Reviewerly/exHarmony huggingface dataset:
Monocular stereo vision based method for validating path accuracy of industrial robots

The evaluation of industrial robot path accuracy is significant for improving its dynamic operating performance. In this paper, a monocular stereo vision (MSV) based method is proposed for measuring the path error of robot end-effectors. Firstly, the structure parameters of the MSV system are analyzed to determine appropriate imaging parameters of the equivalent virtual binocular vision (VBV). Secondly, to save cost and measurement time, an all-in-one high-accuracy artifact, which integrates path representation and camera calibration, is designed. And accordingly, an improved decoding method is proposed to distinguish different tags on the artifact. After calibrating depth of field (DoF) dependent lens distortion, the path accuracy can be assessed with high-accuracy by the difference between path measured by the vision and the nominal one. Experimental results show that the MSV system with an average measurement accuracy of 0.012 mm can evaluate the path performance of the robot, and the maximum circular path error at 1.5 m/min is 0.252 mm.
Work in Progress: Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

Efficient deployment of Deep Neural Networks (DNNs) on edge devices (i.e., FPGAs and mobile platforms) is very challenging, especially under a recent witness of the increasing DNN model size and complexity. Although various optimization approaches have been proven to be effective in many DNNs on edge devices, most state-of-the-art work focuses on ad-hoc optimizations, and there lacks a thorough study to comprehensively reveal the potentials and constraints of different edge devices when considering different optimizations. In this paper, we qualitatively and quantitatively compare the energyefficiency of FPGA-based and mobile-based DNN executions, and provide detailed analysis.
k Stacked Bidirectional LSTM for Resource Usage Prediction in Cloud Data Centers

Cloud computing leverages virtualization as the most popular technique to deploy enterprise applications on virtual machines. Since the cloud system dynamically adapts to workload changes depending on the time of the day. It is required to ensure elasticity as a robust technique to efficiently model the changing workload requirements. However, it is an extremely challenging task, as several users may enter and depart from the cloud system over time. Predicting the different resource usage metrics of dynamically arriving jobs can help the cloud service providers (CSPs) in better capacity planning to fulfill the service level agreements (SLAs). In this paper, we propose a k clustering-based stacked bidirectional LSTM (BiLSTM) deep learners to model the multi-variate resource usage predictions for highly varying cloud workloads. We evaluate the proposed model on the Google cluster trace and validate its performance with the current approaches.
A Container-Oriented Virtual-Machine-Introspection-Based Security Monitor to Secure Containers in Cloud Computing

In recent years, container technology has been widely used in cloud computing, so the security monitoring technology for containers has also received widespread attention. To enhance the isolation of containers, cloud service providers usually run containers in different virtual machines. In this environment, in-container security tools can be detected or attacked by in-container attackers, and in-VM security tools face the risk of container escape attacks. This paper proposes a container-oriented virtual machine introspection technology to secure containers in cloud computing. It runs in cloud hypervisor and analyzes in-VM containers, so it is more secure and transparent. Even though there is container escaping to the operating system of VM, the security monitors are secure. Firstly, our approach automatically identifies the namespace and container processes in the virtual machine from outside by using virtual machine introspection technology. Secondly, security analysis is performed on processes belonging to different containers in the virtual machine, and our system can perform real-time abnormal response based on the analysis results. Finally, our system can monitor container escape behaviors from outside. Experimental results show that the approach proposed in this paper can automatically perform security analysis for different containers, and can monitor container escape behaviors with acceptable overhead.
What Can We Learn from the Analysis of Information Security Policies? The Case of UK’s Schools

Security standards consider that developing a security policy is a cornerstone in information security management. In practice, the development of a security policy is contextually dependent and there is no agreement on what organisations should include in their security policies. This paper argues that analysing information security policy documents could potentially provide new insights into existing issues with security practices. The paper explores and analyses the content and form of 100 UK schools' information security policies to assess their scope and accessibility. The key findings show that the content varied widely between schools but tended to have a technical focus, many security policies had not been updated to address changes to work practices due to the Covid-19 situation and many policies have poor readability scores preventing readers from engaging with them.
Neutrosophic Function for Assessing the Scale Effect of the Rock Joint Surface Roughness

A new investigation method is proposed for recording large-sized joint profiles and making statistical analyses of the joint roughness coefficient (JRC) values of the 10–300 cm sized profiles. The mechanical hand profilograph is used for joint roughness measurement due to its advantage of easy operation and high accuracy in recording joint traces. Based on the proposed method, it provides sufficient samples from various positions on the large joint profile, which allows the statistical evaluation of JRC values. A neutrosophic number (NN) is employed for revealing determinate and/or indeterminate information as it consists of determinate and indeterminate parts. Due to the uncertainty of JRC in the real world, NN is chosen to represent the JRC value, which is not only random but also a fuzzy indefinite parameter. The neutrosophic function is used to analyze and express the scale effect of joint surface roughness, and its derivative is used to describe the changing trend of the scale effect. The results show that the JRC value of the joint profile is related to the scale and has a negative effect on the surface roughness of the rock joint. The indeterminate information about the scale effect on joint roughness is described by the neutrosophic functions, and the derivative indicated that the JRC values of small samples are more sensitive than those of large-sized examples. When the length of the sample exceeds the stationarity limit of 80 cm, the roughness appears to be almost scale independent.
Multi‐actuator control with modal switching and different disturbance for scanning imaging motion compensation

Scanning imaging can be used to obtain wide field-of-view, high-resolution remote-sensing images with a telescope lens. However, the motion between the image and the sensor causes blurring during scanning, which reduces the image quality. In this paper, a motion compensation method based on multi-actuator control with modal switching is proposed. In this method, the tracking controller realizes scanning and compensation; the cooperative controller reduces the relative motion that causes motion blur, and modal switching solves the travel limitation of the linear actuator. Further, to improve the control performance, an adaptive sliding mode controller is proposed and combined with a disturbance observer. The stability theorems for multi-actuator and modal switching are studied. Experimental results show that the accuracy, robustness, and frame rate of the proposed method improved compared with those of traditional methods. Compared with the non-cooperative structure using a PD controller, the root mean square error is reduced by 76.61 % .
Exploratory analysis of the Monte Carlo tree search for solving the linear ordering problem

Monte-Carlo Tree Search has delivered great results in two-player game-playing and its current success has turned it into a popular choice of study in different use cases. Recently, many works have applied MCTS and, especially, its neural variant, as an end-to-end approach to solve Combinatorial Optimization Problems. However, its efficiency for solving regular Combinatorial Problems has still to be studied.
Deontic Rule of Rule-Based Service Choreographies

Service choreography describes the interaction across different participating services capturing the ordering constraints of global message exchanges. Semantics of Business Vocabulary and Rules (SBVR) model, an Object-Oriented Management (OMG) standard is proposed to specify services interaction (service choreographies), The specification focuses on a deontic rule expressing both prohibition and obligation which can be progressively helpful in working with coordinating service interactions. Then Alloy Analyzer, a relational constraint solver, is used to transform a developed service choreographies, an SBVR model into an Alloy model. This transformation allows to generate and to verify, the choreography for the developed SBVR model and the realisability of the generated choreography, automnatically.
ORA, FCS, and PT Strategies in Functional Enrichment Analysis

Downstream analysis of OMICS data requires interpretation of many molecular components considering current biological knowledge. Most tools used at present for functional enrichment analysis workflows applied to the field of proteomics are either borrowed or have been modified from genomics workflows to accommodate proteomics data. While the field of proteomics data analytics is evolving, as is the case for molecular annotation coverage, one can expect the rise of enhanced databases with less redundant ontologies spanning many elements of the tree of life. The methodology described here shows in practical steps how to perform overrepresentation analysis, functional class scoring, and pathway-topology analysis using a preexisting neurological dataset of proteomic data.
Audio Enhancement of Physical Models of Musical Instruments Using Optimal Correction Factors: The Recorder Case

A simulation of a musical instrument is considered to be a successful one when there is a good resemblance between the model’s synthesized sound and the real instrument’s sound. In this work, we propose the integration of physical modeling (PM) methods with an optimization process to regulate a generated digital signal. Its goal is to find a new set of values of the PM’s parameters’ that would lead to a synthesized signal matching as much as possible to reference signals corresponding to the physical musical instrument. The reference signals can be: (a) described by their acoustic characteristics (e.g., fundamental frequencies, inharmonicity, etc.) and/or (b) the signals themselves (e.g., impedances, recordings, etc.). We put this method into practice for a commercial recorder, simulated using the digital waveguides’ PM technique. The reference signals, in our case, are the recorded signals of the physical instrument. The degree of similarity between the synthesized (PM) and the recorded signal (musical instrument) is calculated by the signals’ linear cross-correlation. Our results show that the adoption of the optimization process resulted in more realistic synthesized signals by (a) enhancing the degree of similarity between the synthesized and the recorded signal (the average absolute Pearson Correlation Coefficient increased from 0.13 to 0.67), (b) resolving mistuning issues (the average absolute deviation of the synthesized from the recorded signals’ pitches reduced from 40 cents to the non-noticeable level of 2 cents) and (c) similar sound color characteristics and matched overtones (the average absolute deviation of the synthesized from the recorded signals’ first five partials reduced from 41 cents to 2 cents).
EmojiCam: Emoji-Assisted Video Communication System Leveraging Facial Expressions

This study proposes the design of a communication technique that uses graphical icons, including emojis, as an alternative to facial expressions in video calls. Using graphical icons instead of complex and hard-to-read video expressions simplifies and reduces the amount of information in a video conference. The aim was to facilitate communication by preventing quick and incorrect emotional delivery. In this study, we developed EmojiCam, a system that encodes the emotions of the sender with facial expression recognition or user input and presents graphical icons of the encoded emotions to the receiver. User studies and existing emoji cultures were applied to examine the communication flow and discuss the possibility of using emoji in video calls. Finally, we discuss the new value that this model will bring and how it will change the style of video calling.
Improve Students’ Learning Experience in General Chemistry Laboratory Courses

The current shift from a traditional classroom laboratory to a partial remote virtual lab is mainly due to COVID-19 pandemic. This pedagogical change is forcing academic institutions to rethink and redesign their lab courses. VR is promising technology to support this change. This study reviews current literature of the VR systems applied in higher education, clarifies some technical terms related to VR technology, and identifies the strengths and weaknesses of this technology. After a semi-interview with faculty members and students in the Chemistry department at a US university, we propose a new hybrid lab design with VR technology based on the literature review and interview results.
How Simulation Training Can Benefit from Virtual Reality Extensions? Case: A Virtual Reality Extension to a Simulated Ship Bridge for Emergency Steering Training

This paper presents a Virtual Reality (VR) training application that is connected to maritime training simulator. We call this a VR extension to simulation training. The solution enables a more comprehensive training scenario than a traditional simulator exercise. The case study concerns emergency steering procedure, in which the steering of the vessel must be handled manually from the steering gear room with only an audio connection to the bridge. The steering gear room is implemented as a VR training connected to the bridge simulator. The paper presents the technical implementation and the training process where the VR application has been integrated as a part of the simulator training. The implementation was tested with maritime professionals and teachers. The results validate the feasibility of the training scenario and the usability of the VR implementation. Furthermore, the interviews provide insight into the potential of VR-extended simulator training scenarios in maritime training.
Intelligent Security Image Classification on Small Sample Learning

Image classification is an important task in the field of the intelligent security and deep learning methods represented by convolutional neural networks have achieved many great results in this field. Image classification based on deep learning usually performs well on large-scale datasets, but its performance is often greatly limited by the size of the data. When the dataset is not sufficient, the traditional deep learning method cannot perform well on the small-scale datasets and this situation often occurs in practical applications. To address the drawback, we propose a deep learning framework based on the combination of SoftMax classifier and Bayes learning for small-sample image classification. Within this framework, we utilize transfer learning to solve the problem of too few data, and it can also reduce model training time and space costs. At the same time, we make use of the combination of the above two classifiers to improve the effectiveness and accuracy of the model on different datasets. We empirically find that the model has higher classification accuracy and less training time than the general deep learning model on the datasets. The experiment results demonstrate that the proposed method generally has better classification accuracy on the small-scale datasets, compared with mainstream methods.
Leakage-Suppressed Range Query Scheme for Structured Data in IoT

The structured data collected by the Internet of Things can be encrypted for protecting the user’s privacy. Range query can be used to get the expected data with some specific attributes among the encrypted data, that is, given the upper and lower limits <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(x, y)$</tex-math></inline-formula> of a certain attribute, the range query will get all the records whose corresponding attribute values are in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(x, y)$</tex-math></inline-formula> . However, in the structured encryption scheme with range query, there is a certain amount of information leakage, which will lead to the so-called inference attacks, i.e., the attacker can obtain the user’s attribute values. To hide attribute values and their relationship, this article transformed the problem of the numerical comparison between two attribute values into the problem of the intersection of two sets. By using the Bloom filter, the elements in the attribute value collection are recorded and determined whether the intersection of the two sets is empty. This ensures that our scheme effectively resists inference attacks. Besides, by multiplying the endpoints of the range interval by a large number, we gave an improved scheme to hide the user’s search pattern. In the query process, our scheme will not leak the upper and lower limits of the range value and will not leak the relationship between the attribute value and the range interval. This will prevent the attacker from inducing the relationship of attribute values by multiple range queries. Finally, we conducted a simulation evaluation of the scheme by using the published novel coronavirus pneumonia data, and the results show that our scheme has a better performance than the existing schemes.
Segmentation of WM, GM, and CSF from the Brain MRIs Using an Advanced Metaheuristic Approach

Segmentation of brain tissues from the Magnetic Resonance Image (MRI) is vital to detect and diagnose brain-related diseases. Precise brain MRI segmentation is challenging because of the unpredictable anatomical structure of the brain, existence of Intensity Inhomogeneity (IIH), Partial Volume (PV) effects, and noise. Most of the brain disorders can be identified exactly by the segmentation of brain tissues, such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The optimization algorithmic approach is widely used for the brain image segmentation. In this work, a robust metaheuristic Multi-level Thresholding (MT) technique named as Advanced Wind Driven Optimization (AWDO) is employed for the brain MRI segmentation. The optimal MT values can be attained by combining the AWDO with the very popular Otsu’s objective function. The results obtained by the proposed AWDO in segmenting various brain images at an optimized value of threshold are compared with the groundtruths and have given the high values for the metrics such as sensitivity, specificity, segmentation accuracy, dice coefficient, and Precision. The values of the objective function, Peak Signal to Noise Ratio (PSNR), Standard Deviation (SD), and Convergence time of the proposed method are also being obtained at various threshold levels. The results are compared with the results of some existing algorithms like Multiplicative Intrinsic Component Optimization (MICO), Electromagnetism Optimization (EMO), Histogram based Darwinian Particle Swarm Optimization (HDPSO), Fully Convolutional Neural Networks (FCNN), and Advance Ant Colony Optimization (AACO). The results justified the notable performance of the proposed approach in segmenting different brain tissues.
Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.
Research Progress and Development of Deep Learning Based on Convolutional Neural Network

As a popular branch of the field of artificial intelligence and machine learning, deep learning has received increasing attention and continuous development. This paper first introduces the development and current situation of deep learning. Then, it introduces the component and mathematical theories of convolutional neural network (CNN). As for CNN optional variables and parameters, the optimal range of each parameter tested is explored through the training and tests on the datasets of Fashion-MNIST and CIFAR20 respectively. Finally, this paper proposes existing defects and future development of deep learning based on CNN.
Building Bi-script Language Resources for the Tunisian Dialect’s NLP

Language resources like corpora, lexicons and dictionaries are the key element to automatically process any natural language. In this paper, we focus on the written Tunisian dialect (TD) which is abundantly present on social media and yet still qualified as a low-resource language. We automatically construct sizable bi-script TD language resources from the social web using two deep learning based-NLP components for the TD identification and the TD transliteration. We target the Latin transcription of the language and we use it to generate TD language resources written in the Arabic script. The presented work resulted in creating a Romanized TD corpus composed of 284,894 TD messages extracted from YouTube, an Arabic TD corpus generated through an automatic Latin to Arabic transliteration of the extracted Romanized TD corpus, and two bi-script TD dictionaries (Latin-to-Arabic and Arabic-to-Latin), respectively composed of 293,570 entries and 155,954 entries. The creation process and the constructed TD resources are described and evaluated.