Reviewerly/exHarmony huggingface dataset:
Design of fire alarm system with automatic position

Among all kinds of disasters, fire is one of the most frequent and common disasters that threaten public security and social development. At present, the sensitivity of fire detector is not high, and it is difficult to evacuate and rescue the trapped people. In this paper, a fire alarm system with automatic positioning function is designed. The system adopts ZigBee technology and relies on the mobile node worn by personnel, the detector which integrates various fire signal data is used as the terminal node, ZigBee router and coordinator self-organized network are used to monitor the fire situation and personnel position in the building. Realize accurate positioning of personnel and timely warning of fire.
The co-construction of humor in computer-mediated teacher-student communication

This paper investigates the way online humorous exchanges are jointly constructed and recognized by a teacher and her students. It focuses on the role humor studies play in understanding issues related to Computer-mediated Communication (CMC). The data consist of synchronous discussions of 11 Tunisian learners of English and their teacher via instant messaging on Skype. The analysis uses the notion of script oppositions (SOs) in the General Theory of Verbal Humor (GTVH) and offers an account for the internal structure of exchanges; how the exchange begins with a serious mode, how it gradually develops into a humorous play frame, and how inferences are drawn. The study reveals that humor is created by activating binary SOs and shows that the teacher/student dynamics can make joking online more difficult to interpret than joking among peers. With reference to the analysis of the specific context of the CMC discussions, a serious set up of a humorous sequence created by a teacher can leave students uncertain toward the playfulness of the sequence. A playful set up, on the other hand, makes participants engage in a joint fantasy and build new layers of meaning as the conversation develops.
On the Usefulness of User Nudging and Strength Indication Concerning Unlock Pattern Security

Strong passwords rely on complexity and length, no matter if text-based or of any other type. For text-based passwords, there are many established methods to measure complexity while for graphical passwords, e.g., unlock patterns on mobile devices, it is still an open question what criteria can be used to describe complexity. Also, users tend to choose a stronger password if the strength of their password is visualized. We conduct a user study on the helpfulness of strength indication and user nudging regarding unlock patterns. Participants create such graphical passwords under carefully specified circumstances, e.g., practical nudges on how to improve their password's security. We show that the choice of a strong password does not rely on being tech-savvy and users with different technical backgrounds can be helped by visualizations of a graphical password strength as well as by hints on how to improve it. Most users even perceive this as a helpful feature.
Incorporating Named Entity Information into Neural Machine Translation

Most neural machine translation (NMT) models normally take the subword-level sequence as input to address the problem of representing out-of-vocabulary words (OOVs). However, using subword units as input may omit the information carried by larger text granularity, such as named entities, which leads to a loss of important semantic information. In this paper, we propose a simple but effective method to incorporate the named entity (NE) tags information into the Transformer translation system. The encoder of our proposed model takes both the subwords and the NE tags of source sentences as inputs. Furthermore, we introduce a novel entity-aligned attention mechanism to make full use of the chunk information of NE tags. The proposed approach can be easily integrated into the existing framework of Transformer. Experimental results on two public translation tasks demonstrate that our proposed method can achieve significant translation improvements over the basic Transformer model and also outperforms the existing competitive systems.
Design of solar tree with photovoltaic panel using phyllotaxis phenomenon

A design of solar tree is explained by using the different types of phenomenon of phyllotaxis. This phenomenon is used by the leaves of a tree to convert the solar energy into use full energy for itself likewise the leaves of the solar tree that is PV cells convert the solar energy into electrical energy. The solar energy for generating power is still not completely exploited in the world. The energy received from sun light is an unlimited source of energy for mankind. But the portion of the energy we have utilized not available in any systematic and developed form. So it's discoveries have become lukewarm we require an amount of land to install to install it on large space like rooftop. While earth is facing land scarcity we can't leave our future of renewable energy generation on traditional solar photovoltaic (SPV). But anyway we need to find a path to overcome the power shortage problem as world population is growing faster but we need to remember that that the energy generated must be free from pollution and economical. Solar tree is invented to generate electric power with the solar PV modules by using very less amount of land. Solar tree installation takes much less land of the land for installation as compare to the traditional solar PV module.
Placement of Runtime Checks to Counteract Fault Injections

Bitflips form an increasingly serious problem for the correctness and security of software and hardware, whether they occur inadvertently as soft errors or on purpose as fault injections. Error Detection Codes add redundancy and make it possible to check for faults during runtime, making systems more resilient to bitflips. Codes require data integrity to be checked regularly. Such checks need to be used sparingly, because they cause runtime overhead.In this paper, we show how to use static verification to minimize the number of runtime checks in encoded programs. We focus on loops, because this is where it is important to avoid unnecessary checks. We introduce three types of abstractions to decide correctness: depending on (i) whether we keep track of errors precisely or of their Hamming weights, (ii) how we check whether faults can still be detected, and (iii) whether we keep track of the data or not. We show that checks in loops induce simple and natural loop invariants that we can use to speed up the verification process.The abstractions let us trade verification time against the number of required runtime checks, allowing us to find efficient sets of integrity checks for critical program fragments in reasonable time. Preliminary experimental data shows that we can reduce the number of runtime checks by up to a factor of ten.
Fuzzy Speech Driven Indoor Modeling System

Previous work on text-to-scene generation is based on specified objects input. However, natural language description is uncertain. We propose a fuzzy-input-based scene generation model. Operation is identified by synonym analysis between verbs in the input and operations of insert and delete. A fuzzy speech corpus containing functional descriptions of models is created to help make smarter recommendations. Object is identified by text similarity analysis between the input text and descriptions in the corpus. On this basis, we design a voice-interaction 3D scene generation system. Chinese speech is input in the system and analyzed to extract operations and objects. Recommended objects are retrieved from the 3D model database and placed in the scene. With the average recognition accuracy of 0.90 and the object recommendation precision of 0.89, results showed that our system had a good understanding of Chinese speech input and successfully analyzed operations and objects.
Analysis of Semantic Comprehension Algorithms of Natural Language Based on Robot’s Questions and Answers

The deep learning has made a significant breakthrough and rapid development in the field of natural language processing under the background of big data. We analyzed deep learning algorithms based on natural language processing and its semantic comprehension, and studied the method of natural language semantic comprehension of robot's questions and answers that is suitable for commercial use occasion, by consulting and analyzing related works and literatures, we sorted out and analyzed the methods of existing deep learning algorithms in natural language processing and semantic comprehension, aimed to improve the accuracy of robots in recognizing users' core information and extracting users' true intentions from the theoretical research level. This paper summarized the natural language semantic comprehension algorithms and research progress suitable for robot's questions and answers around preprocessing technology, word sense disambiguation, semantic integrity analysis, etc. on this basis, through contrast tests and performance analysis, and laid the foundation for further scientific research.
UAV Path-Following Strategy for Crossing Narrow Passages

Integration of unmanned aerial vehicles in different tasks and services rely on their ability to deliberate and plan solutions to navigate an environment, which is often subject to geometric constraints that frequently cannot be tackled with classic obstacle avoidance. Therefore, we propose a smooth, closed path that ensures safe passage through challenging locations while respecting a robot's physical limitations. A novel approach for proposing control points in Bèzier curve path planning is outlined and a multi-layer control scheme is presented and validated in simulation and experiments with a varying number of robots and restrictions in motion.
A Unique Approach towards Image Publication and Provenance using Blockchain

The recent spurt of incidents related to copyrights and security breaches has led to the monetary loss of several digital content creators and publishers. These incidents conclude that the existing system lacks the ability to uphold the integrity of their published content. Moreover, some of the digital content owners rely on third parties, results in lack of ability to provide provenance of digital media. The question that needs to be addressed today is whether modern technologies can be leveraged to suppress such incidents and regain the confidence of creators and the audience. Fortunately, this paper presents a unique framework that empowers digital content creators to have complete control over the place of its origin, accessibility and impose restrictions on unauthorized alteration of their content. This framework harnesses the power of the Ethereum platform, a part of Blockchain technology, and uses S mart Contracts as a key component empowering the creators with enhanced control of their content and the corresponding audience.
Hybrid multilayer perceptron-firefly optimizer algorithm for modelling photosynthetic active solar radiation for biofuel energy exploration

Modeling photosynthetic active radiation (PAR) is paramount for decisions in environmental and energy applications such as the growth of plants, agriculture, assessing oceanic conditions for coral bleaching, solar energy, and understanding of climate change/global warming and food security. This chapter develops an optimized hybrid multilayer perceptron-firefly algorithm (MLP-FFA) using advanced artificial intelligence models for PAR forecasting in regional Queensland, Toowoomba using historical lagged PAR as the model input features. The prediction capacity of MLP-FFA was compared to random forest, MLP, and multiple linear regression. Parameter tuning was performed by investigating values of (1) number of input variables; (2) train, validation and test data splits; (3) number of hidden layers; (4) hidden transfer function; and (5) training/learning algorithms, which are primary components of the MLP-FFA and MLP model. In order to select the statistically significant lagged PAR as features to be used as a model input, the partial autocorrelation function was used where a total of nine significantly lagged PAR values (i.e., t−1, t−2, …, t−9) were selected. The results show that MLP-FFA outperformed the other three models in predictive ability, elucidating the effectiveness of such a hybrid-optimized model for future renewable biofuel modeling, as well as the use of satellite data for such projects.
Compliance and application tests of usability guidelines about giving information quickly and comprehensibly

Abstract The objective of this publication is to analyze compliance with the web usability guidelines on Giving information quickly and comprehensibly . The behavior of 20 IT engineers without experience in web usability is analyzed to collect data on the application and compliance of each of the studied guidelines. The objectives are: (1) make a list of recommendations on the presentation of information and possible actions in a quickly understandable way. And highlight the most forgotten guidelines or the worst followed by web developers to think about the importance of offering specific training in this field. (2) Know the most important guidelines according to the participants themselves. To obtain the results, user tests are performed that evaluate the most ignored and applied guidelines. And its correct compliance is studied, since the participants do not have experience in web usability. Besides, interviews are conducted to find out which are the guidelines that they consider most important. It is expected to know if there are guidelines that apply intuitively and why. It is also intended to know if this innate application is helpful or compliance is wrong and needs specific training.
The Coordination Centre Model as a Tool for Increasing Human Capital Efficiency and Commercialization of University Innovations

Abstract Currently, one of the main conditions for the modernization of educational activities and the economy as a whole is the innovative activity of higher education institutions. This article analysed the innovative activities of the St. Petersburg Polytechnic University. Peter the Great, during which it was revealed that despite the rather active development of innovative products by the technical departments, the level of commercialization of the developments is extremely low. The main reason for this phenomenon is weak marketing support, which allows both to predict the demand for certain developments from the market before the implementation of innovative projects and to effectively promote the results of already completed work. To solve this problem, we propose to introduce, on the basis of the Engineering and Economic Institute, the Coordination Centre for the Promotion of University Innovations, which is able to ensure continuous interaction of all departments of the university with the goal of effective commercialization of technologies and developments.
A Case Study in Comparative Speech-to-Text Libraries for Use in Transcript Generation for Online Education Recordings

With a proliferation of Cloud based Speech-to-Text services it can be difficult to decide where to start and how to make use of these technologies. These include the major Cloud providers as well as several Open Source Speech-to-Text projects available. We desired to investigate a sample of the available libraries and their attributes relating to the recording artifacts that are the by-product of Online Education.
Financing Sustainable Development in Africa

Actualizing sustainable development in Africa will require enormous economic resources. This implies that there must be a clear balance of prioritization of financing options on what works best for development in this region without further escalating other societal problems. The authors argue in the chapter that though there has been some benefit from the traditional means of financing development in Africa, some drawbacks still exist considering the rising rate of inequality and poverty head-count in the region. On this note, this present study examines financing options that have previously been advocated for in financing development in the African region (i.e., taking stock), such as development assistance and foreign investment, by considering its implication on development outcomes like poverty, inequality, and aggregate human development indicator in the region.
Gradual syntactic triggering: The gradient parameter hypothesis

In this article, we propose a reconceptualization of the principles and parameters (P&P) framework. We argue that in lieu of discrete parameter values, a parameter value exists on a gradient plane that encodes a learner’s confidence that a particular parametric structure licenses the utterances in the learner’s linguistic input. Crucially, this gradient parameter hypothesis obviates the need for default parameter values. Default parameter values can be put to use effectively from the perspective of linguistic learnability but are lacking in terms of empirical and theoretical consistency. We present findings from a computational implementation of a gradient P&P learner. The findings suggest that the gradient parameter hypothesis provides the basis for a viable alternative to existing computational models of language acquisition in the classic P&P paradigm. We close with a brief discussion of how a gradient parameter space offers a path to address shortcomings that have been attributed to the P&P framework.
Intelligent Feedback on Hypothesis Testing

Abstract Hypothesis testing involves a complex stepwise procedure that is challenging for many students in introductory university statistics courses. In this paper we assess how feedback from an Intelligent Tutoring System can address the logic of hypothesis testing and whether such feedback contributes to first-year social sciences students’ proficiency in carrying out hypothesis tests. Feedback design combined elements of the model-tracing and constraint-based modeling paradigms, to address both the individual steps as well as the relations between steps. To evaluate the feedback, students in an experimental group ( N = 163) received the designed intelligent feedback in six hypothesis-testing construction tasks, while students in a control group ( N = 151) only received stepwise verification feedback in these tasks. Results showed that students receiving intelligent feedback spent more time on the tasks, solved more tasks and made fewer errors than students receiving only verification feedback. These positive results did not transfer to follow-up tasks, which might be a consequence of the isolated nature of these tasks. We conclude that the designed feedback may support students in learning to solve hypothesis-testing construction tasks independently and that it facilitates the creation of more hypothesis-testing construction tasks.
An Enhanced Swarm Method Based on the Locust Search Algorithm

In the evolutionary methods, the optimal balance of exploration and exploitation performance in search strategies improve efficiency to found the best solution. In this chapter, an improved swarm optimization technique called Locust search II (LS-II) based on the desert locust swarm behavior, adapted to an emulation of a group of locusts which interacts to each other based on the biological laws of the cooperative swarm is proposed for solving global optimization problems. Such methodology combines a technique of exploration which avoids premature convergence effectively and a technique of exploitation able to intensify the global solutions. The proposed LS-II method was tested over several well-known benchmark test functions and engineering optimization problems and its performance was further compared against those of other state-of the-art methods such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat Algorithm (BA), Differential Evolution (DE), Harmony Search (HS) and the original Locust Search (LS). Our experimental results show LS-II to be superior to all other compared methods in terms of solution quality and as such proves to be an excellent alternative to handle complex optimization problems.
Exploring the security narrative in the work context

Abstract It is a well-known fact that the language of IT security experts differs from that of non-security-related people, leading to a multitude of problems. However, very little work has examined the differences in perception between security experts within a single security department or company. The sociological theory of power relations and organizational uncertainties by Croizer and Friedberg suggests that uncertainties about the narratives used in a department can lead to potentially harmful power relations and dissatisfied employees. We conducted a qualitative interview study within two distinct IT security companies in order to research the impact of diverging security narratives within security departments. Our results show that there is indeed an uncertainty about the term IT security. However, one company we interviewed regarded this uncertainty as highly beneficial for team creativity, communication, and mutual education, while the other, more technical-focused company showed few diversions within the security staff, but a possibly uniting conflict with the company’s IT department. Our results suggest that conscious shaping of a zone of uncertainty around the security narrative in the work context can be an important management skill for IT security practitioners. Furthermore, we show that the analysis of language uncertainties provides a powerful approach to studying the motivation of professional security groups.
Intercepting a Target Moving on a Racetrack Path

We consider a continuous monitoring application where multiple agents coordinate their positions while traveling along a racetrack. In practice, agents may need to leave the loop for maintenance or recharging, and a new agent has to take its place. The focus of this paper is on the off-the-loop planner that generates a path back to the racetrack, such that the agent arrives to the loop at the appropriate (moving) point with the shortest possible path. Assuming the distance from pursuer's initial position to any location on the racetrack is greater than four times the turn radius of that agent, we develop a planner to find the shortest path that intercepts the moving point on the racetrack and satisfies the agent's kinematic constraints. To develop this planner, Dubins paths are considered, and we present algorithms to find the intercepting path to a point moving on a straight line or a circular arc. These algorithms are integrated together to find the path that intercepts a target moving in the racetrack pattern. We prove that the algorithm finds the optimal solution and demonstrate the results with a few examples.
Discussion of using Machine Learning for Safety Purposes in Human Detection

The reliability and robustness of systems using machine learning to detect humans is of high importance for the safety of workers in a shared workspace. Developments such as deep learning are advancing rapidly, supporting the field of robotics through increased perception capabilities. An early detection of humans will support robot behavior to reduce downtime or system stoppages due to unsafe proximity between humans and robots. In this work, we present an industry-oriented experimental setup, in which humans and robots share the same workplace. We have created our own dataset to detect humans wearing different clothing. We evaluate Faster R-CNN and SSD which are state-of-the-art detectors on two different camera viewpoints. In addition, this paper elaborates on the requirements for validating the safety of such a system to be used in industrial safety applications.