen answer. Then, it uses a classifier to determine the similarity between the meaning of the students' answers and the specimen answer. Experiments show that the scheme achieves high accuracy when marking assignments of specific topics.
A Nonconvex Archetypal Analysis for One-Class Classification Based Anomaly Detection in Cyber-Physical Systems

Data-driven anomaly detection is one of the central issues for the implementation of predictive maintenance in cyber-physical systems (CPS). The increasing nonstationary dynamics in CPS lead to complex shapes of collected process data, e.g., convex and nonconvex. Algorithms with the ability of handling nonconvex data are desired for anomaly detection tasks in CPS. Archetypal analysis selects extreme points (archetypes) to represent a dataset-mainly to improve run times, e.g., for anomaly detection. The classic archetypal analysis uses convex combinations of archetypes to represent a set of observations (data points). This leads to the performance depression of the classic archetypal analysis methods for anomaly detection tasks on nonconvex datasets. Such nonconvex sets are typical for CPS. In this article, the anomaly detection tasks are considered as one-class classification problem due to the lack of abnormal samples. A novel nonconvex archetypal one-class classification algorithm is proposed to address the challenge of nonconvex data, which combines the random projection and the AdaBoost algorithm. The major advantages of this method are its high efficiency, flexibility, and its ability to handle both convex and nonconvex datasets, i.e., it can be applied to CPS analysis tasks. The usefulness of the presented approach is evaluated for fault diagnosis tasks in CPS.
Group Recommender User Interfaces for Improving Requirements Prioritization

Requirements engineering is one of the most critical phases in the context of software development. Unclear textual specifications of requirements, hidden dependencies between requirements, and suboptimal prioritizations and release plans represent the major reasons for project delays and even cancellation. In this paper, we show how group recommender user interfaces can help to improve the quality of requirements engineering processes. To that end, we developed a novel group recommendation approach that focuses on the aspect of improving requirements prioritization by making preference elicitation processes more flexible as well as by introducing innovative user interfaces that foster information exchange among stakeholders. We conducted a large user study (N=313 participants) to evaluate our approach. The evaluation results indicate that argumentation-based user interfaces in a group setting trigger more rating and communication activity among the group members which significantly improves the quality of the prioritization process. Our main contributions are twofold: (1) more flexibility of the requirements evaluation by supporting the delegation of votes to experts and (2) an increased engagement of the stakeholders responsible for the requirements.