Reviewerly/exHarmony huggingface dataset:
Empirical Comparison of the Feature Evaluation Methods Based on Statistical Measures

One of the most important classification problems is selecting proper features, i.e. features that describe the classified object in the most straightforward way possible. Then, one of the biggest challenges of the feature selection is the evaluation of the feature’s quality. There is a plethora of feature evaluation methods in the literature. This paper presents the results of a comparison between nine selected feature evaluation methods, both existing in literature and newly defined. To make a comparison, features from ten various sets were evaluated by every method. Then, from every feature set, best subset (according to each method) was chosen. Those subsets then were used to train a set of classifiers (including decision trees and forests, linear discriminant analysis, naive Bayes, support vector machines, k nearest neighbors and an artificial neural network). The maximum accuracy of those classifiers, as well as the standard deviation between their accuracies, were used as a quality measures of each particular method. Furthermore, it was determined, which method is the most universal in terms of the data set, i.e. for which method, obtained accuracies were dependent on the feature set the least. Finally, computation time of each method was compared. Results indicated that for applications with limited computational power, method based on the average overlap between feature’s values seem best suited. It led to high accuracies and proved to be fast to compute. However, if the data set is known to be normally distributed, method based on two-sample <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> -test may be preferable.
Open-world probabilistic databases: Semantics, algorithms, complexity

Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic databases. Many systems based on probabilistic databases, however, still have certain semantic deficiencies, which limit their potential applications. We revisit the semantics of probabilistic databases, and argue that the closed-world assumption of probabilistic databases, i.e., the assumption that facts not appearing in the database have the probability zero, conflicts with the everyday use of large-scale probabilistic knowledge bases. To address this discrepancy, we propose open-world probabilistic databases, as a new probabilistic data model. In this new data model, the probabilities of unknown facts, also called open facts, can be assigned any probability value from a default probability interval. Our analysis entails that our model aligns better with many real-world tasks such as query answering, relational learning, knowledge base completion, and rule mining. We make various technical contributions. We show that the data complexity dichotomy, between polynomial time and , for evaluating unions of conjunctive queries on probabilistic databases can be lifted to our open-world model. This result is supported by an algorithm that computes the probabilities of the so-called safe queries efficiently. Based on this algorithm, we prove that evaluating safe queries is in linear time for probabilistic databases, under reasonable assumptions. This remains true in open-world probabilistic databases for a more restricted class of safe queries. We extend our data complexity analysis beyond unions of conjunctive queries, and obtain a host of complexity results for both classical and open-world probabilistic databases. We conclude our analysis with an in-depth investigation of the combined complexity in the respective models.
A statistical active contour model for interactive clutter image segmentation using graph cut optimization

This paper presents a statistical region based Active Contour Model (ACM) considering the correlation between local and global image statistics to segment cluttered images. Generally, cluttered images do not have constant intensity distribution; rather, the intensity may follow near constant variation in different regions. To quantify this variation, we have considered the Coefficient of Variation (CoV) of the regions interior and exterior to the contour as global statistics and the CoV in the local patches as local statistics. Subsequently, the region energy term of the proposed ACM is designed such that it minimizes the difference between the local and global statistics i.e. it encourages CoV for all the local patches inside and outside of the final contour to be nearly homogeneous. Further, we have verified that the energy formulation can be efficiently discretized and solved using graph cut optimization. The main advantages of graph-based formulation over level set formulation are the existence of a global optimal solution and lesser sensitivity to contour initialization. Additionally, the former formulation is significantly faster being non-iterative or convergable with very few iterations. Experimental results demonstrate the superior performance of our approach against other state-of-the-art active contour approaches and also over its level set counterpart.
On using human activity recognition sensors to improve the performance of collaborative mobile manipulators: Review and outlook

The operation of mobile manipulators in a collaborative environment needs to be adapted to the characteristics and skills of human operators. Human activity recognition, utilizing wearable sensors and vision systems, could be used to fine tune the performance of the mobile manipulator so that human operators be better assisted. The goal is to develop a sense of safety and trust between the human and the manipulator in order to improve the ergonomics of the operator within the collaborative workspace. This paper reviews the technologies that can be used for activity tracking together with gait recognition as a biometric tool. These technologies could potentially allow the mobile robotic manipulator to dynamically adapt to the motion, skills, and intentions of the human operator and to the requirements of the task in action. This paper also proposes an idea of combining a gait recognition model and activity tracking towards improving the performance of mobile collaborative robots.
Aspect-based sentiment analysis in Chinese based on mobile reviews for BiLSTM-CRF

Aiming at the problem that the Aspect-based sentiment analysis in Chinese has low recognition rate due to many steps, this paper proposes an improved BiLSTM-CRF model based on combine the Chinese character vector and Chinese words position feature, which can extract attribute words and sentiment words jointly simultaneously, while extracting Polarity judges of sentiment words. Experiments show that the improved model improves the precision rate by 9.2% 13.32%, recall rate improves 0.48% 21.29%, F-measure improves 7.33% 15.74% compared with Conditional Random Fields (CRF) model and Long Short Term Memory (LSTM) model on the self-built 6357 mobile reviews dataset. The experimental results show that the model improves the accuracy of Aspect-based sentiment analysis and can effectively obtain the information required by users need in evaluation texts.
Tuned in on senders’ self-revelation: Emojis and emotional intelligence influence interpretation of WhatsApp messages

Emojis function like nonverbal cues in digital communication, but how do they impact the meaning of a message? We tested experimentally (N ​= ​50) if the presence of emojis in WhatsApp messages influences their interpretation as either factual information, self-revelation of the sender, relationship information, or an appeal ("four-ears model"). Messages were most frequently interpretated as self-revelation, especially if they contained an emoji, whereas sole text messages were equally as frequently perceived as either self-revelation or factual information. The interpretation of messages with or without emojis interacted with whether their content was positive or negative. Additionally, recipient's emotional intelligence both as an ability (cognitive measure) and trait (personality measure) was associated with interpreting self-revelation, but only in emoji messages. Emojis may thus provide the cues necessary to extract emotional information from text-based messages, to which emotionally intelligent recipients seem to be especially responsive.
A Secure and Flexible FPGA-Based Blockchain System for the IIoT

Blockchain is a promising solution for Industry 4.0; however, it does not guarantee input data integrity. We propose a field-programmable gate array (FPGA)-based private blockchain system for the industrial Internet of Things, where the transaction generation is performed inside the FPGA in an isolated and enclaved manner.
Cross-chain Oracle Based Data Migration Mechanism in Heterogeneous Blockchains

As things currently stand, the blockchain industry is siloed among many different platforms and protocols resulting in various islands of blockchains. Restrictions regarding assets transfers and data migration between different blockchains reduce the usability and comfort of users, and hinder novel developments within the blockchain ecosystem. Interoperability will be the main topics of next-generation blockchain technologies. In this paper, we focus on how to enable interoperability between two heterogeneous blockchains in the context of data migration. We first build an cross-chain data migration architecture based on data migration oracle. Second, we design a data migration mechanism based on former architecture. By employing the proposed data migration architecture, it is equivalent to opening a secure channel between two heterogeneous blockchains allowing secure data migration. By applying data migration mechanism, the confidentiality, integrity and security of migrated data can be well guaranteed.
A holistic approach to a context-aware IoT ecosystem with Adaptive Ubiquitous Middleware

The Internet of Things is envisioned to provide connectivity and communication between various devices all over the world. Based on the devices and applications involved, the nature of the network formed differs. Thus, an intelligent and holistic ecosystem needs to be created wherein clients, data sources, smart objects, and services can all co-exist and interact with each other. We present Adaptive Ubiquitous Middleware for context-aware IoT ecosystems, which considers the situational context of the applications, devices, or people and the contexts of the network formed and accordingly adapts the behavior of the ecosystem. Adaptive Ubiquitous Middleware is a multi-agent, multi-communication protocol-facilitated middleware that acts as an integration point for applications to access relevant context, share it with other applications, and have relevant services made available via a multi-communication protocol bridge. We also present an optimal service allocation model for a single service class that utilizes available computing resources and achieves a minimum average response time. The system implementation has been evaluated with two use cases to demonstrate its applicability, effectiveness, and generality. The evaluation of optimal service allocation demonstrates the service response is much faster in the proposed model.
A Certificateless Consortium Blockchain for IoTs

Blockchain is multi-centralized, immutable and traceable, thus is very suitable for distributed storage, privacy and security management in IoTs. However, most existing researches focus on the integration of public blockchain and IoTs. In fact, problems such as slow consensus, low transmission throughput, and completely open storage on the public blockchain are intolerable in IoT scenarios. Although consortium blockchain represented by Hyperledger Fabric has improved the transmission rate, its data security completely relies on the PKI-based certificate mechanism, resulting in transmission inefficiency and privacy leakage. In this paper, a key-derived Controllable Lightweight Secure Certificateless Signature (CLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) algorithm is proposed to significantly improve the transmission efficiency and keep similar computation overhead of consortium blockchain. Compared with the existing certificateless signatures, CLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> achieves more secure transactions, whose controllable anonymity and key-derived mechanism not only prevents public key replacement attacks and forged signature attacks, but also supports hierarchical privacy protection. Armed with CLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , we design a consortium blockchain security architecture based on Hyper-ledger Fabric and edge computing. To the best of our knowledge, this is the first implementation of certificateless signature in consortium blockchain. We formally prove the security of our schemes in the random oracle model. Specifically, the security of the proposed scheme is reduced to the Elliptic curve discrete logarithm problem (ECDLP). Security analysis and experiments in IoT scenarios verify the feasibility and effectiveness of CLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Graph-based Hierarchical Knowledge Representation for Robot Task Transfer from Virtual to Physical World

We study the hierarchical knowledge transfer problem using a cloth-folding task, wherein the agent is first given a set of human demonstrations in the virtual world using an Oculus Headset, and later transferred and validated on a physical Baxter robot. We argue that such an intricate robot task transfer across different embodiments is only realizable if an abstract and hierarchical knowledge representation is formed to facilitate the process, in contrast to prior literature of sim2real in a reinforcement learning setting. Specifically, the knowledge in both the virtual and physical worlds are measured by information entropy built on top of a graph-based representation, so that the problem of task transfer becomes the minimization of the relative entropy between the two worlds. An And-Or-Graph (AOG) is introduced to represent the knowledge, induced from the human demonstrations performed across six virtual scenarios inside the Virtual Reality (VR). During the transfer, the success of a physical Baxter robot platform across all six tasks demonstrates the efficacy of the graph-based hierarchical knowledge representation.
DiffCo: Autodifferentiable Proxy Collision Detection With Multiclass Labels for Safety-Aware Trajectory Optimization

The objective of trajectory optimization algorithms is to achieve an optimal collision-free path between start and goal states. In real-world scenarios, where environments can be complex and nonhomogeneous, a robot needs to be able to gauge whether a state will be in collision with various objects in order to meet some safety metrics. The collision detector should be computationally efficient and, ideally, analytically differentiable to facilitate stable and rapid gradient descent during optimization. However, methods today lack an elegant approach to detect collision differentiably, relying rather on numerical gradients that can be unstable. We present DiffCo, the first, fully autodifferentiable, nonparametric model for collision detection. Its nonparametric behavior allows one to compute collision boundaries on the fly and update them, requiring no pretraining and allowing it to update continuously in dynamic environments. It provides robust gradients for trajectory optimization via backpropagation and is often 10–100 times faster to compute than its geometric counterparts. DiffCo also extends trivially to modeling different object collision classes for semantically informed trajectory optimization.
A New Multi-scale Pedestrian Detection Algorithm in Traffic Environment

Pedestrian detection in traffic environment requires high accuracy and speed of the algorithm. Traditional methods can meet the speed requirement, but there is a long gap in accuracy. Traditional methods based on convolution neural network have higher accuracy, but the amount of calculation is enormous. Aiming at the problems of background confusion, there are pedestrian ambiguity and pedestrian multi-scale in pedestrian detection, this paper constructs a pedestrian detection method with higher accuracy and faster speed based on Faster Regions Convolutional Neural Network (Faster RCNN) method, which is the leading method in the field of target detection. We have mainly improved three aspects: (1) The design criteria based on the summary and the scale characteristics of pedestrians; (2) The setting of anchor windows and the way of creating regional networks have been adjusted, and the pooling layer of environmental regions has been added; (3) Fusion of different levels of features acquires more comprehensive features. Then, based on the open-source deep learning framework, the network is implemented on the California Institute of Technology pedestrian data set. The experimental results show that our method improves the detection accuracy by 2.9% and the detection speed by 10.1 frames/second compared with the original Faster RCNN on the same data set.
Role‐based Access Control Video Surveillance Mechanism Modeling in Smart Contract Environment

Abstract Closed circuit television (CCTV) is installed on roads or in facilities for the purpose of filming videos to be used for crime prevention or as evidence for accidents in the future, and the number of CCTVs installed annually is increasing sharply. The videos collected by the devices can be submitted as legal evidence for accidents, which could happen in the future, but the damage to their integrity may cause the loss of reliability. Blockchain, which guarantees the integrity of data by sharing data as a distributed network method on these issues, can be applied as an effective means of data forgery. In general, however, video information contains sensitive data within the image, and sharing it externally can cause many problems. Thus, in this paper, by implementing role‐based approach control based on a block chain that guarantees integrity through sharing of data, a mechanism is proposed to share filmed images but prevent indiscriminate access by third parties.
Security and Privacy Implementation in Smart Home: Attributes Based Access Control and Smart Contracts

There has been wide range of applications involving smart home systems for user comfort and accessibility to essential commodities. Users enjoy featured home services supported by the IoT smart devices. These IoT devices are resource-constrained, incapable of securing themselves and can be easily hacked. Edge computing can provide localized computations and storage which can augment such capacity limitations for IoT devices. Furthermore, blockchain has emerged as technology with capabilities to provide secure access and authentication for IoT devices in decentralized manner. In this paper, we propose an authentication scheme which integrate attribute based access control using smart contracts with ERC-20 Token (Ethereum Request For Comments) and edge computing to construct a secure framework for IoT devices in Smart home system. The edge server provide scalability to the system by offloading heavier computation tasks to edge servers. We present system architecture and design and discuss various aspects related to testing and implementation of the smart contracts. We show that our proposed scheme is secure by thoroughly analysing its security goals with respect to confidentiality, integrity and availability. Finally, we conduct a performance evaluation to demonstrate the feasibility and efficiency of the proposed scheme.
UAV Image Stitching Based on Mesh-Guided Deformation and Ground Constraint

This article introduces a drone image stitching based on mesh-guided deformation and ground constraint, which can closely match the characteristics of images and achieve precise registration and acquire ideal stitching effect.The traditional methods use the homography model to align the image, which causes artifacts in the result of stitching the images with parallax.To overcome this situation, the image is divided into meshes and the mesh vertices of the target image are used to guide the warping.A new energy function is designed to represent the deformation characteristics of the image.We propose a new alignment term by using local homography and a local scale term by using the edge information of the mesh.The established mesh-guided deformation model can overcome image parallax caused by some external factors and eliminate the ghostly parts of the result.Moreover, imaged scene is not effectively planar and some fluctuations exist in the scene of the images, which will distort the stitching result.We propose a ground constraint with the ground plane as the main plane to reduce projection distortions in non-overlapping areas between images.Finally, the method of creating groundtruth is proposed, which can evaluate the naturalness of results and make comparison more reasonable.Several sets of challenging drone images are tested, and the experimental results show that our stitching system has good results.
Sparse elastic net multi-label rank support vector machine with pinball loss and its applications

Multi-label rank support vector machine (RankSVM) is an effective technique to deal with multi-label classification problems, which has been widely used in various fields. However, it is sensitive to noise points and cannot delete redundant features for high dimensional problems. Therefore, to address the above two limitations, a sparse elastic net multi-label RankSVM with pinball loss (pin-ENR) is first proposed in this paper. On the one hand, pinball loss is employed to enhance the robustness. On the other hand, it adopts the sparse elastic net regularization, so that it can do variable selection. However, it still has challenges for large-scale problems with a huge number of features, samples, and labels. Therefore, motivated by the sparsity of pin-ENR, a safe simultaneous feature and label-pair elimination rule is further constructed for accelerating pin-ENR, which is termed as FLER-pin-ENR. Its main idea is to delete a large number of inactive features and label-pairs simultaneously before training without sacrificing accuracy. Numerical experiments on four synthetic and seven benchmark datasets demonstrate the feasibility and validity. Moreover, we apply our FLER-pin-ENR to the diagnosis of diabetes complications and the natural scene image classification problems, which further verifies the practicability of our proposed method.
Implementation of TOPSIS Algorithm for Selection of Prominent Student Class

Abstract This study aims to provide the right solution in overcoming the problem of selection for prominent class students senior high school at SMAN 2 Pematangsiantar. The solution is to make a decision support system for the selection of prominent class XI IPA students in determining rankings. The method used is the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) algorithm by using several criteria, namely: Mid Test Score, Final Exam Score, Extracurricular, Personality, and Attitude. The results of student data processing using the decision support system obtained weights of 1, 0.57188272655161, and 0.56259472567445 for rank 1 to rank 3. The implementation of this Decision Support System is beneficial and makes it easier for the school and the homeroom teacher to determine ranking for the selection of prominent class students.