Reviewerly/exHarmony huggingface dataset:
Interpretability Constraints and Criteria for Fuzzy Systems

Fuzzy systems are commonly considered suitable tools to express knowledge in a human comprehensible fashion. This kind of characterization makes them eligible for being applied in several contexts where interpretability is a major issue and humans may profit from a self-explanatory form of automatic computation. However, fuzzy systems are not interpretable per se and the simple adoption of a natural language representation does not guarantee full acceptance and plain confidence among human experts. A number of constraints and criteria have been proposed in literature to drive the design and the construction of fuzzy systems so that they can be deemed interpretable. The aim of this chapter is to provide a thorough exposition of constraints and criteria which have been variously adopted in the research community in this context of investigation. Due to their heterogeneity and multiplicity, we resorted to a particular arrangement of their presentation. By following a hierarchical organization, we start from the basic constituents of a fuzzy system (namely, the fuzzy sets) and we go through the other design levels where compound elements are involved: for each level, an exhaustive review of interpretability constraints and criteria is expounded supplying formal definitions, illustrative examples, and bibliographical references.
Who Controls Your Robot? An Evaluation of ROS Security Mechanisms

The Robot Operation System (ROS) is widely used in academia as well as the industry to build custom robot applications. Successful cyberattacks on robots can result in a loss of control for the legitimate operator and thus have a severe impact on safety if the robot is moving uncontrollably. A high level of security thus needs to be mandatory. Neither ROS 1 nor 2 in their default configuration provide protection against network based attackers. Multiple protection mechanisms have been proposed that can be used to overcome this. Unfortunately, it is unclear how effective and usable each of them are. We provide a structured analysis of the requirements these protection mechanisms need to fulfill by identifying realistic, network based attacker models and using those to derive relevant security requirements and other evaluation criteria. Based on these criteria, we analyze the protection mechanisms available and compare them to each other. We find that none of the existing protection mechanisms fulfill all of the security requirements. For both ROS 1 and 2, we discuss which protection mechanism are most relevant and give hints on how to decide on one. We hope that the requirements we identify simplify the development or enhancement of protection mechanisms that cover all aspects of ROS and that our comparison helps robot operators to choose an adequate protection mechanism for their use case.
The development of e-modules using Kodular software with problem-based learning models in momentum and impulse material

Abstract This research aims to the development of an e-module using Kodular software with a Problem-based Learning model on the momentum and impulse material. This study used the Research and Development method which is a research method used to produce a certain product and test the effectiveness of the product. The development procedure used a 4-D model. The 4-D model consists of 4 main stages, namely: Define, Design, Develop, and Disseminate. The data collection instrument used was a questionnaire given to the material expert and media expert to test the quality of e-module learning media using Kodular software with the Problem-based Learning model on momentum and impulse material. The final product produced has met the eligibility criteria with an average score of the assessment of material expert, 86,8%, media expert 84,8%, and education practitioner/teacher 87,2% in the highest feasible, category percentage for small group trial 86,8% and field trial 86,5%. The result of this research was an e-module with a Problem-Based Learning model that has been developed using Kodular software that accessible via smartphone.
The multi-scroll hyper-chaotic coexistence attractors and its application

Applying a chaotic system to digital image communication is an effective way to resist the attack of intelligent system with strong computing power. However, the traditional chaotic systems lack more complex dynamic behavior that is very valuable for a series of applications. Based on Lorenz system, this study proposes a hyper-chaotic system with multi-scroll coexistence attractors by using a piecework function. Through bifurcation diagram and Lyapunov exponents, we analyzed the dynamic behavior of the system, and observed multi-scroll coexistence attractors in the numerical simulation. The results show that compared with the traditional chaotic attractor, multi-scroll coexistence attractor has more complex structure and dynamic behavior. Furthermore, we design a cryptosystem based on this system for the digital image. Scrambling and diffusing images with the sequence of multi-scroll coexistence attractors can destroy the correlation of adjacent pixels and hide the information of all pixels. Theoretical analysis and simulation on key space, histogram, information entropy, key sensitivity and pixel correlation show that the encryption scheme is feasible and secure.
The challenges of migrating an active learning classroom online in a crisis

The coronavirus disease of 2019 (COVID-19) pandemic has severely crippled our globalized society. Despite the chaos, much of our civilization continued to function, thanks to contemporary information and communication technologies. In education, this situation required instructors and students to abandon the traditional face-to-face lectures and move to a fully online learning environment. Such a transition is challenging, both for the teacher tasked with creating digital educational content, and the student who needs to study in a new and isolated working environment. As educators, we have experienced these challenges when migrating our university courses to an online environment. Through this paper, we look to assist educators with building and running an online course. Before we needed to transition online, we researched and followed the best practices to establish various digital educational elements in our online classroom. We present these elements, along with guidance regarding their development and use. Next, we designed an empirical study consisting of two surveys, focus group discussions, and observations to understand the factors that influenced students' engagement with our online classroom. We used the same study to evaluate students' perceptions regarding our digital educational elements. We report the findings and define a set of recommendations from these results to help educators motivate their students and develop engaging digital educational content. Although our research is motivated by the pandemic, our findings and contributions are useful to all educators looking to establish some form of online learning. This includes developers of massive open online courses and teachers promoting blended learning in their classrooms.
Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network

Facial Emotion Recognition (FER) is the approach of detecting emotions of humans from facial expressions. Emotions are detected automatically by the human brain and hence, many computer aided techniques are implemented for recognizing emotions. Recognition of natural emotions is an exciting area with a broad range of real time applications like automated tutoring models, smart environments computer–human interaction, driver warning systems, and video and image retrieval. The major goal of this paper is to implement an intelligent model for FER. The proposed model involves few steps: (a) Face extraction (b) Image filtering, (c) Extraction of facial components, (d) Descriptor selection, and (d) Classification. Initially, the face is extracted from the input image by the Viola–Jones method, which is generally employed for the detection of object. Further, the noise of the image is filtered by the Gabor Filtering. Further, the facial components are extracted as features via the Affine-Scale-Invariant Feature Transform (ASIFT), which is the modified version of SIFT method. Since the length of the descriptors generated from ASIFT is high; the number of descriptors is reduced by the optimal descriptor selection approach with the aid of hybrid meta-heuristic algorithm termed as MV-WOA. The extracted descriptors are subjected to Neural Network (NN). As a modification to the existing machine learning algorithms, the number of hidden neurons in the NN is optimized by the proposed WOA+MVO algorithm. By conducting experiments, the results demonstrate that the developed system can perform better the recent traditional approaches for emotion recognition for classifying seven emotions like normal, smile, sad, surprise, anger, fear, and disgust.
Visual Perception Based Algorithm for Fast Depth Intra Coding of 3D-HEVC

3D-HEVC (The 3D Extension of High Efficiency Video Coding) is the newest 3D video coding standard, which enriches multimedia applications with the video format of multi-view plus depth. For the depth map coding in 3D-HEVC, the advanced coding tools enhance the coding efficiency of the depth map and the quality of the synthesized view. However, the time consumption and complexity of 3D-HEVC also increase significantly. This paper utilizes the characteristics of human visual system to propose a fast algorithm based on visual perception for the acceleration of the depth intra coding of 3D-HEVC. The depth map is segmented into different regions by Otsu's auto-thresholding. The dominate edge direction is categorized for each prediction unit. We detect the perceptual edge based on just noticeable depth difference model to extract the area that may affect the visual perception. According to depth map segmentation and edge distribution, we reduce the corresponding intra angular modes and determine whether to perform depth modelling mode. We also incorporate the boundary continuity and rate-distortion cost thresholding to propose the fast coding unit decision. The experimental results show that the proposed algorithm eliminates 53.09% of the depth coding time with only 0.15% BD-BR on average. The coding performance of the proposed algorithm outperforms the previous works significantly.
End-to-end aspect-based sentiment analysis with hierarchical multi-task learning

End-to-end aspect-based sentiment analysis (E2E-ABSA) is a sequence labeling task which detects aspect terms and the corresponding sentiment simultaneously. Previous works ignore the useful task-specific knowledge and embed the vital aspect and sentiment attributes implicitly in the intermediate layers. In this paper, we propose a hierarchical multi-task learning framework, which explicitly leverages task-related knowledge via the supervision of intermediate layers. Specifically, aspect term extraction, sentiment lexicon detection, and aspect sentiment detection are designed to encode the aspect boundary and sentiment information. The tasks are in charge of different perspectives and levels of knowledge, which provide multi-fold regulation effects to optimize the main task. Unlike vanilla multi-task learning, all the tasks are integrated into a hierarchical structure to help the higher-level tasks make full use of the lower-level tasks' information. Experimental results on three datasets demonstrate that the proposed method achieves state-of-the-art results. Further analysis shows that the proposed method achieves better performance than single-task and vanilla multi-task learning methods and yields a more discriminative feature representation.
A universal scheme for robust self-testing in the prepare-and-measure scenario

We consider the problem of certification of arbitrary ensembles of pure states and projective measurements solely from the experimental statistics in the prepare-and-measure scenario assuming the upper bound on the dimension of the Hilbert space. To this aim, we propose a universal and intuitive scheme based on establishing perfect correlations between target states and suitably-chosen projective measurements. The method works in all finite dimensions and allows for robust certification of the overlaps between arbitrary preparation states and between the corresponding measurement operators. Finally, we prove that for qubits, our technique can be used to robustly self-test arbitrary configurations of pure quantum states and projective measurements. These results pave the way towards the practical application of the prepare-and-measure paradigm to certification of quantum devices.
TransforMED: End-to-Εnd Transformers for Evidence-Based Medicine and Argument Mining in medical literature

Argument Mining (AM) refers to the task of automatically identifying arguments in a text and finding their relations. In medical literature this is done by identifying Claims and Premises and classifying their relations as either Support or Attack. Evidence-Based Medicine (EBM) refers to the task of identifying all related evidence in medical literature to allow medical practitioners to make informed choices and form accurate treatment plans. This is achieved through the automatic identification of Population, Intervention, Comparator and Outcome entities (PICO) in the literature to limit the collection to only the most relevant documents. In this work, we combine EBM with AM in medical literature to increase the performance of the individual models and create high quality argument graphs, annotated with PICO entities. To that end, we introduce a state-of-the-art EBM model, used to predict the PICO entities and two novel Argument Identification and Argument Relation classification models that utilize the PICO entities to enhance their performance. Our final system works in a pipeline and is able to identify all PICO entities in a medical publication, the arguments presented in them and their relations.
Smart Contracts in the 5G Roaming Architecture: The Fusion of Blockchain with 5G Networks

The rollout of fifth generation (5G) cellular network technology has generated a new surge of interest in the potential of blockchain to automate various use cases involving cellular networks. 5G is indeed expected to offer new market opportunities for small and large enterprises alike. In this article, we introduce a new roaming network architecture for 5G based on a permissioned blockchain platform with smart contracts. The proposed solution improves the visibility for mobile network operators of their subscribers' activities in the visited network, as well as enabling quick payment reconciliation and reducing fraudulent transactions. The article further reports on the methodology and architecture of the proposed blockchain-based roaming solution using the Hyperledger platform.
A dynamic goal adapted task oriented dialogue agent

Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent( DevVA ), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serving action. User sentiment appears to be an appropriate indicator for goal discrepancy that guides the agent to complete the user’s desired task with gratification. The negative sentiment expressed by the user about an aspect of the provided choice is treated as a discrepancy that is being resolved by the GDM depending upon the observed discrepancy and current dialogue state. The goal update capability and the VA’s interactiveness trait enable end-users to accomplish their desired task satisfactorily. Findings The obtained experimental results illustrate that DGDVA can handle dynamic goals with maximum user satisfaction and a significantly higher success rate. The interaction drives the user to decide its final goal through the latent specification of possible choices and information retrieved and provided by the dialogue agent. Through the experimental results (qualitative and quantitative), we firmly conclude that the proposed sentiment-aware VA adapts users’ dynamic behavior for its goal setting with substantial efficacy in terms of primary objective i.e., task success rate (0.88). Practical implications In real world, it can be argued that many people do not have a predefined and fixed goal for tasks such as online shopping, movie booking &amp; restaurant booking, etc. They tend to explore the available options first which are aligned with their minimum requirements and then decide one amongst them. The DGDVA provides maximum user satisfaction as it enables them to accomplish a dynamic goal that leads to additional utilities along with the essential ones. Originality To the best of our knowledge, this is the first effort towards the development of A Dynamic Goal Adapted Task-Oriented Dialogue Agent that can serve user goals dynamically until the user is satisfied.
3D Human Pose, Shape and Texture from Low-Resolution Images and Videos

3D human pose and shape estimation from monocular images has been an active research area in computer vision. Existing deep learning methods for this task rely on high-resolution input, which however, is not always available in many scenarios such as video surveillance and sports broadcasting. Two common approaches to deal with low-resolution images are applying super-resolution techniques to the input, which may result in unpleasant artifacts, or simply training one model for each resolution, which is impractical in many realistic applications. To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed method is able to learn 3D body pose and shape across different resolutions with one single model. The self-supervision loss enforces scale-consistency of the output, and the contrastive learning scheme enforces scale-consistency of the deep features. We show that both these new losses provide robustness when learning in a weakly-supervised manner. Moreover, we extend the RSC-Net to handle low-resolution videos and apply it to reconstruct textured 3D pedestrians from low-resolution input. Extensive experiments demonstrate that the RSC-Net can achieve consistently better results than the state-of-the-art methods for challenging low-resolution images.
Pose transfer generation with semantic parsing attention network for person re-identification

Pose variation as one of the key factors prevents the network from learning a robust person re-identification (Re-ID) model. We in this paper propose a novel Semantic Parsing Attention Network (SPAN) to transfer person pose from the source to another. SPAN is constructed with several Semantic Parsing Attention Blocks. Each block focuses on a local transfer of the human manifold, which can attend to put the sample condition patches to the corresponding location of the target image. The introduction of the binary segmentation mask and the semantic parsing map is not only significant for the seamless stitching of the foreground and the background, but also decreases the computation load considerably. Compared with other methods, our network can characterize better body shape as well as keeping clothing attributes during the pose transfer. And our synthesized image can obtain better appearance and shape consistency related to the source image. Experimental results are provided to show the superiority of our network on both qualitative and quantitative results on Market-1501 and DeepFashion datasets. Furthermore, extensive experiments are also conducted on person Re-ID systems trained with the augmented data, where our network has the ability to improve the person Re-ID accuracy.
Binary Linear Codes With Few Weights From Two-to-One Functions

In this paper, we apply two-to-one functions over b F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2<sup>n</sup></sub> in two generic constructions of binary linear codes. We consider two-to-one functions in two forms: (1) generalized quadratic functions; and (2) (x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2t</sup> +x) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sup> with gcd(t, n)=gcd(e, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1)=1. Based on the study of the Walsh transforms of those functions or their variants, we present many classes of linear codes with few nonzero weights, including one weight, three weights, four weights, and five weights. The weight distributions of the proposed codes with one weight and with three weights are determined. In addition, we discuss the minimum distance of the dual of the constructed codes and show that some of them achieve the sphere packing bound. Moreover, examples show that some codes in this paper have best-known parameters.
Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble

Data streams can be defined as the continuous stream of data coming from different sources and in different forms. Streams are often very dynamic, and its underlying structure usually changes over time, which may result to a phenomenon called concept drift. When solving predictive problems using the streaming data, traditional machine learning models trained on historical data may become invalid when such changes occur. Adaptive models equipped with mechanisms to reflect the changes in the data proved to be suitable to handle drifting streams. Adaptive ensemble models represent a popular group of these methods used in classification of drifting data streams. In this paper, we present the heterogeneous adaptive ensemble model for the data streams classification, which utilizes the dynamic class weighting scheme and a mechanism to maintain the diversity of the ensemble members. Our main objective was to design a model consisting of a heterogeneous group of base learners (Naive Bayes, k-NN, Decision trees), with adaptive mechanism which besides the performance of the members also takes into an account the diversity of the ensemble. The model was experimentally evaluated on both real-world and synthetic datasets. We compared the presented model with other existing adaptive ensemble methods, both from the perspective of predictive performance and computational resource requirements.