Reviewerly/exHarmony huggingface dataset:
Post-treatment methods for organic solid wastes

The anaerobic process is considered to be a sustainable technology for organic waste treatment mainly due to its lower energy consumption and production of residual solids coupled with the prospect of energy recovery from the biogas generated. However, the anaerobic process cannot be seen as providing the ‘complete’ solution as its treated effluents would typically not meet the desired discharge limits in terms of residual carbon, nutrients and pathogens. This has given impetus to subsequent post treatment in order to meet the environmental legislations and protect the receiving water bodies and environment.This book discusses anaerobic treatment from the perspective of organic wastes and wastewaters (municipal and industrial) followed by various post-treatment options for anaerobic effluent polishing and resource recovery. Coverage will also be from the perspective of future trends and thoughts on anaerobic technologies being able to support meeting the increasingly stringent disposal standards. The resource recovery angle is particularly interesting as this can arguably help achieve the circular economy. It is intended the information can be used to identify appropriate solutions for anaerobic effluent treatment and possible alternative approaches to the commonly applied post-treatment techniques. The succeeding discussion is intended to lead on to identification of opportunities for further research and development.This book can be used as a standard reference book and textbook in universities for Master and Doctoral students. The academic community relevant to the subject, namely faculty, researchers, scientists, and practicing engineers, will find the book both informative and as a useful source of successful case studies.ISBN: 9781780409733 (paperback)ISBN: 9781780409740 (eBook)ISBN: 9781780409757 (ePub)
Robust phase unwrapping algorithm based on enhanced denoising and fringe quality improvement routines

Phase unwrapping algorithms have widely been studied and implemented with efforts aimed at unwrapping wrapped phase signals. However, the presence of noise and unreliable fringe quality poses a major obstacle for the retrieval of reliable phase signals. While many techniques have been implemented to deal with the aforementioned issues, most algorithms are application dependent or difficult to implement. Here we present a simple yet effective global phase unwrapping algorithm, that does not resort to Least-Squares Minimization, making use of Fast-Fourier Transform (FFT) based spectral differentiation, Signal Dependent Rank Ordered Mean (SD-ROM) filtering, and Fuzzy Logic Edge Detection (FLED). The proposed algorithm was tested using simulated, noisy, wrapped phaseograms and has shown to improve image and fringe quality, as well as overall retrieved phase reliability.
Why openness makes research infrastructure resilient

Key points Open research infrastructure provides the building blocks of scientific progress, which must be available to everyone, with no barriers to access. Organizations enabling open research infrastructure must endorse these fundamental principles: equity, value, trust, interoperability, sustainability, and community governance. Finding ways to invite co‐creation and community participation engenders a strong sense of ‘buy‐in’ and is therefore essential to developing successful research infrastructure.
Features of the development of project activity skills among students in the educational environment of the university

We reveal the features of the development of project activity skills among students in the educational environment of the university. It is substantiated that the project activity, which is used in the teaching process of students, contributes to the development of knowledge, skills, it is able to form the competencies they need, to become a versatile personality. It is proved that in the educational environment, willingness to learn new knowledge, skills, focus on results, hard work is being developed, creative thinking is developing, and most importantly, high skill and qualifica-tion are acquired. It is concluded that the design process is a kind of transition of the student from one state to another, step-by-step conditions that allow you to achieve great educational, practical results in a shorter period of time. The design process is the most effective and comfortable for students, since this process focuses on the individuality of each person, which, in the process of joint, teamwork, undergoes changes in the student himself, by taking a certain role in the design process. It is proved that the creation in the educational environment of the most favorable condi-tions for the development of students’ skills leads to the successful and effective formation of a holistic, informed personality, which, in turn, is the driving force for the development of society as a whole. The skills that a graduate possesses determine the level of development of the educational environment.
The Relationship and Impact of Communication on Leadership

A manager is the one who is competent to fulfill this by effective leading, sound planning, monitoring, and communicating. It is the manager's communication skills that motivate and inspire teammates to work hard and achieve team target and organizational goals, as well. Furthermore, this paper later explains the idea of leadership communication, steps, and solution, which involved in overcoming the problems. Notably, future leaders must also certainly ensure that the subordinates are treated as members of the organization, whereby consistency in providing sound leadership would determine employee motivation and performance. This paper study also identifies trust in direction, which is related to work outcomes and trust in each other to improve better communication. Therefore, with the help of better communication, one can fulfill their job satisfaction and communication competency.
Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.
Global Convergence of Three-layer Neural Networks in the Mean Field Regime

In the mean field regime, neural networks are appropriately scaled so that as the width tends to infinity, the learning dynamics tends to a nonlinear and nontrivial dynamical limit, known as the mean field limit. This lends a way to study large-width neural networks via analyzing the mean field limit. Recent works have successfully applied such analysis to two-layer networks and provided global convergence guarantees. The extension to multilayer ones however has been a highly challenging puzzle, and little is known about the optimization efficiency in the mean field regime when there are more than two layers. In this work, we prove a global convergence result for unregularized feedforward three-layer networks in the mean field regime. We first develop a rigorous framework to establish the mean field limit of three-layer networks under stochastic gradient descent training. To that end, we propose the idea of a neuronal embedding, which comprises of a fixed probability space that encapsulates neural networks of arbitrary sizes. The identified mean field limit is then used to prove a global convergence guarantee under suitable regularity and convergence mode assumptions, which – unlike previous works on two-layer networks – does not rely critically on convexity. Underlying the result is a universal approximation property, natural of neural networks, which importantly is shown to hold at any finite training time (not necessarily at convergence) via an algebraic topology argument.
Learning-Based vs Model-Free Adaptive Control of a MAV Under Wind Gust

Navigation problems under unknown varying conditions are among the most important and well-studied problems in the control field. Classic model-based adaptive control methods can be applied only when a convenient model of the plant or environment is provided. Recent model-free adaptive control methods aim at removing this dependency by learning the physical characteristics of the plant and/or process directly from sensor feedback. Although there have been prior attempts at improving these techniques, it remains an open question as to whether it is possible to cope with real-world uncertainties in a control system that is fully based on either paradigm. We propose a conceptually simple learning-based approach composed of a full state feedback controller, tuned robustly by a deep reinforcement learning framework based on the Soft Actor-Critic algorithm. We compare it, in realistic simulations, to a model-free controller that uses the same deep reinforcement learning framework for the control of a micro aerial vehicle under wind gust. The results indicate the great potential of learning-based adaptive control methods in modern dynamical systems.
Automatic Text Summarization of COVID-19 Research Articles Using Recurrent Neural Networks and Coreference Resolution

Purpose: Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. Automatic text summarization makes this possible.&#x0D; Materials and Methods: In this study, a recurrent neural network-based extractive summarization is proposed. The extractive method identifies the informative parts of the text. Recurrent neural network is very powerful for analyzing sequences such as text. The proposed method has three phases: sentence encoding, sentence ranking, and summary generation. To improve the performance of the summarization system, a coreference resolution procedure is used. Coreference resolution identifies the mentions in the text that refer to the same entity in the real world. This procedure helps to summarization process by discovering the central subject of the text.&#x0D; Results: The proposed method is evaluated on the COVID-19 research articles extracted from the CORD-19 dataset. The results show that the combination of using recurrent neural network and coreference resolution embedding vectors improves the performance of the summarization system. The Proposed method by achieving the value of ROUGE1-recall 0.53 demonstrates the improvement of summarization performance by using coreference resolution embedding vectors in the RNN-based summarization system.&#x0D; Conclusion: In this study, coreference information is stored in the form of coreference embedding vectors. Jointly use of recurrent neural network and coreference resolution results in an efficient summarization system.
Fine-grained analysis and improved robustness of quantum supremacy for random circuit sampling

We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that estimating the output probabilities of random quantum circuits to within $\exp(-\Omega(m\log m))$ additive error is hard for any classical computer, where $m$ is the number of gates in the quantum computation. More precisely, we show that the above problem is $\#\mathsf{P}$-hard under $\mathsf{BPP}^{\mathsf{NP}}$ reduction. In the recent experiments, the quantum circuit has $n-$qubits and the architecture is a two-dimensional grid of size $\sqrt{n}\times\sqrt{n}$. Indeed for constant depth circuits estimating the output probabilities to within $2^{-\Omega(n\log{n})}$ is hard, and for circuits of depth $\sqrt{n}$, for which the anti-concentration property holds, estimating the output probabilities to within $2^{-\Omega(n^{3/2}\log n)}$ is hard. We prove these results from first principles and do not use the standard techniques such as the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.
Application of Cloud Model and Bayesian Network to Piracy Risk Assessment

Piracy is a major threat to maritime safety. Assessing piracy risk is crucial to ship safety, travel security, and emergency plan preparation. In the absence of a thorough understanding of the factors and mechanisms that influence piracy, no perfect mathematical equation can be set up for such risk assessment. Therefore, the major factors that influence piracy were identified to construct an indicator system for assessment. These factors were analyzed, keeping in view the overall hazard, vulnerability, and antirisk properties, and then the Bayesian network was introduced into the risk assessment model to fuse multiresource information. For some indicators, which have only qualitative information or fragmentary statistical data, the cloud model theory was adopted to realize prior probability settings of the Bayesian network and thus made up for the deficiency in parameter settings. Finally, the inherent hazard of the South China Sea was assessed, as an example for the model, and two real piracy cases were studied to validate the proposed model. The assessment model constructed here can be applied to all cases, similar to the ones studied here.
Exploring EFL student use of digital backchannels during collaborative learning activities

This chapter highlights several findings related to the learners' use of digital communication channels during online collaborative activities. The term "digital backchannel" is used to imply that there are two channels of communication operating simultaneously during collaborative activities. The predominant digital channel is that of the online content management system controlled by the instructor and accessed in the target language, English. The secondary channel of digital communication (backchannel) is that of the external personal social network systems (SNS) that students employed to interact with group members and others. The researcher collected qualitative and quantitative data on learner interactions within a yearlong series of language learning activities through internet logs and interviews. The students employed digital backchannels with increasing frequency throughout the study period, even though there was an initial reluctance to use a communication channel, they considered very private for public educational activities. These digital backchannels were primarily mobile-based SNS. The students reported that the use of these backchannels increased in use over the study period and led to increased peer communication and networking. Also, students' language use changed when moving between these primary and secondary communication channels, with L1 being used for the main-channel and L2 used for the backchannel.
Computer Vision-Based Approach for Indian Sign Language Character Recognition Using CNN and ROI Segmentation

Sign language is popular with hearing-impaired individuals around the globe. There are a pair of languages that employ predefined steps as well as motions to express a personal message. These languages are largely created to help deaf and verbally inhibited individuals. They normally use a precise and simultaneous mixture of motion of hands and wrists, the orientation of hands, hands styles, etc. Various areas have varying indication languages such as American Sign Language, Indian Sign Language. In this paper, we have concentrated on Indian sign words. This particular research work is designed to exhibit a simple strategy toward sign dialect to textual content transformation through personalized region of interest (ROI) segmentation as well as convolutional neural network (CNN). Multiple hint gestures are educated utilizing a customized picture dataset as well as applied by Python dialect. Making use of the ROI choice strategy, the task displays much better results compared to traditional methods within the terminology of real-time detection and accuracy level from video clip streaming by webcam.
A Through Analysis on Protecting Cyber Threats and Attacks on Cps Embedded Subsystems

Cyber- Physical Systems (CPS) consist of a combination of different embedded subsystems, which work independently of each other and also interact with the external environment. Such embedded systems operate in the presence of inherent uncertainty, context dependencies and adversarial certainty arising from both the cyber and physical worlds. Security is one of the key concepts to shield the CPS environment and different embedding devices in order to have a reliable and secure communication platform. There are many security approaches and methods proposed and implemented globally in order to secure CPS, along with areas such as social engineering, security standards, vendor control, as well as access control implementation, etc. However, in addition to these areas, another important concept, namely trust, is significant in ensuring secure and reliable communications in CPS. In the current state-of-the-art, none of the existing approaches discusses the issue of a secure, trust-based CPS. Thus, to address this shortcoming, in this paper, a two-tier blanket approach is proposed consisting of internal and external layers of trust among different entities to create reliable and secure CPS. This trust-based framework improves the confidence of secure entities joining the CPS system and also builds relationships among entities, thereby increasing the security protecting the formed CPS from outside threats and attacks.
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition

Clinical Named Entity Recognition (CNER) aims to automatically identity clinical terminologies in Electronic Health Records (EHRs), which is a fundamental and crucial step for clinical research. To train a high-performance model for CNER, it usually requires a large number of EHRs with high-quality labels. However, labeling EHRs, especially Chinese EHRs, is time-consuming and expensive. One effective solution to this issue is active learning, where a model asks labelers to annotate data which is beneficial to model performance improvement. Conventional active learning assumes a single labeler that always replies noiseless answers to queried labels. However, in real settings, multiple labelers provide diverse quality of annotation with varied cost and labelers with low overall annotation quality can still assign correct labels for some specific instances. In this paper, we propose a Cost-Quality Adaptive Active Learning (CQAAL) approach for CNER in Chinese EHRs, which maintains a balance between the annotation quality, labeling cost and the informativeness of selected instances. Specifically, our proposed CQAAL method selects cost-effective instance-labeler pairs to achieve better annotation quality with lower cost in an adaptive manner. Computational results on the CCKS2017 dataset demonstrate the superiority and effectiveness of CQAAL.
Cross-Collaboration Processes based on Blockchain and IoT: a survey

Cross-collaboration processes are decentralized by nature and their centralized monitoring can trigger mistrust.Nevertheless, a decentralized monitoring facility such as a blockchain-based and Internet-of-Things-aware (IoT-aware) business process management system can reduce this pitfall.However, concerns related to usability, privacy, and performance, hamper the wide adoption of these systems.To better understand the challenges at stake, this paper reviews the use of blockchain and IoT devices in cross-collaboration processes.This survey sheds some light on standard uses such as model engineering or permissioned blockchains which help adopt cross-collaboration business process management systems.Moreover, with respect to process design, two schools of thought coexist, addressing both constrained and loosely processes.Furthermore, a focus on data-centric processes appears to get some momentum, as many industries go digital.Finally, this survey underlines the need to orient future research towards a more flexible, scalable, and data-aware blockchain-based business process management system.
Combining Gesture and Voice Control for Mid-air Manipulation of CAD Models in VR Environments

Modeling 3D objects in domains like Computer Aided Design (CAD) is time-consuming and comes with a steep learning curve needed to master the design process as well as tool complexities. In order to simplify the modeling process, we designed and implemented a prototypical system that leverages the strengths of Virtual Reality (VR) hand gesture recognition in combination with the expressiveness of a voice-based interface for the task of 3D modeling. Furthermore, we use the Constructive Solid Geometry (CSG) tree representation for 3D models within the VR environment to let the user manipulate objects from the ground up, giving an intuitive understanding of how the underlying basic shapes connect. The system uses standard mid-air 3D object manipulation techniques and adds a set of voice commands to help mitigate the deficiencies of current hand gesture recognition techniques. A user study was conducted to evaluate the proposed prototype. The combination of our hybrid input paradigm shows to be a promising step towards easier to use CAD modeling.
Universal lasing condition

Usually, the cavity is considered an intrinsic part of laser design to enable coherent emission. For different types of cavities, it is assumed that the light coherence is achieved by different ways. We show that regardless of the type of cavity, the lasing condition is universal and is determined by the ratio of the width of the atomic spectrum to the product of the number of atoms and the spontaneous radiation rate in the laser structure. We demonstrate that cavity does not play a crucial role in lasing since it merely decreases the threshold by increasing the photon emission rate thanks to the Purcell effect. A threshold reduction can be achieved in a cavity-free structure by tuning the local density of states of the electromagnetic field. This paves the way for the design of laser devices based on cavity-free systems.