Reviewerly/exHarmony huggingface dataset:
Efficacy of Cell Phones Within Instructional Design

Through qualitative measures, this article seeks to explore the efficacy of cell phones within college-level instructional design specifically through the eyes of the professor. Three main themes evolved from this data: Pedagogical Approaches, Value, and Setting Behavioral Expectations. Based on interviews with ten college professors regarding their experiences with the use of mobile devices within instructional design, findings show that when establishing the right balance with intentional and/or impromptu pedagogical approaches, that value and motivation is evident. Setting behavioral expectations was recommended by all participants in order to convey solid expectations; all expectations were unique, yet successful within the participants' classroom. Participants discussed their experiences within all of those areas and how they utilize cell phones to motivate students, how they set appropriate boundaries, and the overall effectiveness of the cell phone usage in the college classroom is explored.
Turbo Codes

The groundbreaking codes called turbo codes are introduced in [1, 2].
Performance Indicator

What is lacking in the current industrial framework is a consideration on how they can provide their customers with psychological satisfaction. The current engineering is focused on products. And it regards customers as consumers. The current industrial framework emphasizes consumer culture. But as the world is expanding rapidly and it is now an open world without boundary, diversification and personalization are emerging as a key concept. To cope with this shift, we should move away from mass production and consumption to diversified and personalized customization. We should change our industry framework to meet such personal and mental requirements. And as Deci and Ryan pointed out the maximum satisfaction and happiness are achieved when we are internally motivated, and we make self-decision.
Source Localization

Quantitative methods in acoustic emission (AE) analysis require localization techniques to estimate the source coordinatesSource localizationsource coordinates of the AE events as accurately as possible. There are a number of different ways to localize AE sources in practice, i.e. to obtain the desired point estimate in one, two, or three dimensions. This chapter starts with approaches for automated onset detection since the travel time information is one of the most critical input parameters for most localization approaches. In general, most localization methods presented in this chapter have in common that the travel time information from source to receiver is used for localizing an AE source. Most of the methods of AE localization discussed here were developed in the framework of earthquake seismology and GPS techniques. Array-type approaches, which were designed especially for plate-like structures, are also discussed. Different techniques for one, two and three dimensional source localization are described. Approaches based on numerical inversions as well as grid search and array localization approaches are discussed. Further concepts developed or adapted for the AE localization problem presented in this chapter use, e.g., neural networks, probabilistic approaches, or direct algebraic methods from GPS technology. Localization accuracyLocalizationlocalization accuracy is influenced by various factors. Therefore, how to determine localization errors and some measures to ensure high localization accuracy are also listed and discussed.
Deep Fakes

When Kellyanne Conway, a counselor to President Trump, was interviewed on the TV news show Meet the Press in 2017, she invented the term “alternative facts” in the defense of an incorrect estimate made by the President of the number of people attending the 2016 presidential inauguration. Deep fakes—the growing inability to tell true facts from false—becomes an increasingly significant tripping point. The capability to counterfeit almost any fact, of any source of data—physical and biological—including voice, facial images, biological prints, DNA identity and many others is increasing. Data, images, ideas, news, history, science, all can be real or fake and fakes are becoming indistinguishable from the real thing. The inability to distinguish what is false and what is real could bring down security systems, financial systems, health systems, elections, institutions, corporations, and governments, creating an unforeseeable number of tripping points as deep fake-generating technologies continue to evolve.
Coherent orders

A preference relation is a binary endorelation B over an arbitrary ground set X of alternatives. The requisite that B is a strict partial order relation is equivalent to positing that the implication y∈B(x)⇒B(y)⊂B(x) – with B(t) denoting {s∈X:s,t∈B} for all t∈X and with ⊂ denoting strict inclusion – holds true for every pair y,x in X×X. The paper introduces the notion of a coherent order relation, defined as one that satisfies the previous one-way implication as a double implication. Examined are some properties of coherent order relations and how they connect with strict weak order relations and with transitive relations. In particular, the paper shows various conditions for a transitive relation to be a coherent order relation and for a coherent order relation to be a strict weak order relation.
Teaching with a Tablet PC

The modern Tablet PC, such as the Surface Pro® is a flexible teaching tool. It can be used to increase the lecturer's productivity in note taking and in assignment marking. It can be used in the lecture room with increased interaction. With a few minor accessories it can be used to record many aspects of a lecture or presentation. It can also be used to record short topic segments that can be used as references or summaries by students. Containing the abilities of both a tablet device with multi touch, a pen interface for accurate drawing and handwriting and with the power of a full PC, it is a complete teaching studio.
Cloud Database Security Issues and Challenges

To get the desired benefits for the IT enterprise, NoSQL databases must be combined with proven and reliable SQL features into a single proven infrastructure meeting the manageability and security requirements of cloud computing. Various specifications, including SAML (Security Assertions Markup Language), improved interoperability across organizational boundaries are coming up. This chapter discuses some of the security issues of NoSQL databases. All the related technology used in the chapter are explained either where they appear or at the end of the chapter.
Introduction

OpenVX is an Application Programming Interface (API) that was created to make computer vision programs run faster on mobile and embedded devices. It is different from other computer vision libraries, because from the very beginning it was designed as a Hardware Abstraction Layer (HAL) that helps software run efficiently on a wide range of hardware platforms. OpenVX is developed by the OpenVX committee, which is a part of the Khronos Group. Khronos is an open and nonprofit consortium; any company or individual can become its member and participate in the development of OpenVX and other standards, including OpenGL, Vulkan, and WebGL. OpenVX is royalty free, but at the same time, it is developed by the industry: some of the world largest silicon vendors are members of the OpenVX committee.
Face detection algorithm based on improved Retinaface

In view of the insufficient use of the semantic information of high-level feature maps under the existing face detection algorithms, resulting in unsatisfactory face detection results in complex field conditions, an improved face detection algorithm based on Retinaface is proposed. Use the backbone feature extraction network ResNeSt-50 to extract features, combine the feature pyramid CE-FPN to perform multi-scale fusion of feature maps of different scales, and then transfer the information processed by the feature pyramid to the context module, and finally pass the training and loss function Soft non-maximum suppression to realize face detection and feature point positioning. Experimental results show that the average accuracy of this model on the WiderFace dataset is 94.8%, which is better than the original Retinaface model.
ANALYSING THE IMPACT OF PERFORMANCE MANAGEMENT AND INTERNET OF THINGS ON PROJECT SUCCESS IN LARGE ENTERPRISE

The industrial world is flourishing day by day where each organization is trying to reach to the best potential success managing the extreme competitiveness in this contemporary world. Organization success is inevitably determined by efficient performance, and those performances must be organized through a systematic way. Performance manager (PM) and Internet of Things (IoT) are the two crucial things within the organizational structure which ultimately determines the success of the organization. This study is developed illustrating the key features of PM and IoT and theirrole in organizational success. Major purpose of this study demonstrates the functions of these to reach success and how it works connecting the available resources and strategies of the business. Performance management is considered as the key driver of the business which organizes the entire management; similarly the IoT enables the management gathering multiple data of management over the internet. Furthermore, this article focuses on the current situation ofIoT implementation and performance management within organizational boundaries in the entire globe as well as under the domestic boundary of India. In some ways, these appraises and evaluates the entire management depicting the future vision of success and loss, as both of them raises the assumption about the outcomes acquiring propensity to break and make the entire organization. Additionally, this study describes those features why it is necessary to acquirea suitable performance management and IoT implementation in business and how it works in achieving the best potential success of the business in the long-term.
Maritime Anomaly Detection of Malicious Data Spoofing and Stealth Deviations from Nominal Route Exploiting Heterogeneous Sources of Information

Based on a proper stochastic formulation of the vessel dynamic, exploiting piecewise Ornstein-Uhlenbeck (OU) mean-reverting processes, we propose an effective anomaly detection procedure to jointly reveal Automatic Identification System (AIS) data spoofing and/or surreptitious deviations from the planned route. Supported by reliable information from monitoring systems (coastal radars and spaceborne satellite sensors), an expanded five-hypothesis testing problem is posed involving two anomaly detection strategies based on the Generalized Likelihood Ratio Test (GLRT) and the Model Order Selection (MOS) methodologies.
Developing Smart Contracts for Financial Payments as Innovation

Innovation in smart contract systems helps to create decentralized payment systems. These developing concepts will be a new perspective to solve transparency issues. Smart contracts can prove all transactions and flow of funds and money between parties. Business deals and financial payment methods are expected to be a hybrid approach in both traditional and smart contracts in the future. Empirical analysis of smart agreements among their emerged platforms, different applications, and design views will enlighten future needs of trade payment methods. This chapter investigates the concept of smart contracts and critical issues for developing on financial payments environment. This research also aims to examine possible advantages of the application of smart contracts as innovation, legal, and technical aspects of the emerging business environment. The analysis will compare new mechanisms securing a block-chain applied to financial payments. This chapter also reviews the mechanisms of smart contracts and block-chain and focus on predicted future areas on the financial system.
Optimization of auto lamp spraying trajectory based on improved PSO algorithm

In this paper, the optimization method of lamp spraying trajectory is studied, the parabolic coating thickness accumulation rate model is established, and a segmentation algorithm based on lamp spraying is proposed. Taking the deviation between the thickness of the overlapping area and the ideal thickness as the optimization objective, the width and the spraying speed of the two adjacent spraying paths are determined. An improved particle swarm optimization (PSO) algorithm based on edge recombination operator is proposed for the optimal combination of all spraying trajectories on the lamp surface. Simulation experiments and spraying experiments verify the feasibility of the algorithm.
Refined estimation of Li in mica by a machine learning method

Abstract Li-rich micas are crucial in the exploration for and exploitation of Li resources. The determination of Li in mica using classical bulk chemical methods or in situ microanalytical techniques is expensive and time-consuming and has stringent requirements for the quality of micas and reference materials. Although simple linear and nonlinear empirical equations have been proposed, they are inconsistent with the complex physicochemical mechanisms of Li incorporation and commonly lead to large errors. In this study, we introduce a refined method of multivariate polynomial regression using a machine learning algorithm to estimate Li from multiple major oxide abundances. The performance of our regression model is evaluated using the coefficient of determination (R2) and the root-mean-square error (RMSE) of the independent test sets. The best-performed models show R2 of 0.95 and a RMSE of 0.35 wt% for the test set of data set 1 (all compiled data, n = 2124) and R2 of 0.96 and a RMSE of 0.22 wt% for the test set of data set 2 (only data obtained using in situ techniques, n = 1386). Our results indicate that integration of electron probe microanalysis and multivariate polynomial regression (based on data set 1) presents a robust and convenient approach to quantify Li contents in micas. The application of the proposed approach to micas from central Inner Mongolia, NE China, suggests that in addition to the Weilasituo ore bodies, the Jiabusi granite and greisen and the Shihuiyao metamorphic sediment formation have good potential for Li exploration. Our study also provides preliminary constraints on the genesis of Li deposits.
On a Detection Method of Adversarial Samples for Deep Neural Networks

Data-driven classification based on deep learning provides overwhelming results in various applications. However, such neural networks are easily mislead by malicious attacks, if the parameters are known to an attacker. Though the perturbations of such adversarial examples added to the data are so small that the human eye hardly sees it, neural networks provide false results with very high probability. In this paper, we show that generative methods can well be used to visualize the effect to an human operator. By means of the generated samples, an automated detector can be constructed. The performance of such a detector is evaluated for three different architectures. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Digital Divide, Data Trash, and the Commodification of Information

In an attempt to build a theoretical framework for new media within the African context, this chapter considers terminology that routinely surface around ICTs. Terms such as knowledge economy, information poverty, the digital divide, information, knowledge and data though used interchangeably or roughly to mean similar things, they are laden with value, meaning and reflect power relations within a global and local context. Routinely used terms in a field create discourses which in turn reveal sociocultural, economic practices and power relations which reflect certain hegemonies. This chapter attempts to review some of the terms in the field of ICTs in order to make visible the sociocultural and economic power relations embedded in them. The digital divide offers a key entry point into ICT discourses and the opportunities and challenges presented by new media technology within the context of Africa. The theoretical concepts of the digital divide are reviewed within the larger context of global constructions of information wealth and information poverty.
AI-Based Chatbot System Integration to a Social Media Platform for Controlling IoT Devices in Smart Agriculture Facilities

The issue of establishing interaction methods among users, applications and systems involved in Smart Agriculture through interfaces which are simple and friendly in end-usage, is considered to be essential for achieving the maximum possible penetration of the IoT technologies in this sector, for the benefit of sustainability. Herewith, in this paper an attempt is made to encounter this issue through the involvement of intelligent conversational agents in controlling IoT devices applied to Smart Agriculture facilities, by introducing the idea of developing a chatbot system which is integrated to a messenger application of a popular social media platform in natural language environment. This solution is considered to provide an efficient, effective and user-friendly mean of interaction between the end-users and the IoT devices deployed in agriculture facilities.
Phrase-Based Chinese-Vietnamese Pseudo-Parallel Sentence Pair Generation

The lack of Chinese-Vietnamese parallel corpus has resulted in poor translation of Chinese-Vietnamese neural machine translation. In order to solve this problem, we propose a phrase-based Chinese-Vietnamese pseudo-parallel sentence pair generation method. This method expands the corpus of Chinese-Vietnamese neural machine translation and improves the performance of Chinese-Vietnamese neural machine translation. Firstly, based on the small-scale Chinese-Vietnamese parallel corpus, the method selects the phrase module according to the phrase syntactic structure information. Then this method combines word alignment information with replacement rules. Finally, the method achieves the expansion of Chinese-Vietnamese pseudo-parallel corpus. Experiments show that this method can effectively generate Chinese-Vietnamese pseudo-parallel sentence pairs and improve the performance of Chinese-Vietnamese neural machine translation.
Manual assembly action segmentation method based on spatiotemporal features

Accurate segmentation of manual assembly action in an uncertain assembly scene is the premise and important foundation of robot autonomous learning to obtain action sequence. Therefore, this paper proposes a method of hand assembly action segmentation based on spatiotemporal features. This method takes the RGB-D video in the process of manual assembly demonstration as the research object. Firstly, the assembly scene graph of each video frame is constructed. On this basis, the spatial features of each video frame are extracted by using the graph network model. Then, the multi-stage temporal convolution network is used to process the spatial features in the time dimension to obtain the spatial and temporal features of each video frame. The spatiotemporal features pass through the softmax layer to obtain the recognition results of each frame of video, and the adjacent frames with the same action type are combined to obtain the manual assembly action sequence. This method obtains the temporal relationship between the front and back actions and avoids the problems of unaligned action boundaries and unsmooth action fragments in the hand assembly action segmentation method based on a single spatial feature. The experimental results show that the action editing score is improved from 78.18% to 99.28%, which verifies the effectiveness of the method.
Artificial Intelligence Based on Biological Neurons

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and creation of the neural circuits for the sphere of IoT which represent the next generation of artificial intelligence and IoT. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on simulation of biological neurons, creation of biologically close neural circuits where every device will implement the function of either a sensor or a “muscle” in the frame of the home based live AI and IoT. The research demonstrates the developed nervous circuit constructor and its usage in building of the AI (neural circuit) for IoT.
Continuous Teacher-Student Learning for Class-Incremental SAR Target Identification

In this paper, we propose a class-incremental SAR target identification approach based on continuous teacher-student learning. The main challenge of class-incremental SAR target identification is catastrophic forgetting: as the learned model tend to adapt to the most recently seen new identification task, they forget what they have learned before and therefore lose performance on the tasks that were learned previously. Our method aims at introducing the teacher model, which can utilize data from tasks so far, to prevent the student model from catastrophic forgetting. For each task, the teacher model learn to capture the knowledge contained in the tasks by now. When a new task is presented, the student model is encouraged to learn from the teacher model so that the information on which the previous task relied is retained. At the same time, we also make the student model to review its own knowledge to further alleviate catastrophic forgetting. The evaluation of continuous SAR target recognition task shows that this method reduces forgetting effect.
GLARE

Augmented reality (AR) shows the potential for positively impacting learning about cultural heritage. However, current AR tools do not allow users (e.g., teachers, educators) to easily create their own experiences and lessons; there is a significant skill-barrier for producing augmented content. In order to address this problem, the authors created GLARE (GeoLocated Augmented Reality Editor), an open source and extensible AR platform hosted on Github. Utilizing overlaid imagery on live video feed using a hotspot and walking path design. The platform is designed to allow users to create tours by simply adding a media list and associated GPS coordinates. The underlying software architecture uses basic HTML, scripting, and the ThreeJS and Google application programming interfaces. This article describes the framework and then presents a case study of the system being used to create an augmented reality tour based on the events of May 4th 1970 at Kent State University.
Malicious Packages Lurking in User-Friendly Python Package Index

Python has gradually become one of the most important programming languages through artificial intelligence's development. PIP, a package management tool for Python, offers one-click installation, allowing developers to utilize other people's code to speed up development. However, any registered member can easily upload packages to the repository that stores third-party packages. This functionality is used by attackers to poison the package index, i.e., to publish enormous malicious pip packages for installing backdoor, gathering information, etc. To know the situation of third-party packages in the Python community ecosystem, we establish the criteria for judging packages' suspicious or malicious behavior by analyzing the code logic in disclosed malicious packages. With the gained findings, we propose and implement Pip Poisoning Detector (PPD), an approach based on anomaly detection. PPD evaluated 228,723 packages, and after human inspection, we found 63 malicious and 238 suspicious ones among the output 5,699 results. The experimental results prove that our approach is effective and can significantly reduce review workload by 97.51%.