Reviewerly/exHarmony huggingface dataset:
Study on the Online Teaching of College English Course in the Era of "Internet + Education"

Nowadays, online teaching and learning as a new form of education has been widely used for all age groups and in almost all subjects. Under the background of "Internet + Education", college English classes are also trying to explore new teaching modes and methods, such as MOOC, micro-class, flipped classroom [4]. From the beginning of 2020 till now, because of the spread of the pandemic, there are many countries and regions that have been seriously affected and therefore have to convert the ordinary classroom teaching to complete online teaching. This article takes the 2019-2020-2 semester, in which the author’s university has launched complete online teaching, as an example and explore the online teaching mode of college English course. In the paper, the online teaching modes that was used for the whole semester are first introduced; Then, the study compares students’ academic performances of College English II with their scores of College English I when they had classes in campus; finally, the advantages and disadvantages of online teaching and learning are discussed in details based on interviews of teachers and students involved in the online course, suggestions for college English teaching and learning are also provided afterwards.
Extraction Method of Athlete's Shooting Motion Trajectory Based on Image Segmentation Algorithm

The shooting action is one of the commonly used offensive methods in basketball games. This article proposes a method of extracting the basketball shooting action based on the image division algorithm. Aiming at the unique feature of the basketball shooting object in the basketball video, the video image is mixed with Gaussian modeling, and the Grab Cut algorithm is used to divide the basketball object to realize the extraction of the basketball foreground. The results of the simulation experiments show that in the video of the basketball ball, the goal of the basketball shot is divided and extracted. It has good results, and the method has the advantages of less interactive operations and high accuracy of division, which can extend the color field of the image.
Concept of Cloud Computing in ESB

Cloud computing is evolving as a very important IT service platform with Its advantages of cost effectiveness and international access. To become a wide adopted IT infrastructure and service platform, cloud computing should be integrated with different systems in organizations. In academia, there's terribly restricted study of cloud computing integration. In follow, the industry lacks a comprehensive systems integration design or tools that may integrate any system universally. Built upon Enterprise Service Bus (ESB) as an integration backbone, this text proposes a universal integration design. With this design, any system or service (e.g., ERP and cloud computing) will simply be integrated through the ESB without needing software system renovation. so as to completely support the enterprise-level business operations during a heterogeneous computing setting, this design conjointly introduces a rule-based business method management (BPM) engine to contour business method management across disparate systems.
Solar Based Automatic Irrigation System

This study was conducted with few objectives of design a microcontroller based solar powered automatic irrigation system (AIS) model. To quantify the paddy field water content of and as well to provide adequate water supply in the right paddy field areas. In agricultural areas this may help for the production of crops as well can prevent the wastage of energy. To provide an efficient design to the farmers is the main objective of this paper.
The NIST Cybersecurity Framework

With the increase in cybercrimes over the last few years, a growing realization for the need for cybersecurity has begun to be recognized by the nation. Unfortunately, being aware that cybersecurity is something you need to worry about and knowing what steps to take are two different things entirely. In the United States, the National Institute of Standards and Technology (NIST) developed the Cyber Security Framework (CSF) to assist critical infrastructures in determining what they need in order to secure their computer systems and networks. While aimed at organizations, much of the guidance provided by the CSF, especially the basic functions it identifies, are also valuable for communities attempting to put together a community cybersecurity program.
Angle-Only, Range-Only and Multistatic Tracking Based on GM-PHD Filter

Multi-object detection and tracking with spatially distributed sensor networks are used in many applications across the domains of autonomous and surveillance systems. The sensors typically used in these systems often provide incomplete observations such as bistatic and angle- or range-only measurements, thus posing a challenge to the task of retrieving the targets and estimating their state. In this paper, we first present a new variant of a multi-sensor tracking algorithm based on the Gaussian-mixture probability hypothesis density (GM-PHD) filter. Next, we show how it can be applied on fusing incomplete observations. For tracking asynchronous range- and angle-only measurements, we leverage the well-known concepts of angle and range parametrization, respectively, to describe the adaptive target birth density based on the parameters of received observations. In the case of multistatic tracking, we propose parametrizing the birth density from target hypotheses, generated by statically fusing bistatic range measurements, using the M-best S-D assignment algorithm. We investigate the performance using challenging simulation scenarios and evaluate it with established tracking metrics. Our preliminary results demonstrate the effectiveness of the proposed algorithms. Furthermore, for range- and angle-only fusion, the more common use case of unsynchronized sensor measurements is supported. While many algorithms in the literature are tailored for a specific problem, we show that the proposed GM-PHD tracker is generic and can be potentially leveraged in a wide range of sensor fusion and tracking applications.
Designing Identities

Abstract This chapter examines the ways in which the automotive and appliance industries approach concepts of sound branding to delineate both product and consumer identities. The chapter elucidates how sonic aspects of the car-driver experiences—whether it is the sound of doors closing, distinctive engine sounds, or the variety of interior warning indicators and chimes—are carefully designed to appeal to and identify with various target markets. The growing complexity of built-in or “intentional” sound sources and of the sonic experience of operating cars and appliances also calls into question the relationship between machines and people. Marketers and sound designers attempt to inculcate the “emotional values” they want consumers to associate with a product. As such, people inhabit a world of commodities that are increasingly marketed to them as anthropomorphic, sentient entities that give the appearance of sharing their values and enabling their lifestyle choices.
Multi-Sensor Motion Fusion Using Deep Neural Network Learning

Hand pose estimation for a continuous sequence has been an important topic not only in computer vision but also human-computer-interaction. Exploring the feasibility to use hand gestures to replace input devices, e.g., mouse, keyboard, joy-stick and touch screen, has attracted increasing attention from academic and industrial researchers. The fast advancement of hand pose estimation techniques is complemented by the rapid development of smart sensors technology such as Kinect and Leap. We introduce a hand pose estimation multi-sensor system. Two tracking models are proposed based on Deep (Recurrent) Neural Network (DRNN) architecture. Data captured from different sensors are analyzed and fused to produce an optimal hand pose sequence. Experimental results show that our models outperform previous methods with better accuracy, meeting real-time application requirement. Performance comparisons between DNN and DRNN, spatial and spatial-temporal features, and single- and dual- sensors, are also presented.
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI)

The 4th International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) will bring together HRI, robotics, and mixed reality researchers to address challenges in mixed reality interactions between humans and robots. Topics relevant to the workshop include development of robots that can interact with humans in mixed reality, use of virtual reality for developing interactive robots, the design of augmented reality interfaces that mediate communication between humans and robots, the investigations of mixed reality interfaces for robot learning, comparisons of the capabilities and perceptions of robots and virtual agents, and best design practices. Special topics of interest this year include VAM-HRI research during the COVID-19 pandemic as well as the ethical implications of VAM-HRI research. VAM-HRI 2021 will follow on the success of VAM-HRI 2018-20 and advance the cause of this nascent research community.
Topic Modeling

This chapter introduces topic modeling using the mallet package and topic-based word cloud visualization using the wordcloud package. (In this chapter we assume that readers are already familiar with the basic idea behind topic modeling. Readers who are not familiar may consult Appendix B for a general overview and some suggestions for further reading.)
ComplexRec 2021: Fifth Workshop on Recommendation in Complex Environments

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that seek to provide personalized interactions to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Applications require more complex domain-specific constraints on inputs to the recommender systems. Likewise, the outputs of recommender systems are moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. For the past four years, the ComplexRec workshop series has offered an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.
Improving Performance Loan Fraud Model Prediction Using Mean Decrease Accuracy and Mean Decrease Gini

The purpose of this research is to develop a fraud detection model on loan transactions at failed banks in the context of deposit and deposit guarantees mandated to the Indonesia Deposit Insurance Corporation (IDIC). The data used in this study is the data of a bank in the Jakarta area that had liquidated at the end of 2015. Meanwhile, data on loan transaction ranges ranged from 2010 to 2015. This research also focuses on improving the performance of detection models by using feature selection. With the feature selection, it expected that the impact of the reduced performance of the model exposed to high variance and high bias due to the many features used can handled better.
Structural dynamics

Ch. 1 gives the theoretical background on the structural dynamics. This chapter is organized in two sections. The first section gives the theoretical background on the single-degree-of-freedom systems, whereas the second section is on the multi-degree-of-freedom systems. Initially, damping-free systems are discussed, but then the damping in the form of the viscous and hysteretic model is presented. Free and forced responses are discussed. The theory for harmonic, periodic, and general forcing excitation is presented. The strain/stress response, later important in fatigue estimation, is related to the theory of structural dynamics. In real applications the dynamically loaded structures are attached to the supporting vibrating structure (e.g., an alternator to the engine), whereas in the laboratory the dynamical loads are usually applied using an (electrodynamic) shaker. Without proper care, the real application and laboratory replication can lead to significantly different dynamical systems, and therefore this chapter also discusses the details of base-excited structures and the difference of the dynamic excitation and the kinematic excitation. Finally, incomplete models are discussed as flexible structures have, theoretically, an infinite number of natural frequencies, whereas the experimental/numerical models are limited with regard to the number of natural frequencies.
The Impact of Human Behavior on Cyber Security

Perhaps the greatest challenge to cyber security is that people are inherently behind each cyber problem as well as its solution. The reality is that people have been stealing secrets and information and attacking others for thousands of years; the technology of the Internet just allows it to happen at a faster pace and on a larger scale. This chapter describes aspects of human behavior that impact cyber security efforts. Cognitive overload, bias, incentives and behavioral traits all affect the decision making of both those who develop policy and strategy, those who fall victim to cyber attacks, and those who initiate cyber attacks. Although limited research has been completed on the behavioral aspects of cyber security, many behavioral principles and models are applicable to cyber security issues.
Bug Handling in Service Sector Software

The technology enabled service industry is emerging as the most dynamic sectors in world's economy. Various service sector industries such as financial services, banking solutions, telecommunication, investment management, etc. completely rely on using large scale software for their smooth operations. Any malwares or bugs in these software is an issue of big concern and can have serious financial consequences. This chapter addresses the problem of bug handling in service sector software. Predictive analysis is a helpful technique for keeping software systems error free. Existing research in bug handling focus on various predictive analysis techniques such as data mining, machine learning, information retrieval, optimisation, etc. for bug resolving. This chapter provides a detailed analysis of bug handling in large service sector software. The main emphasis of this chapter is to discuss research involved in applying predictive analysis for bug handling. The chapter also presents some possible future research directions in bug resolving using mathematical optimisation techniques.
Trajectory Prediction Based on Roadside Millimeter Wave Radar and Video Fusion

In recent years, with the development of intelligent transportation system, roadside service is more and more widely used in urban road transportation system. Based on the roadside millimeter wave radar and camera, this paper obtains the motion trajectory data of vehicles close to the intersection, filters and processes the radar data, detects and tracks the video data, and then carries out data fusion to obtain more accurate trajectory data. Compared with a single sensor, the accuracy and stability of fused data are improved. In addition, the LSTM neural network is used to predict the vehicle trajectory and obtain the location information of the target vehicle passing through the intersection, so as to improve the traffic condition of the intersection.
Hexa-Dimension Code of Practice for Data Privacy Protection

Cyberspace inhabitants live under threat of a complex data privacy protection problem in a technology-dependent and information-intensive phenomenon grown out of a vicious circle. The frontline information security professionals are among the first to bear the brunt and are in dire need of guidance for enforcing effectively the policies and standards and mitigating the adverse consequences of data privacy breaches since the policy statements are invariably dated due to the rapid advances of the technology, limited to cope with techno-socio threats, inadequate to deal with the well-equipped and cunning cybercriminals, and vague and less than user-friendly, or simply difficult to absorb and follow. A framework that comprises the newly developed hexa-dimension code of practice based on the six-dimension metric (represented by the LESTEF model) and an operationalization scheme are proposed, where the code in which the gist of the adopted policies is incorporated promises to be a handy reference or a quick guide capable of alleviating the information security staff's burden.
Choosing an appropriate sensor for movement analysis: the case study of clipping in fall detection

The synthesis of TiO2 nanotubular films is presented together with their physical, chemical, electrochemical and photoelectrochemical characterization in order to assess their use as photocatalyst. The synthesis was made through the anodic oxidation procedure using an inorganic fluoride based electrolyte. The nanotubes presented an inner diameter of 54 nm, a wall thickness of 18 nm and a length of 570 nm in amorphous condition. After annealing at 500 °C the alignment and definition of the nanotubes were improved observing more space between the nanotubes, contrary to that observed at 700 °C, in which a detrimental morphology was seen, presenting a disruption of a little portion of the superior edge of the nanotubes. The electrochemical characterization was made for four samples: the amorphous one and two crystallized samples at 500 (TNTC-5) and 700 °C (TNTC-7) and for Ti. The TNTC-5 (constituted by anatase-rutile) which improved its morphology and kept its mechanical strength after annealing, presented the best electrochemical stability after 5 days of exposure. In conclusion, TNTC-5 sample showed to be a good candidate for photocatalysis processes due to: (i) its better ordered 1D nanostructure geometry with longer nanotubes after annealing compared to that of the TNTC-7 sample, which conferred an increase of surface area, (ii) due to short length nanotubes, since better efficient light absorption and fast carrier transport is expected, and thus less unwanted recombination losses, (iii) its chemical structure composition of anatase-rutile in comparison to that at 700 °C that transformed completely in rutile, (iv) a clean top surface, (v) the preservation of stable morphology after exposing to corrosive electrolytes in long term showing a high corrosion stability and (vi) its major and stable photocurrent density.
Extending R2RML-F to support dynamic datatype and language tags

Robotic Process Automation (RPA) is used in various fields of human activity in order to implement faster and more secure processes through a reduction in the risks or errors but also an increase in the productivity rates. The increase of its use and importance calls for evermore efficient solution methods for this problem. In this paper, the RPA is addressed in the context of a financial institution. The problem consists in assigning transactions to software robots, where each transaction type has a different clearance date and a different processing time. First, four heuristics are used to compute an upper bound on the number of required software robots. Then, this bound is given as a parameter to an integer linear program, which is used to assign the transactions to the different robots. The quality of the solutions is assessed by an extensive experimental study on a set of 39,000 instances. The results show that two heuristics outperform the others and allow for a faster resolution by the integer linear program which in turn finds the optimal solution for most of the instances within a timeout of 60 seconds.
A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks

Structured Query Language injection (SQLi) attack is a code injection technique where hackers inject SQL commands into a database via a vulnerable web application. Injected SQL commands can modify the back-end SQL database and thus compromise the security of a web application. In the previous publications, the author has proposed a Neural Network (NN)-based model for detections and classifications of the SQLi attacks. The proposed model was built from three elements: 1) a Uniform Resource Locator (URL) generator, 2) a URL classifier, and 3) a NN model. The proposed model was successful to: 1) detect each generated URL as either a benign URL or a malicious, and 2) identify the type of SQLi attack for each malicious URL. The published results proved the effectiveness of the proposal. In this paper, the author re-evaluates the performance of the proposal through two scenarios using controversial data sets. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed model in terms of accuracy, true-positive rate as well as false-positive rate.
Rethinking of Reentrancy on the Ethereum

Smart contract is one of the key technologies of the blockchain and is becoming more and more popular. But at the same time, the economic losses caused by the vulnerabilities of smart contracts have also emerged in recent years. One of the most famous smart contract security issues is reentrancy. There have been many related reports and researches on reentrancy. However, the existing methods only pay attention to offensive reentrancy one-sidedly, but do not conduct a systematic and comprehensive analysis of reentrancy and some existing reentrant detection methods for smart contracts often have high false positives(FPs). We proposed a more accuracy and comprehensive detection and analysis method of reentrancy from real Ethereum transactions. In general, we used Datalog-based formulation to detect reentrant transactions in real world of Ethereum, and compared the detection results with other tools. We finally detected 2,712 reentrant transactions and 10 vulnerable smart contracts with zero false positive, furthermore, we found that many vulnerable contracts detected by other tools did not trigger real reentrant transactions. We summarized the problems in the existing detection tools and gave effective preventive measures. In addition, we also analyzed the detected benign reentrancy to further confirm the above findings, and the discovery that benign reentrancy also has its certain application scenario.
Integration of Dynamic Security for Lifetime Enhancement in Underwater Sensor Networks

Underwater Sensor Networks are susceptible from assorted attacks and threats which can consume and destroy the security and energy from the network. A number of approaches are devised for security and integrity still the integration of effective cryptography is one of the challenging tasks. Dynamic security using cryptography can enhance the security and related lifetime because lesser exploitable by the attacks can secure and network and overall performance can be improved. Security is the key to underwater network security challenges, since it supports protection, secrecy and personality, which together give the basics to trusted e-business and secure correspondence. There is an expansive scope of cryptographic algorithms which are classically utilized for securing systems and in no time consistent looks into on the new cryptographic calculations are continuing for developing more propelled methods for secures correspondence. In this research manuscript, a detailed view of dynamic security approaches are presented with the proposal of a new multilayered, secured and energy aware approach so that the overall lifetime of underwater sensor networks can be escalated.