Reviewerly/exHarmony huggingface dataset:
GSCS: General Secure Consensus Scheme for Decentralized Blockchain Systems

Blockchain, a type of a decentralized network system that allows mutually distrustful parties to transact securely without involving third parties, has recently been attracting increasing attention. Hence, there must be a consensus mechanism to ensure a distributed consensus among all participants. Such a consensus mechanism may also be used to guarantee fairness, correctness and security of such decentralized systems. Thus, in this paper we propose a novel consensus mechanism named GSCS that is an improved version of PoW. Compared with existing consensus mechanisms (such as PoW, PoS and so on), GSCS provides strong resistance to resource centralization, the quantum attack and other malicious attacks. In this work, we first present the serial mining puzzle to resist collusive mining and the quantum attack. It guarantees that participants can only obtain a negligible advantage by solving the relevant problem in parallel. Second, GSCS considers the influence of participant credibility. The credibility is reflected by the mining behavior of each participant and directly influence to the mining difficulty of participant. Thus, credible participants enjoy a higher probability of winning the mining competition than do participants who are not credible. Finally, performance of GSCS is analyzed in terms of the common prefix, chain quality, chain growth, and power cost. The results indicate that GSCS is security- and incentive-compatible with suitable security parameter settings. In brief, GSCS has the potential to ensure a more secure and robust environment for decentralized blockchain systems.
How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines

The efficiency of modern automotive body shop assembly lines is highly related to the reduction of downtimes due to failures and quality deviations within the manufacturing process. Consequently, the need for implementing tools into the assembly lines for on-line monitoring, and failure diagnosis, also under the prism of improving the troubleshooting, is of great importance. While the identification of root causes and elimination of failures is usually built upon individual on-site expert knowledge, causal graphical models (CGMs) have opened the possibility to make a purely data-driven assessment. In this demo, we showcase how a CGM of the production process is incorporated into a monitoring tool to function as a decision-support system for an operator of a modern automotive body shop assembly line and enables fast and effective handling of failures and quality deviations.
The Influence of Using Smartphone to Interesting Learning and GPA in Students at Physics Education Department

Research using a quantitative descriptive approach aims to determine the effect of smartphone usage on learning interest and GPA of Physics Education students at Universitas Syiah Kuala. The samples in this study were all physics students in academic year 2016 and 2017. Data collection was obtained by using a questionnaire for smartphone usage and interest in learning, while documentation for student GPA. Data processing using percentage analysis and multiple linear regression test. The results of the statistical test show that there was an influence of the use of smartphones on student interest and GPA. It was obtained that the calculated rcount was greater than rtable that was equal to 17.38. Keywords: Influence, Smartphone Usage, Interests and GPA
A Self-Optimized Generic Workload Prediction Framework for Cloud Computing

The accurate prediction of the future workload, such as the job arrival rate and the user request rate, is critical to the efficiency of resource management and elasticity in the cloud. However, designing a generic workload predictor that works properly for various types of workload is very challenging due to the large variety of workload patterns and the dynamic changes within a workload. Because of these challenges, existing workload predictors are usually hand-tuned for specific (types of) workloads for maximum accuracy. This necessity to individually tune the predictors also makes it very difficult to reproduce the results from prior research, as the predictor designs have a strong dependency on the workloads.In this paper, we present a novel generic workload prediction framework, LoadDynamics, that can provide high accuracy predictions for any workloads. LoadDynamics employs Long-Short-Term-Memory models and can automatically optimize its internal parameters for an individual workload to achieve high prediction accuracy. We evaluated LoadDynamics with a mixture of workload traces representing public cloud applications, scientific applications, data center jobs and web applications. The evaluation results show that LoadDynamics have only 18% prediction error on average, which is at least 6.7% lower than state-of-the-art workload prediction techniques. The error of LoadDynamics was also only 1% higher than the best predictor found by exhaustive search for each workload. When applied in the Google Cloud, LoadDynamics-enabled auto-scaling policy also outperformed the state-of-the-art predictors by reducing the job turnaround time by at least 24.6% and reducing virtual machine over-provisioning by at least 4.8%.
A Framework of Power Pylon Detection for UAV-based Power Line Inspection

Regular power line inspection is of great importance for safe and reliable electric power transmission. Compared with the traditional manual inspection, the use of unmanned aerial vehicle with a camera has the advantages of fast speed, low labor cost, and small personnel risk. In order to automatically select the images that may contain faulty power pylons, we introduce a power pylon detection framework by fusing multi-source information, including camera calibration, power pylon model projection and clustering, and feature extraction and matching. Furthermore, we propose an implementation of this framework and evaluate its effectiveness on real power pylon images. The results show that our method is able to detect the power pylon automatically and accurately, and report the abnormal status of power pylon such as missing poles, which indicates the validity of the proposed method.
Evaluating Machine Learning Classification Using Sorted Missing Percentage Technique Based on Missing Data

Missing data are common in industrial sensor readings owing to system updates and unequal radio-frequency periods. Existing methods addressing missing data through imputation may not always be appropriate. This study presented a sorted missing percentages technique for filtering attributes when building machine learning classification models using sensor readings with missing data. Signal detection theory was employed to evaluate the distinguishing ability of resulting models. To evaluate its performance, the proposed technique was applied to a publicly available air pressure system dataset, which then was used to build several classifiers. The experimental results indicated that the proposed technique allowed a logistic regression model to achieve the best accuracy score (99.56%) and a better distinguishing ability (response bias of 0.0013, adjusted response bias of 0.0044, and decision criterion of −1.8994) compared with the methods applied to the same dataset and reported in papers published between 2016 and 2019 March on binary classification, wherein attributes with more than 20% of missing data were filtered out. The proposed technique is suitable for industrial sensor data analysis and can be applied to the scenarios dealing with missing data owing to unequal radio-frequency periods or a system being updated with new fields.
Learning Dynamic Spatio-Temporal Relations for Human Activity Recognition

Human activity, which usually consists of several actions (sub-activities), generally covers interactions among persons and/or objects. In particular, human actions involve certain spatial and temporal relationships, are the components of more complicated activity, and evolve dynamically over time. Therefore, the description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. In this paper, we develop a method for human activity recognition that tackles these two issues. In the proposed method, an activity is divided into several successive actions represented by spatio-temporal patterns, and the evolution of these actions are captured by a sequential model. A refined comprehensive spatio-temporal graph is utilized to represent a single action, which is a qualitative representation of a human action incorporating both the spatial and temporal relations of the participant objects. Next, a discrete hidden Markov model is applied to model the evolution of action sequences. Moreover, a fully automatic partition method is proposed to divide a long-term human activity video into several human actions based on variational objects and qualitative spatial relations. Finally, a hierarchical decomposition of the human body is introduced to obtain a discriminative representation for a single action. Experimental results on the Cornell Activity Dataset demonstrate the efficiency and effectiveness of the proposed approach, which will enable long videos of human activity to be better recognized.
Cloud Computing Adoption Barriers Faced by Saudi Manufacturing SMEs

Cloud Computing is arguably the most significant technological development after the Internet. It accelerated technology adoption and gave birth to new business models. More importantly, it is acting as a foundation for new technologies like the Internet of Things (IoT) and Artificial Intelligence (AI). The Cloud Computing paradigm provides a level playing field for Small and Medium-sized Enterprises (SMEs) as they are able to adopt technologies that were not affordable before. Governments, vendors and business support organisations across the world have a plethora of initiatives to encourage SME adoption of Cloud Computing technologies. Despite these initiatives, many SME decision makers are still hesitant to adopt the cloud. This research reports an engagement with 16 information technology (IT) managers working for Saudi manufacturing SMEs. This paper presents their perceived barriers to migrating their applications to cloud services.
Facial Expression Synthesis using a Global‐Local Multilinear Framework

Abstract We present a practical method to synthesize plausible 3D facial expressions for a particular target subject. The ability to synthesize an entire facial rig from a single neutral expression has a large range of applications both in computer graphics and computer vision, ranging from the efficient and cost‐effective creation of CG characters to scalable data generation for machine learning purposes. Unlike previous methods based on multilinear models, the proposed approach is capable to extrapolate well outside the sample pool, which allows it to plausibly predict the identity of the target subject and create artifact free expression shapes while requiring only a small input dataset. We introduce global‐local multilinear models that leverage the strengths of expression‐specific and identity‐specific local models combined with coarse motion estimations from a global model. Experimental results show that we achieve high‐quality, plausible facial expression synthesis results for an individual that outperform existing methods both quantitatively and qualitatively.
Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation

Background Artificial neural networks have achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns, and people want to take control over their sensitive information during both the training and using processes. Objective To address security and privacy issues, we propose a privacy-preserving method for the analysis of distributed medical data. The proposed method, termed stochastic channel-based federated learning (SCBFL), enables participants to train a high-performance model cooperatively and in a distributed manner without sharing their inputs. Methods We designed, implemented, and evaluated a channel-based update algorithm for a central server in a distributed system. The update algorithm will select the channels with regard to the most active features in a training loop, and then upload them as learned information from local datasets. A pruning process, which serves as a model accelerator, was further applied to the algorithm based on the validation set. Results We constructed a distributed system consisting of 5 clients and 1 server. Our trials showed that the SCBFL method can achieve an area under the receiver operating characteristic curve (AUC-ROC) of 0.9776 and an area under the precision-recall curve (AUC-PR) of 0.9695 with only 10% of channels shared with the server. Compared with the federated averaging algorithm, the proposed SCBFL method achieved a 0.05388 higher AUC-ROC and 0.09695 higher AUC-PR. In addition, our experiment showed that 57% of the time is saved by the pruning process with only a reduction of 0.0047 in AUC-ROC performance and a reduction of 0.0068 in AUC-PR performance. Conclusions In this experiment, our model demonstrated better performance and a higher saturating speed than the federated averaging method, which reveals all of the parameters of local models to the server. The saturation rate of performance could be promoted by introducing a pruning process and further improvement could be achieved by tuning the pruning rate.
Pembelajaran Online Berbasis Media Google Formulir dalam Tanggap Work From Home Masa Pandemi Covid-19 di Madrasah Ibtidaiyah Negeri (MIN) 1 Paser

Google Forms works as a form of professionalism in the midst of the COVID-19 pandemic in making students keep learning. Preparation to work from home is anticipated with socialization activities using Google Forms as a first step to the success of the education process through online learning. This research was conducted online with data sources of 41 MIN 1 Paser educational community with data collection techniques through interviews, non-participant observation, and document studies. Using a qualitative approach in the form of case studies, data are collected interactively according to the Miles and Huberman models. The results show: MIN 1 Paser teachers during work from home are using Google Forms as an education room and assignment bag during online learning activities. Teacher professionalism is built to always be creative and innovate in the Google Form space. Present meaningful teaching with learning videos and quizzes that motivate students. Create a new cultural tradition of teaching from paper to online form sheets.
An Integrated Teleoperation Assistance System for Collision Avoidance of High-speed UAVs in Complex Environments

UAV teleoperation is a demanding task, especially for amateur operators who wish to successfully accomplish their mission without collision. In this work we present an integrated 2D LIDAR based Sense-and-Avoid system which actively assists unskilled human operator in obstacle avoidance, so that the operator can focus on higher-level decisions and global objectives in UAV applications such as search and rescue, farming etc. Specifically, with our perception-assistive vehicle control design, novel adaptive virtual cushion force field (AVCFF) based avoidance strategy, and integrated sensing solution, the proposed UAV teleoperation assistance system is capable of obstacle detection and tracking, as well as automatic avoidance in complex environment where both static and dynamic objects are present. The proposed system is constructed on the basis of Hector Quadrotor open source framework [1], and its effectiveness is demonstrated and validated on a realistic simulated UAV platform in Gazebo simulations where the UAV is operated at a high speed.
Mask-wearing recognition in the wild

For public health and safety, wearing of masks is one of the most significant means to prevent infections. Additionally, masks protect employees of heavy industry from certain diseases during manufacture. To meet the demand of automatic mask-wearing recognition in scenes of life, we propose a recognition algorithm based on face detection and face attribute recognition. The face detection model not only adopted a fused feature pyramid and a spatial and channel attention mechanism but also a segmentation branch for weak supervision learning. Then for the detected face, we used classification for fast recognition. Moreover, we employed nearly 200000 images, attention mechanisms, data augmentation, and other techniques to enhance the robustness. Besides, this technology has been widely used in Didi Chuxings inspection systems and achieves 99.50% accuracy. Importantly, both the service and key algorithms have been opened to the public to maximize their social and application value.
BLDC MOTOR : MODELING AND OPTIMIZATION SPEED CONTROL USING FIREFLY ALGORITHM

BLDC motor applications used in various forms in instrumentation, robotics, household, and transportation. One application of transportation equipment used as a propeller of electric bicycle vehicles. The value of the bicycle vehicle adjusted to the speed set, the amount that has determined. The purpose used in this study is to improve the efficiency of the regulation of BLDC motors on electric bicycles. Indicators of increasing performance are increasingly steady-state errors, and transient response required. The method used in this research is to do mathematical modeling in the form of transfer and optimization function equations. The model used is the model with the structure of the transfer function, while the optimization method used in this study is the Ziegler-Nichols method and firefly algorithm. The firefly algorithm is used in this study to obtain optimal Kp, Ki, and Kd values. The results showed that the firefly algorithm achieved better performance compared to the Ziegler-Nichols method.
Exploring and Comparing the Performance of Design Methods Used for Information Intensive Websites

At present, web applications are one of the most widely used software applications in different organizations. A website is a virtual representation of the structure of any organization. Good user experience of a website is highly dependent on the content organization or design of a website. Thus, the design is a very important factor to develop a high usability standard website. Different types of design techniques are used to develop different types of websites. It is a very challenging task to develop information-intensive websites using a suitable design technique so that the website can serve its intended purpose with enhanced usability and user experience. Therefore, the objective of this research is to compare the performance of two alternate design techniques for developing an information-intensive website. To achieve this objective, the existing design techniques were explored; and selected the Card Sorting and Interactive Dialogue Model (IDM) techniques to design two websites for an orphanage system. Later, the developed orphanage web systems were evaluated with ten participants through a user study; and found that the IDM design technique performs better than the Card Sorting technique in terms of the system’s usability and user experience.
Robust Stepwise Correspondence Refinement for Low-Altitude Remote Sensing Image Registration

Low-altitude aerial photography using small unmanned aerial vehicles (UAVs) often exists image viewpoint changes as human, nature, and equipment factors. The various viewpoint changes bring a series of problems to the subsequent applications of low-altitude remote sensing images. In this letter, a robust neighborhood structure-invariant descriptor is proposed to solve the scaling, rotation, deformation, and their mixture problems existing in the images. To estimate a robust correspondence under severe mismatches, a dynamic strategy is designed. Using the designed descriptor, a classifier is trained to distinguish between true matches and mismatches. Our method compares with six state-of-the-art methods on 55 small UAV images and performs a feature-invariant capability in horizontal/vertical rotation, scaling, mixture, and extreme situations, and gives the best performance in feature matching and image registration.
e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.
Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems

For autonomous driving, an essential task is to detect surrounding objects accurately. To this end, most existing systems use optical devices, including cameras and light detection and ranging (LiDAR) sensors, to collect environment data in real time. In recent years, many researchers have developed advanced machine learning models to detect surrounding objects. Nevertheless, the aforementioned optical devices are vulnerable to optical signal attacks, which could compromise the accuracy of object detection. To address this critical issue, we propose a framework to detect and identify sensors that are under attack. Specifically, we first develop a new technique to detect attacks on a system that consists of three sensors. Our main idea is to: 1) use data from three sensors to obtain two versions of depth maps (i.e., disparity) and 2) detect attacks by analyzing the distribution of disparity errors. In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method. Based on the detection scheme, we further develop an identification model that is capable of identifying up to n-2 attacked sensors in a system with one LiDAR and n cameras. We prove the correctness of our identification scheme and conduct experiments to show the accuracy of our identification method. Finally, we investigate the overall sensitivity of our framework.