Reviewerly/exHarmony huggingface dataset:
Socially Assistive Robot’s Behaviors using Microservices

In this work, we introduce a set of robot's behavior aimed at being used for monitoring and interaction with elderly people affected by Alzheimer disease. Robot's behaviors for a low cost robotic device rely on the use of microservices running on a local server. A microservice is an independent, self-contained, self-scope, and self-responsibility component of the robotic system proposed for decoupling the implemented functions needed to obtain the proper robot behaviors. The developed robotic behaviors include navigation, interaction, and monitoring capabilities. The requests and the signals of the patients are handled and managed relying on event-based communications between the system components. The use of design patterns like this one increases the overall reliability of a service composition. The system is currently operating in a private house with an elderly couple.
PATHFINDER: Graph-Based Itemset Embedding for Learning Course Recommendation and Beyond

We demonstrate a tool, named as PATHFINDER, that captures and visualizes rich latent relationships among courses as a graph, mines students' past course performance data, and recommends pathways or top-k courses most helpful to a given student, using an itemset embedding based learning model. With dedicated design for the asymmetric, non-additive and non-negative challenges specific to the problem, our model for helpfulness achieves the best performance among competing models. We demonstrate the visualization of four course relationships (e.g., mandatory, prerequisite, helpful, and top-k) in a graph. The PATHFINDER demo is publicly available at: http://140.82.60.177:8000/.
Semi-Supervised Structured Sparse Graph Data Classification

Face classification has been developed for many years with the aim of dividing the face image into the identity of the person to whom they belong. The existing classification methods still have some problems such as the classifier is difficult to conform to the multi-level graph model and the data in the real scene rarely has a valid label. In this paper, a semi-supervised structured sparse graph data classification method for face feature extraction is proposed. The method of transforming graph data into uniform length vector is used to construct a semi-supervised method using the manifold that preserves the global data structure and local data structure. Sparse graph data classifier was utilized in which sparse learning automatically obtains the connection relationship between the label data vector and the unlabeled data vector and its weight with the L2 norm to control the model. Finally it realizes the sparse representation of the semi-supervised data. Experiments based on standard face datasets show that this semi-supervised sparse graph data classification method has better classification performance.
Quantitative Evaluation of Clothing Assistance using Whole-Body Robotic Simulator of the Elderly

The recent demographic trend across developed nations shows a dramatic increase in the aging population, fallen fertility rates and a shortage of caregivers. Robotic solutions to clothing assistance can significantly improve the Activity of Daily Living (ADL) for the elderly and disabled. We have developed a clothing assistance robot using dual arms and conducted many successful demonstrations with healthy people. It was, however, impossible to systematically evaluate its performance because human arms are not visible due to occlusion from a shirt and robot during dressing. To address this problem, we propose to use another robot, Whole-Body Robotic Simulator of the Elderly that can mimic the posture and movement of the elderly persons during the dressing task. The dressing task is accomplished by utilizing Dynamic Movement Primitives (DMP) wherein the control points of DMP are determined by applying forward kinematics on the robotic simulator. The experimental results show the plausibility of our approach.
A New Construction for Linkable Secret Handshake

Abstract In this paper, we introduce a new construction for linkable secret handshake that allows authenticated users to perform handshake anonymously within allowable times. We define formal security models for the new construction, and prove that it can achieve session key security, anonymity, untraceability and linkable affiliation-hiding. In particular, the proposed construction ensures that (i) anyone can trace the real identities of dishonest users who perform handshakes for more than k times; and (ii) an optimal communication cost between authorized users is achieved by exploiting the proof of knowledges.
Blockchain Applications in Midstream Oil and Gas Industry

Blockchain is believed to be a foundational technology that will create foundations in the economic and financial systems in a way that will revolutionize the business models and the organizational structures. Blockchain has opened a series of possibilities for blockchain-based innovative applications that are extended to midstream Oil and Gas industry. In the era of the digital revolution, the need for an advanced information and communication technology has become crucial in midstream oil and gas industry to be able support increased challenges of decarbonization, decentralization, digitalization, and security. Blockchain is proposed to be an integrated platform through a series of different applications, such as, metering, billing, carbon trading, security, supply chain management, and performance-based contracts. The opportunities and impacts of utilizing blockchain-based applications will be demonstrated including economic, environmental, operational and social related benefits. Blockchain is disruptive technology, however, the current applications are in early development phase. Key improvement areas have to be assessed to reach the desired levels of scalability, decentralization and security. The paper concludes that this technology has significant potential to enhance the efficiency and maximize the profit that needs further digging from both research and industry. This work provides an overview of the blockchain technology and working principles. It describes the novelty and innovation that blockchains brings to industry, especially when combined with smart contracts. Also, the paper explores blockchain potential for the midstream oil and gas industry and investigates the state-of-the-art current business cases. To the best of our knowledge, this is one of the earliest works to explore blockchain-based applications in midstream oil and gas industry.
A Combined Prediction Method of Industrial Internet Security Situation Based on Time Series

Attacks on industrial control systems have different types and various intensities. Predicting the development trend of security state for updating defense strategy in time is important. Every single prediction model has its different emphases, and the accuracy of single prediction model may be reduced when the system is attacked. Combined prediction model integrates the prediction results of multiple single prediction models to improve the overall prediction accuracy. The weight of the combined model is determined by the mean square error of every single model. It is taken a logarithmic function of the mean square error of each single model to enlarge the difference between the superior and inferior models. Then, the simple weight function is taken to determine the weight of each model based on the logarithm result of each model.This approach makes greater use of accurate model information. Through the comparative analysis of the model, the error of the combined prediction method is obviously reduced. Through the comparison and analysis of the weighted method, the error of the weighted method of the combined prediction model proposed in this paper is the minimum. The combined prediction method can provide more accurate defense opinions for network security administrators.
t-SNE Manifold Learning Based Visualization: A Human Activity Recognition Approach

Visualization of data which is enormously generated at present has become one of the major steps in machine learning before applying the classifier on related domain problems. This paper inculcates the visualization and recognition of human activity from n-dimensional space to low-dimensional space by implementing an algorithm and sometimes best termed as a tool called as t-SNE (t-distributed stochastic neighborhood embedding), which is a non-linear algorithm and has been inspired by SNE (stochastic neighbor embedding). This algorithm is applied to build the relationship between various actions done by human, where class label information has been introduced to well represent the actions from similar action class. This comes under a machine learning methodology called manifold learning, where the class label information helps to classify the action done by human. This learning technique uses probabilistic embedding of neighbors from n-dimensional space with low-dimension to find a mapping of data points from one distribution to another distribution. Experimental results and their comparison with other visualization tools like PCA shows the effectiveness of t-SNE algorithm with human activity recognition and MNIST dataset. The results produced by t-SNE algorithm in terms of visualization are always better than many other algorithms.
Automated Extraction of Security Concerns from Bug Reports

Issue tracker repositories contain a wealth of textual information including bug reports that capture information, often implicit, about Information Security (IS) concerns and vulnerabilities associated with certain issues. Deriving an approach to extract such security concerns from bug reports can yield several benefits, such as bug management (e.g., prioritization) or bug triage. Existing research on Information Extraction (IE) for extracting knowledge from bug reports has mainly focused on supervised learning, which requires a significant amount of human labor in preparing a training corpus. In this paper, we explore a fully automated approach that can extract security concepts (tags) from bug reports without the need for manual training data. This approach can automatically identify and classify bug reports based on their security concepts and textual similarities. In addition, we further enrich these tags with meaningful and representative security names derived from the security domain.
GAAS: An Efficient Group Associated Architecture and Scheduler Module for Sparse CNN Accelerators

Convolutional neural networks (CNNs) have become powerful algorithms in various tasks. Application-specific integrated circuit (ASIC) has been widely used to accelerate CNN on mobile platforms because of its tremendous energy efficiency and performance. Meanwhile, CNNs have become much sparser with the development of network pruning algorithms. Recent works have employed different methods to improve the energy efficiency and performance of ASIC accelerators by utilizing the sparsity character of CNN. However, some of these methods suffer from tremendous output memory overhead and performance degradation induced by hash collisions. To overcome the aforementioned problem, we propose GAAS: an efficient group associated architecture and scheduler module for sparse CNN accelerators. It achieves smaller output memory overhead and higher performance compared with the state-of-the-art accelerator. Our proposed method GAAS mainly consists of two parts: 1) an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -way group associated architecture to reduce the output memory overhead and 2) a scheduler module to improve the performance. Besides, a load-balancing algorithm is proposed and implemented in the scheduler module to improve the performance by reducing the hash collision rate. To demonstrate the efficiency of GAAS, we implement a 4-way image-principal associated architecture with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> PE array and the scheduler module on our proposed method. The experimental results on AlexNet, VGG16, ResNet18, and MobileNet show that GAAS can reduce the output memory overhead by 50%, and it can surely improve the performance of them by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.53\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.62\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.46\times $ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.55\times $ </tex-math></inline-formula> , respectively.
Autonomous Cross-Floor Navigation System for a ROS-Based Modular Service Robot

In this paper, we present an autonomous cross-floor navigation system including mapping, localization, path planning, and scene recognition based on robot operating system (ROS) architecture. The Gmapping algorithm is utilized to build a 2D map with a laser range-finder, and AMCL algorithm is utilized in the robot localization. Moreover, an improved A* algorithm is proposed to prevent robot from getting too close to the wall. Because our robot needs to navigate in the multi-floor environment, a decision system using deep convolutional neural network (DCNN) is also designed to recognize the current floor and the associated map can be download to the robot system. By training with the scene images of the featured location in each floor, the robot can recognize the current floor and then complete the navigation task. Finally, real test of our robot is conducted to demonstrate the feasibility of the proposed method.
Thermal Face Recognition Under Disguised Conditions

A thermal face recognition under disguised conditions using model fusion is proposed. The proposed model fusion has three main approaches: Linear Support Vector Machine Classifier (Linear SVC), Convolutional Neural Network (CNN) and Ordinary Least Square (OLS). A grid of 22 thermal face points based on physiological information is extracted for training Linear SVC. The support vectors of testing images using Linear SVC are calculated to find the hyperplane for classification. The novelty is that we firstly apply temperature information in face recognition. In CNN model, the multiple layers of convolutional layer are utilized to extract more effective features. The fully connected layer (FC layer) is trained using the feature matrix of the last convolutional layer. This FC layer is then a classifier to identify the category of the test image. In the training phase, the predicted values from above two approaches are provided to the OLS for linear regression. The OLS assigns weighting values to these two approaches. This can effectively compensate for the advantages and disadvantages of two approaches. In addition to the comparison with the traditional thermal face recognition, an experiment under disguised conditions was conducted. Experimental results of the proposed method outperform the existing methods.
A privacy‐preserving density peak clustering algorithm in cloud computing

Summary Aiming at preventing the privacy disclosure of sensitive information, issues related to privacy protection in cloud computing have attracted the interest of researchers. To protect the privacy of users during clustering in a cloud computing environment, we present a privacy‐preserving density peak clustering (PPDPC) algorithm that neither discloses personal privacy information nor leaks the cluster centers. Our scheme contains two steps of density peak clustering: First, a cloud service provider calculates the cluster centers without knowing each participant's private data and without disclosing any cluster center information to the other participants, and second, participant allocation is secure and every participant is prevented from identifying the other members of the same cluster. Security analysis and comparison experiments show that the proposed PPDPC algorithm not only obtains good accuracy with respect to density peak clustering but also resists collusion attacks even if the cloud service provider is collaborating with all except one participant. Both theoretical analysis and experimental results confirm the security and accuracy of our method.
Predicting Service Level Agreement Violations in Cloud using Machine Learning techniques

When discussing about Service Level Agreement contract design it is very important to deal with violations in the Cloud. This means that if the scheduling process doesn’t take in consideration some workloads (it cannot schedule them) this will generate issues in the interaction between Cloud Service Customer(s) and the Cloud Service Provider. In our article we deal with estimating the actual workload that is sent to be scheduled on the Cloud Service Infrastructure (CSI). The estimation is based on four different machine learning techniques: Hidden Markov Models, Neural Networks, K Nearest Neighbor and Support Vector Machines. We chose the four different learning techniques to show that workload prediction can be done in various ways. The results show with a good enough confidence that we can predict the actual load on the Cloud Service Infrastructure.
Data Analytics for Security Management of Complex Heterogeneous Systems: Event Correlation and Security Assessment Tasks

This chapter considers the methods and techniques for security management of complex heterogeneous systems with an emphasis on event correlation and security assessment. The approach suggested in the chapter is based on the integrated analysis of big heterogeneous security data for event correlation, including syntactic and semantic analysis of security events and information. The key feature of the approach is the definition of various relationships between event properties within an automated adaptive correlation process. Correlation of heterogeneous security data allows detecting security incidents, as well as the chains of security events that led to these incidents. The results of event correlation are used in various tasks of security assessment. The approach to the security assessment is based on the Bayesian attack graphs, open security data representation standards, and vulnerability indexes from the Common Vulnerability Scoring System. The results of correlation are used on the stage of system assets criticality assessment for assets inventory and on the stage of security assessment to calculate probability of ongoing attack success considering incident statistics. A technique for vulnerability assessment based on the data mining is also described. The advantages and disadvantages of the suggested approaches, methods and techniques are outlined. The purpose of this chapter is to form a methodological basis for data analysis in security management, as well as to demonstrate its practical application, using the data set of event logs from the Windows operating system and from the SCADA power management system.
Simulation Evaluation of Noise Suppressing Technique Based on Decision Based Adaptive Median Filter for Salt&amp;Pepper Noise

Due to statistical characteristic, ROAD (Rank Order Absolute Difference) is one of the highest similar measurement performance in digital image for recognizing whether the impulsive noise pixel or the non-impulsive noise pixel whence the DBAMF (decision based adaptive median filter) was invented for suppressive an impulsive noise in 2010. As a result, the dissertation article aim to scrutinize the performance of the noise suppressing technique based on DBAMF under S&P noise. For evaluating the performance and its limitation of the noise suppressing technique based on DBAMF, the two assessment digital images constituted of Lena and Pepper are exploited in these simulation evaluations under SPN by initially disparaged by adding the impulsive noise (or SPN) at plentiful quantity. Next, each disparaged digital image is refined by the noise suppressing technique based on DBAMF. Moreover, the refined image is confronted with the refined image from the classical SMF (Standard Median Filter) and AMF (Adaptive Median Filter) for proving the DBAMF performance from quantitative indicator perspective.
The Trace Modality

We propose the trace modality, a concept to uniformly express a wide range of program verification problems. To demonstrate its usefulness, we formalize several program verification problems in it: Functional Verification, Information Flow Analysis, Temporal Model Checking, Program Synthesis, Correct Compilation, and Program Evolution. To reason about the trace modality, we translate programs and specifications to regular symbolic traces and construct simulation relations on first-order symbolic automata. The idea with this uniform representation is that it helps to identify synergy potential—theoretically and practically—between so far separate verification approaches.
De(con) Struction of the Lazy-F Loop: Improving Performance of Smith Waterman Alignment

Striped variation of the Smith-Waterman algorithm is known as extremely efficient and easily adaptable for the SIMD architectures. However, the potential for improvement has not been exhausted yet. The popular Lazy-F loop heuristic requires additional memory access operations, and the worst-case performance of the loop could be as bad as the nonvectorized version. We demonstrate the progression of the lazy-F loop transformations that improve the loop performance, and ultimately eliminate the loop completely. Our algorithm achieves the best asymptotic performance of all scan-based SW algorithms O(n/p+log(p)), and is very efficient in practice.
Set-based Design in Agile Development: Developing a Banana Sorting Module - A Practical Approach

Flexible development processes and robust designs are crucial for today's highly dynamic project environments. Agile methods such as Scrum have gained popularity over the years as an effective approach to address these challenges. Although known for providing flexibility, these methods often lack robustness in designs. Pursuing multiple design concepts can be more advantageous when faced with unforeseen changes and uncertainty. Set-based design (SBD) involves the development of alternative designs in a dynamic setting that, as a result, are both robust and flexible. In this paper, we provide a practical approach to combine SBD with the Scrum methodology by using sprint backlogs. It has been proved that the application of SBD, even in small agile projects, can have advantages. We present our results in a product-oriented way to increase the applicability of our approach.
Researches on the Construction of the Smart Campus System with respect to the Higher Vocational Colleges in the Information Age—Taking Dalian Vocational and Technical College as an Example

Abstract This paper analyzes the foundation and feasibility analysis of the construction of the smart campus system in higher vocational colleges, such as Dalian Vocational and Technical College, in the information age, including the existing problems and deficiencies. It proposes the specific framework and concept of conceive for the smart campus from the four levels, the sensing and the network layer, the data analysis layer, the application and service layer and the security maintenance layer. The smart campus aims at serving the teachers and students of the higher vocational colleges and related school-enterprise cooperation enterprises. The construction of a smart campus has far-reaching guiding significance and practical value for higher vocational colleges.