Reviewerly/exHarmony huggingface dataset:
Co-designing with Visually Impaired Children

Co-design methodology takes user-centred approach a step ahead by including end-users directly in the design process. It ensures that their needs are well addressed by involving them as true experts of the experience. But when it comes to visually impaired (VI) children, it presents some unique challenges which mostly remain unaddressed with limited research in the domain. The paper addresses this gap by presenting a set of learnings which can assist the development of techniques for co-designing with VI children. We explore the co-design methodology for conducting ideation as part of a broader project centred around indoor navigation for VI children. We begin with a discussion of existing literature around co-design techniques for children and related work done for VI adults. Primary research was further done to understand the routine activities that VI children involve in and the challenges they face. Inspired by the insights gathered from this research, new co-design techniques were ideated. In all, five activities were chosen and conducted during the co-design session with six VI children. Findings and observations from this session are presented in the paper. Activities were found to be useful and thought provoking amongst children. It helped them in thinking and expressing new creative ideas which finally contributed in determining key directions for the solution. Finally, we present our learnings and discuss the best practices for conducting co-design sessions with VI children. These learnings can be further expanded and referred by fellow practitioners to build more innovative and accessible solutions for VI children.
Optimization of AMR Local Turning Path Based on Double Line Constraint

Automated Mobile Robot (AMR) is prone to collide and be interfered with other obstacles in a complex and dangerous environment with very narrow corners. To solve the problem, a double line constraint turning path planning algorithm was used to make turns in this study, which calculates the envelope edge lines expression, the turning envelope space, the required maximum turning length and width, and other parameters. Compared with pivot steering and arc steering, the turning required envelope area is reduced by 23.4% and 4.8% respectively. The maximum turning length and width required is reduced is reduced by 23.4% and 4.8% respectively. This paper also put forward a safety principle, which can determine the intersection of double lines and ensure safety. Simulation and experiment show that this turning method can ensure the safety distance between the AMR edge and the road edge, and improve the safety and trafficability.
Unsupervised Subspace Extraction via Deep Kernelized Clustering

Feature extraction has been widely studied to find informative latent features and reduce the dimensionality of data. In particular, due to the difficulty in obtaining labeled data, unsupervised feature extraction has received much attention in data mining. However, widely used unsupervised feature extraction methods require side information about data or rigid assumptions on the latent feature space. Furthermore, most feature extraction methods require predefined dimensionality of the latent feature space,which should be manually tuned as a hyperparameter. In this article, we propose a new unsupervised feature extraction method called Unsupervised Subspace Extractor ( USE ), which does not require any side information and rigid assumptions on data. Furthermore, USE can find a subspace generated by a nonlinear combination of the input feature and automatically determine the optimal dimensionality of the subspace for the given nonlinear combination. The feature extraction process of USE is well justified mathematically, and we also empirically demonstrate the effectiveness of USE for several benchmark datasets.
Partitional clustering based on PCA method for segmentation of products

This paper focuses on grouping e-commerce data for product segmentation with dimension reduction using Principal Component Analysis (PCA) on the data to transform the original data to the top principal components' feature space. K-means based on principal components is used for forming the clusters. Then, we conducted the performance evaluation using metrics to calculate the goodness of the clustering technique. The segments derived from PCA in conjunction with k-means are able to provide interesting insights for data-driven decision making in practice. The results indicate three segments of products: The least sold products with an acceptable rating; the most expensive and highest rated products; the more economical and best-selling products.
Vision-based Position estimation and Indoor scene recognition algorithm for Quadrotor Navigation

Abstract In this paper, an effective and simple Grid based vanishing point detection position estimation algorithm and Enhanced GIST descriptors based indoor scene recognition algorithm for navigation of MAV in indoor corridor environment is described. Two different classifiers, k-nearest neighbour classifier and support vector machine is employed for the categorization of indoor scenes into corridor, staircase or room. Indoor scene classification was performed on Dartaset-1. In the training phase of the indoor scene recognition algorithm, GIST, HODMG and Enhanced-GIST feature vectors are extracted for all the indoor training images in the Dataset-1 and indoor scene classifiers are trained for the extracted image feature vectors and assigned image labels of the indoor scenes (corridor-1, staircase-2 and room-3). In the testing phase of the indoor scene recognition algorithm, for each unknown test image frame GIST, HODMG and Enhanced-GIST feature vectors are extracted and the indoor scene classification is performed using a trained scene recognition model. The proposed indoor scene recognition algorithm using SVM with Enhanced GIST descriptors produced high recognition rates of 99.33% compared to the KNN classifiers. After recognizing the indoor scene as corridor, the MAV has to estimate its position based on the detection of vanishing point in the indoor corridor image frames. Experimental results show that the proposed method is suitable for real time operations.
Forming 2D3D Operator Control for Ambiguous Image Environments

At the heart of developing human-robotic systems is the usage of passive 2D image sensors to extrapolate viable 3D (localization) information for higher-order tasks. However, under ambiguous environments, notions of mistrust begins to form between an overseeing physical operator and the deployed autonomous system that generates blackbox actions. Ideally, there should be a natural interplay between the operator and autonomy whereby decision making is ultimately driven by an operator “on-the-fly” yet also informed by the autonomous counterpart that may assist in such decisions. To do so, both physical operators and their autonomous actions should be canonically equivalent. That is, pure human-vision navigation generally “segments” (visualizes) 2D information for 3D localization in a joint and coupled fashion. On the other hand, robotic vision task of localization should also be naturally coupled yet is often decoupled (i.e., 2D image segmentation and 2D3D pose estimation are often considered different tasks, but are in fact chicken-and-egg problems in regards to 3D localization). To this end, the main aim of this note is to introduce a 2D3D feedback control theoretic framework to fuse such concepts in order to inject real-time operator in-loop to provide control (if needed) in ambiguous situations. The resulting framework can then be naturally extendable to real-time robotic systems.
Blockchain Consensus Mechanism Design for Food Safety Traceability

Food security refers to the food production, storage and circulation processes. In recent years, a series of food quality and safety issues have occurred, which have triggered a trust crisis among consumers. In order to serve the problem of food safety traceability, this article designs a food safety traceability system, which concludes the blockchain consensus mechanism, data model and system external interface, to track the food’s production, transportation, sales and other traceability information. This method can fully use the blockchain characteristics, like decentralization, non-tampering and traceability, to ensure the food traceability information’s authenticity and reliability, which can promote the safe food consumption.
Notes on Advertising Logic in Food Applications

The objective of this article is to understand the way of communicative interaction of applications currently used in the sectors of material culture food in the city of São Paulo, Brazil, in order to reflect on the types of functionalities and logics that these devices establish in the mediatization of consumption and the cultures in which they operate via processes of interaction. At the same time, one observes such applications with a new dimension of work in advertising with their institutionality and promocionalities.
Research on Airport Visibility Detection under Complex Conditions

In order to solve the problem of insufficient accuracy and stability of airport visibility detection under existing complex conditions, a SVM model based on improved feature extraction method was proposed to study the airport visibility detection method under complex conditions, which combined thermodynamic image algorithm and gray histogram to improve feature vector extraction of video images respectively. On this basis, SVM models were established respectively. The method which can be used to improve the detection accuracy of the airport visibility under complex conditions was obtained by comparing the accuracy of the two SVM models. The results show that the improved method has the advantages of fast detection speed, higher accuracy and better stability, and has good practical significance. It provides some application and reference for the visibility detection and identification work of airport surveillance video under complex conditions.
Features Analysis of a Patent Act Based on Legal Condition–Effect Structure: Conversion of Law Texts into Logical Formulas for a Learning Support System

With the development of information and communications technology, intellectual property education is expected to become increasingly important. However, it has been shown that intellectual property education is insufficiently conducted in university education faculties of engineering. Therefore, we previously focused on engineering students learning logic circuits to create an e-learning system that displays articles as logic circuits. Evaluation experiments demonstrated the learning effect of this system, but it could not handle all intellectual property laws because it required manually converting law texts into logic circuits in the problem creation process. To address this problem, in this paper we examine a method for converting intellectual property laws into logical formulas. Previous studies have converted law texts into logical formulas to perform consistent tests of law texts, whereas this study aims at more human-comprehensible visualizations. We divide law texts into constituents based on a legal condition–effect structure and convert them into propositional calculus. This paper presents the results of an attempt to convert 101 law texts of typical patent acts in intellectual property law into logical formulas. From the results of integrating the logical formulas into our law text learning system, we describe future policies.
BIPRE: Bi-directional Inter-Personal Relationship Extraction Task

The existing inter-personal relationship extraction task (the IPRE task) is a one-way task, and it does not take relationship in the opposite direction into consideration. For example, the IPRE task can extract the relationship in “A is the father of B”, but in such a circumstance, one still does not know whether the relationship between B to A is a son-father relationship or a daughter-father relationship. For many natural language processing tasks, it is important to identify specific and clear bi-directional relationships, especially in dialogue and question answering systems. Hence, this paper proposes a Bi-directional InterPersonal Relationship Extraction Task (BIPRE), establishes a corresponding dataset, and implements a Bert-based relationship extraction baseline model. Also, we propose a new model which combines the position and gender information of the persons. The experimental results show that our new model is effective and there is still much room for improvement in this task.
Explainable Reasoning in Face of Contradictions: From Humans to Machines

A well-studied trait of human reasoning and decision-making is the ability to not only make decisions in the presence of contradictions, but also to explain why a decision was made, in particular if a decision deviates from what is expected by an inquirer who requests the explanation. In this paper, we examine this phenomenon, which has been extensively explored by behavioral economics research, from the perspective of symbolic artificial intelligence. In particular, we introduce four levels of intelligent reasoning in face of contradictions, which we motivate from a microeconomics and behavioral economics perspective. We relate these principles to symbolic reasoning approaches, using abstract argumentation as an exemplary method. This allows us to ground the four levels in a body of related previous and ongoing research, which we use as a point of departure for outlining future research directions.
Detection and Prevention of Hypervisor and VM Attacks

Virtualization is a rapidly evolving technology in big data, mobile cloud computing and grid computing due to its nature of less cost, flexibility, efficiency and resources utilization with proper management and efficiency in consuming power in recent years. Therefore, many platforms have evolved in the scenario such as VMware ESXi, Microsoft Hyper-V server and Xen Hypervisors etc VMware ESXi is one of the high level ways of providing internal cloud security infrastructures. As the ESXi have the peculiarity of providing community interest but anyhow it somewhat lacks security concerns. In this Paper we implemented two major attacks former attack is known as hypervisor and later one is VM attack. Kali Linux is one of the striking tool which helps with its advance features in detecting and uncovering various vulnerabilities, from the machine.. For this purpose, Kali Linux setup and installation will be explored in details. Moreover, a method to prevent vulnerable server is also presented. In Future we are looking forward to explore simulated attacks on the server by exploiting benefits from firewalls
One WS-Management Based CoT System Management Model in Edge Computing Environment

The paper discusses the development trend of protocols for CoT (Cloud-of-Things) system management and defines one WS-Management based CoT system management model in Edge Computing environment. Wiseman is one implementation project of WS-Management. The paper compares three Wiseman based implementation scheme of this model. The model can facilitate the CoT systems management function. The comparison result can help venders to choose appropriate CoT system management modules structure. The future research will focus on the different function construction and performance enhancement of the model. At the same time, we will focus on how to achieve efficient edge resource management based on WS-management protocol in the cloud-edge hybrid architecture environment.
Adversariality and Ideal Argumentation: A Second-Best Perspective

What is the relevance of ideals for determining virtuous argumentative practices? According to Bailin and Battersby the telos of argumentation is to improve our cognitive systems, and adversariality plays no role in ideally virtuous argumentation. Stevens and Cohen grant that ideal argumentation is collaborative, but stress that imperfect agents like us should not aim at approximating the ideal of argumentation. Accordingly, it can be virtuous, for imperfect arguers like us, to act as adversaries. Many questions are left unanswered by both camps. First, how do we conceptualize an ideal and its approximation? Second, how can we determine what is the ideal of argumentation? Third, can we extend Stevens and Cohen’s anti-approximation argument beyond virtue theory? In order to respond to these questions, this paper develops a second-best perspective on ideal argumentation. The Theory of the Second Best is a formal contribution to the field of utility (or welfare) optimization. Its main conclusion is that, in non-ideal circumstances, approximating ideals might be suboptimal.
A Framework Based on UCD and Scrum for the Software Development Process

This paper proposes a framework that successfully includes UCD techniques and roles into Scrum. A systematic literature review was previously developed with the purpose of defining the most relevant methodologies and techniques to overcome the challenges of integrating Scrum and UCD. Afterwards, the information gathered was complemented by interviews with HCI experts, designers and developers. The most relevant methodologies and techniques reported were further analyzed through a comparative analysis, after which one methodology and nine techniques were selected to be included in the Scrum-UCD framework. A first version of the proposal was developed, detailing phases, activities and roles that would work as a guide towards the development of small and large software development projects. This proposal was tested in a small software project that involved the redesign and improvement of a banking system, and results regarding the team experience, final product usability and resource efficiency were evaluated against previous projects’ results of the same institution that followed Scrum without the integration of UCD. This comparison demonstrated that following the proposed framework improved the overall software development process, and the first version of the proposal was updated to solve the problems identified during the different stages of the project.
Graph Filter Design for Distributed Network Processing: A Comparison between Adaptive Algorithms

Graph filters (GFs) have attracted great interest since they can be directly implemented in a diffused way. Thus it is interesting to investigate GFs to implement signal processing operations in a distributed manner. However, in most GF models, the input signals are assumed to be time-invariant, static, or change at a very low rate. In addition to that, the GF coefficients are usually set to be node-invariant, i.e. the same for all the nodes. Yet, in general, the input signals may evolve with time and the underlying GF may have parameters dependent on the nodes. Therefore, in this paper, we consider dynamic input signals and both types of GF coefficients, node-variant, i.e. vary on different nodes, and node-invariant. Then, we apply LMS and RLS algorithms for GF design, along with two others called adapt-then-combine (ATC) and combined RLS (CRLS) to estimate the GF coefficients. We study and compare the performance of the algorithms and show that in the case of node-invariant GF coefficients, CRLS gives the best performance with lowest mean-square-displacement (MSD), whereas, for node-variant case, RLS represents the best results. The effect of bias in the input signal has also been examined.
VAMR Basketball on Head-Mounted and Hand-Held Devices with Hand-Gesture-Based Interactions

Virtual, Augmented, and Mixed Reality (VAMR)-based sports simulators may help facilitate learning and training for students confined within closed/cramped spaces (e.g., during a pandemic), and allow disabled or disadvantaged players to participate in the said sports alongside non-disabled peers. This work explores the development of a VAMR basketball simulator that uses hand gestures to accurately mimic the real-world performance of basketball moves such as throwing and shooting. Furthermore, the simulator is developed simultaneously for both head-mounted (VR/AR headsets) and hand-held (smartphone/tablet) form factors, facilitating deployment of the same simulator over multiple hardware configurations. This eliminates the need to force students to use one platform for online learning, and would also allow for future real-time networked multi-user support across different form-factor devices. This model also paves the way for future user studies comparing the efficacy of head-mounted and hand-held modes for simulating basketball and other sports in VAMR.
Advanced Deep Learning Techniques

Artificial intelligence (AI) has been a buzz word for quite a long time now; the advancements in this field have reached its way to our pockets in form of smartphones. Google Lens, Siri, Alexa, and many other AI assistances had become part of our lives now. The key features like identifying an image or text is a necessity for such programs; in fact many investments are being poured in building better models for image recognition and contextual speed recognition through neural networks. In this chapter, the architectures of various neural networks are explored. Fundamentally, convolution neural networks aka ConvNets or CNN and recurrent neural networks or RNN are explained with a few examples and their implementation in Python. Intuitive explanation with easily understandable mathematical interpretation can be seen in this chapter.
Privacy-Preserving and Secure Cloud Computing: A Case of Large-Scale Nonlinear Programming

The volume of data is increasing rapidly, which poses a great challenge for resource-constrained users to process and analyze. A promising approach for solving computation-intensive tasks over big data is to outsource them to the cloud to take advantage of the cloud’s powerful computing capability. However, it also brings privacy and security issues since the data uploaded to the cloud may contain sensitive and private information which should be protected. In this article, we address this problem and focus on the privacy-preserving and secure outsourcing of large-scale nonlinear programming problems (NLPs) subject to both linear constraints and nonlinear constraints. Large-scale NLPs play an important role in the field of data analytics but have not received enough attention in the context of cloud computing. In our outsourcing protocol, we first apply a secure and efficient transformation scheme at the client side to encrypt the private information of the considered NLP. Then, we use the reduced gradient method and generalized gradient method at the server side to solve the transformed large-scale NLPs under linear constraints and nonlinear constraints, respectively. We provide security analysis of the proposed protocol, and evaluate its performance via a series of experiments. The experimental results show that our protocol can efficiently solve large-scale NLPs and save much time for the client, providing a great potential for real applications.
Discussion on the Software V&amp;V Technology in Nuclear Power Plants

Software V&V technology provides reliability and safety guarantee for digital system and intelligent equipment which used in nuclear power plant. Although a systematic scheme of software V&V technologies has been formed for nuclear power plant, there are some technical nodes need to be discussed and considered. The software V&V standard has been updated. The higher requirements are put forward for the depth, scope, the appropriate techniques and methods of the software V&V. This paper will discuss the upgraded version of software V&V related standards, and give the key points that software V&V technology needs to pay attention. Then the pre-development software identification, new software V&V technical of the special instrument and control system, and the applicability assessment of the safety analysis software are also been discussed. The technical difficulties faced by software V&V in the above aspects will be put forward. At last the future development direction of software V&V technology is proposed.