Reviewerly/exHarmony huggingface dataset:
Interactive evolution and exploration within latent level-design space of generative adversarial networks

Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.
Improved Cuckoo Search algorithmic variants for constrained nonlinear optimization

Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.).
Tangible Interfaces with Printed Paper Markers

This pictorial presents a design investigation at the intersection of paper and computer vision for tangible interfaces. Through this exploration, we uncovered various characteristics of paper that connect tangible interactions with concealing and revealing printed fiducial markers for detection-particularly through the affordances of paper craft and fiber. We illustrate a variety of paper structures that construct and deconstruct fiducial markers. We also demonstrate how these structures enable untethered functional physical inputs, such as push buttons and sliders. We showcase four proposals that extend these material insights into tangible interface applications, including interactive data physicalizations and functional paper prototypes. Furthermore, we continue the legacy of pictorials by exposing fabrication drawings for others to engage with this work at a more practical level.
FabriClick

Mechanical pushbuttons, which provide physical landmarks and clear tactile feedback, are easily accessible and highly reliable in eyes-free use. Potentially, their merits can improve the experiences of on-body or wearable HCI. However, they are not commonly adopted as a user interface of smart textiles because the physical mechanism of conventional pushbutton hardware requires further integration, which should be seamless enough to be comfortably worn. In this pictorial, we present a design exploration of the methodologies for interweaving mechanical pushbuttons into fabrics. The exploration used a frame system, which unifies the workflow of digital embroidery and 3D printing and enables the exploration of the physical design. Through the process, we investigated methods of integration and fabrication through making and presented our findings with proof-of-concept implementations. We also discussed the alternative designs and interaction methods as well as their implications to enlighten future research directions and opportunities.
Green-PoW: An energy-efficient blockchain Proof-of-Work consensus algorithm

This paper opts to mitigate the energy-inefficiency of the Blockchain Proof-of-Work (PoW) consensus algorithm by rationally repurposing the power spent during the mining process. The original PoW mining scheme is designed to consider one block at a time and assign a reward to the first place winner of a computation race. To reduce the mining-related energy consumption, we propose to compensate the computation effort of the runner(s)-up of a mining round, by granting them exclusivity of solving the upcoming block in the next round. This will considerably reduce the number of competing nodes in the next round and consequently, the consumed energy. Our proposed scheme divides time into epochs, where each comprises two mining rounds; in the first one, all network nodes can participate in the mining process, whereas in the second round only runners-up can take part. Thus, the overall mining energy consumption can be reduced to nearly 50%. To the best of our knowledge, our proposed scheme is the first to considerably decrease the energy consumption of the original PoW algorithm. Our analysis demonstrates the effectiveness of our scheme in reducing energy consumption, the probability of fork occurrences, the level of mining centralization presented in the original PoW algorithm, and the effect of transaction censorship attack.
Solid-State Qubits: 3D Integration and Packaging

Quantum processing has the potential to transform the computing landscape by enabling efficient solutions to problems that are intractable using classical processors. The field was sparked by a suggestion from physicist Richard Feynman in 1981 that a controllable quantum system can be used to simulate other quantum systems, such as the energy band structure of complex materials or the chemical reaction rates of intricate molecules. In the 1990s, interest in quantum computing grew rapidly with the introduction of the first quantum "killer app"-the potential of a large-scale quantum processor to break certain types of public encryption schemes [1]. Recently, there has been growing consensus that myriad other fields besides data security could be impacted by the development of a quantum processor, including machine learning [2], many optimization problems [3], and Feynman's original idea of the simulation of materials properties [4]. In recent years, the field has progressed rapidly, but many technical challenges must be overcome before a large-scale quantum processor can be built. This article focuses on the development of packaging for solid-state qubits and the use of 3D integration to address this challenge.
Deep learning recognition of diseased and normal cell representation

Abstract Cell classification refers to detecting normal and diseased cells from small amount of data. Sometimes, classification of cells becomes difficult because some cells fall into more than one categories/classes. Current state‐of‐the‐art cell classification methods have been developed on the bases of tumor cell classification but these methods cannot classify diseased or normal cells. This study investigated the performance of two classification methods traditional machine learning and deep learning (normal and diseased cell classification) to categorize normal and diseased cells. Millions of normal cells undergo controlled growth and uncontrolled growth may be involved in disease causation but their clinical applications remain limited due to difficulties in distinguishing normal and diseased cells. Previous studies are limited to identify, systematically, the normal and diseased cells. This study collected information about diseased or normal cells to check the networks for correct cell detection and then eliminated false‐positive cells. We used machine learning methods along with logistic regression, support vector machine, and CNN (convolutional neural network). We found that our proposed method classified better the normal and diseased cells. With the help of two types of images: normal and diseased cells, we trained a CNN that identified diseased cells with 98% accuracy and enabled the discovery of normal and diseased cells. As a result, it will advance the clinical utility of human diseased cells.
Content moderation: Social media’s sexist assemblages

This article proposes ‘sexist assemblages’ as a way of understanding how the human and mechanical elements that make up social media content moderation assemble to perpetuate normative gender roles, particularly white femininities, and to police content related to women and their bodies. It investigates sexist assemblages through three of many potential elements: (1) the normatively gendered content presented to users through in-platform keyword and hashtag searches; (2) social media platforms’ community guidelines, which lay out platforms’ codes of conduct and reveal biases and subjectivities and (3) the over-simplification of gender identities that is necessary to algorithmically recommend content to users as they move through platforms. By the time the reader finds this article, the elements of the assemblages we identify might have shifted, but we hope the framework remains useful for those aiming to understand the relationship between content moderation and long-standing forms of inequality.
DecChain: A decentralized security approach in Edge Computing based on Blockchain

Security in edge computing paradigms has become a major concern in recent times due to the integral role it plays in the framework of edge computing. Privacy-preserving and data security challenges are among the many concerns impeding the goal of making data storage available and processing at the edge of the network quite difficult to implement. Authenticated users must be the only ones with access to their respective stored data which are protected against any form of intruder manipulation. Most authentication schemes proposed and implemented in edge computing and other paradigms use a trusted entity to initialize the authentication process between edge servers and prospective users. Servers and users are expected to register with the trusted party first before they are able to subsequently authenticate one another. The presence of the trusted party presents scalability issues as well as the threat of having a single point of failure which may threaten the availability of the entire network. In this paper, we present a fully decentralized approach to solving this problem by eliminating the public trusted entity within the network framework termed DecChain. In DecChain we employ some notable principles of permissioned blockchain technology in the rollout and authentication of elements within the network. Authenticated users within our proposed framework would not have to sign in to every service provider to be authenticated to access a service or resource. Security experiments and the deployment of our scheme are carried out to evaluate the performance of DecChain. The results show our scheme is secured and achieves the intended purpose efficiently.
Augmented reality and competition in robotics education: Effects on 21st century competencies, group collaboration and learning motivation

Abstract Robotics education has received an increasing attention in recent years as a means to build students' motivation, team collaboration skills, and other valuable 21st century competencies. Yet there is a lack of experimental studies to investigate and identify strategies to facilitate robotics education. This study adopted a 2 × 2 quasi‐experimental design to investigate two strategies: the incorporation of augmented reality (AR) and the introduction of competition in robotics activities. Students' robotics task performance, team collaboration processes, 21st century learning competencies and learning motivation were measured as dependent variables. The results indicated that AR significantly improved students' motivation, team processes, and 21st century competencies. Moreover, the effects of AR were more pronounced with the competition groups. Implications are drawn to provide guidelines on the use of AR and competition in robotics education.
Understanding the Design Space and Authoring Paradigms for Animated Data Graphics

Abstract Creating expressive animated data graphics often requires designers to possess highly specialized programming skills. Alternatively, the use of direct manipulation tools is popular among animation designers, but these tools have limited support for generating graphics driven by data. Our goal is to inform the design of next‐generation animated data graphic authoring tools. To understand the composition of animated data graphics, we survey real‐world examples and contribute a description of the design space. We characterize animated transitions based on object, graphic, data, and timing dimensions. We synthesize the primitives from the object, graphic, and data dimensions as a set of 10 transition types, and describe how timing primitives compose broader pacing techniques. We then conduct an ideation study that uncovers how people approach animation creation with three authoring paradigms: keyframe animation, procedural animation, and presets &amp; templates. Our analysis shows that designers have an overall preference for keyframe animation. However, we find evidence that an authoring tool should combine these three paradigms as designers’ preferences depend on the characteristics of the animated transition design and the authoring task. Based on these findings, we contribute guidelines and design considerations for developing future animated data graphic authoring tools.
Ship target detection and identification based on SSD_MobilenetV2

There are many deep learning algorithms currently used in ship supervision, but they generally have the problems of insufficient target detection speed and accurate identification rate. In this paper, an improved SSD algorithm based on MobilenetV2 convolutional neural network is proposed for ship image target detection and identification. MobilenetV2 network is used for feature extraction of ship images. The MobilenetV2 network is pre-trained on the Coco dataset, and then fine-tuned on the constructed ship image dataset to save training time and computing resources. In order to verify the accuracy of the SSD _MobilenetV2 algorithm in ship image detection and id0entification, Faster R-CNN_InceptionV2 algorithm was used as a comparison group. Experimental results show that the SSD_MobilenetV2 algorithm has better detection and identification effect on ship images. And the SSD_MobilenetV2 algorithm has better performance in terms of detection speed and NMS(Non-Maximum Suppression), which meets the requirements for rapid detection and identification of ship targets.
Trajectory Outlier Detection

Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades. This work studies the existing trajectory outlier detection algorithms in different industrial domains and applications, including maritime, smart urban transportation, video surveillance, and climate change domains. First, we review several algorithms for trajectory outlier detection. Second, different taxonomies are proposed regarding application-, output-, and algorithm-based levels. Third, evaluation of 10 trajectory outlier detection algorithms is performed on small, large, and big trajectory databases. Finally, future challenges and open issues with regard to trajectory outliers are derived and discussed. This survey offers a general overview of existing trajectory outlier detection algorithms in industrial informatics applications. As a result, mature solutions may be further developed by data mining and machine learning communities.
RefineU-Net: Improved U-Net with progressive global feedbacks and residual attention guided local refinement for medical image segmentation

Motivated by the recent advances in medical image segmentation using a fully convolutional network (FCN) called U-Net and its modified variants, we propose a novel improved FCN architecture called RefineU-Net. The proposed RefineU-Net consists of three modules: encoding module (EM), global refinement module (GRM) and local refinement module (LRM). EM is backboned by pretrained VGG-16 using ImageNet. GRM is proposed to generate intermediate layers in the skip connections in U-Net. It progressively upsamples the top side output of EM and fuses the resulted upsampled features with the side outputs of EM at each resolution level. Such fused features combine the global context information in shallow layers and the semantic information in deep layers for global refinement. Subsequently, to facilitate local refinement, LRM is proposed using residual attention gate (RAG) to generate discriminative attentive features to be concatenated with the decoded features in the expansive path of U-Net. Three modules are trained jointly in an end-to-end manner thereby both global and local refinement are performed complementarily. Extensive experiments conducted on four public datasets of polyp and skin lesion segmentation show the superiority of the proposed RefineU-Net to multiple state-of-the-art related methods.
Particle filtering for a class of cyber-physical systems under Round-Robin protocol subject to randomly occurring deception attacks

In this paper, the particle filtering problem is studied for a class of general nonlinear cyber-physical systems with non-Gaussian noises under Round-Robin protocol (RRP) subject to the randomly occurring deception attacks. In order to prevent the data from collisions and alleviate the communication overhead for the shared network with limited resources, the RRP is introduced in the sensor-to-filter channel to schedule the multiple sensors with a predefined transmission order. Under the RRP, only one sensor can be granted the access to the shared channel for measurement transmission at each time instant. A Bernoulli-distributed stochastic variable is utilized to describe the characteristic of random occurrence of deception attacks initiated by the adversaries. A RRP-based particle filtering algorithm is developed by establishing a modified likelihood function, where the statistical property of the randomly occurring deception attacks is exploited and the RRP-induced effect on the filter design is reflected. Finally, an illustrative example regarding the target tracking problem is provided to verify the feasibility and effectiveness of the developed particle filtering scheme.
Blockchain Paradigm for Healthcare: Performance Evaluation

Electronic health records (EHRs) have become a popular method to store and manage patients’ data in hospitals. Sharing these records makes the current healthcare data management system more accurate and cost-efficient. Currently, EHRs are stored using the client/server architecture by which each hospital retains the stewardship of the patients’ data. The records of a patient are scattered among different hospitals using heterogeneous database servers. These limitations constitute a burden towards a personalized healthcare, when it comes to offering a cohesive view and a shared, secure and private access to patients’ health history for multiple allied professionals and the patients. The data availability, privacy and security characteristics of the blockchain have a propitious future in the healthcare presenting solutions to the complexity, confidentiality, integrity, interoperability and privacy issues of the current client/server architecture-based EHR management system. This paper analyzes and compares the performance of the blockchain and the client/server paradigms. The results reveal that notable performance can be achieved using blockchain in a patient-centric approach. In addition, the immutable and valid patients’ data in the blockchain can aid allied health professionals in better prognosis and diagnosis support through machine learning and artificial intelligence.
Efficiency Analysis of Machine Learning Intelligent Investment Based on K-Means Algorithm

With the rapid development of technologies such as big data, intelligent data analysis and cloud computing, the application of Internet financial technology has become more and more extensive, and with the advent of the era of large asset management in the domestic wealth management industry, in order to improve the efficiency of financial services, traditional finance is needed.The products and services provided by the industry have been innovated, resulting in smart investment.Compared with traditional investment, smart investment as a new business model has the advantages of low threshold, low cost and high efficiency.However, as far as its nature is concerned, smart investment must first play the role of an investment adviser.Therefore, for enterprises or individuals who invest, the investment efficiency of smart investment is the most important.At present, the research on the efficiency analysis of smart investment, due to the improper selection of algorithm models or the lack of deep data mining, leads to the analysis of the investment efficiency of smart investment products is inconsistent with or even deviated from the actual situation.In view of these problems, this paper selects China Merchants Bank's Capricorn Intelligence as the research object, and analyzes the investment efficiency of smart investment based on K-means cluster analysis and data mining technology.The results show that Capricorn has a certain randomness in the selection process of the fund, and chooses to reduce the rate of return in order to control the risk.The investment portfolio formulated for the customer has obvious timing.The results show that the machine learning based on K-means algorithm makes a concrete analysis of the investment efficiency of Capricorn Smart Investment, this method can also be used for the efficiency analysis of other smart investment products.
SLOC: Service Level Objectives for Next Generation Cloud Computing

Since the emergence of cloud computing service level objectives (SLOs) and service level agreements (SLAs) have put themselves forward as one of the key enablers for cloud's on-demand, pay-as-you-go service consumption model. To date, the vast majority of cloud platforms provide support for SLAs only in terms of statically predefined SLOs, e.g., service availability, and low-level resource capacity guarantees, e.g., CPU usage. Unfortunately, there is only limited support to clearly map workload performance requirements to the resource capacity guarantees. In this article, we introduce SLOC— a novel elasticity framework, which promotes a novel performance-driven, SLO-native approach to cloud computing. We outline the main research challenges, vision, and approach of our SLOC framework toward the SLO-native paradigm in next generation cloud computing.