Reviewerly/exHarmony huggingface dataset:
Intelligent Interaction of Fluorine containing materials Based on NL2SQL

The data on the production and development of fluorine containing materials are characterized by a large amount of data and a high degree of dimensionality of physical and chemical property characterization indicators. The manual way of analyzing the data item by item not only has high interaction cost, but also is difficult to analyze and explore the data intuitively. In order to efficiently utilize the data, this paper firstly constructs a dataset of fluorine-containing materials and proposes the Mengzi-ITPT model based on it, which takes Mengzi as the encoder and uses the attention mechanism to enhance the representation of the listed information. Meanwhile, for the data characteristics of fluorine containing materials, the training strategy of ITPT is adopted to improve the accuracy of the model. The experimental results show that the accuracy of the Mengzi-ITPT model query reaches 86.9% when the model is trained under the fluorine-containing material dataset.
The intelligent decision model for determine the best path of transportation on smart city using random forest algorithm and bayesian optimization (RF-BO)

This study investigates various approaches and algorithms in the context of object detection and best path determination for managing vehicular traffic in an urban environment, particularly in Palembang city. This research is a step towards the development of smart city concept. In the object detection analysis, we applied the YOLOv3 method on video footage to identify vehicles, resulting in mAP accuracy rates between 72.72% to 79.35% for both motorcycle and car categories. The total detection accuracy of the model reached 76.03%. Next, we adopted the Random Forest algorithm to classify traffic conditions into three classes: smooth, moderate, and congested. After optimizing the algorithm with Bayesian Optimization, the model accuracy increased from 89% to 92%, while the classification accuracy increased from 91.66% to 92.36%. Results from the application of the A* Heuristic Search algorithm revealed that lane 5 (from SMK PGRI 1 Palembang to Bom Baru Jl Perintis Kemerdekaan Arah Charitas (STMIK MBC)) was selected most frequently in 9 out of 12time trials. The selection of this route was based on an evaluation of traffic levels that tended to be "smooth" and the shortest travel distance compared to other alternative routes. The decision in choosing the optimal path also considers the road width factor, where wider roads have the potential to reduce traffic density and the risk of congestion.
Transformation into anti-manipulation method in voting. Changes in properties

Abstract This paper examines the properties of the anti-manipulation method in voting. Such a method can be used by committees and similar bodies to ensure that votes reflect genuine preferences. The anti-manipulation method is based on the Borda Count and discourages strategic voting by excluding scores that deviate excessively from the mean. The method does not eliminate strategic voting but diminishes the motivation to apply it. We compare the properties of the Borda Count and the anti-manipulation method. The properties, which are most often found in the literature, were chosen for comparison. Thus, the following properties are examined: consistency, vulnerability to the no-show paradox, vulnerability to the subset choice condition, homogeneity, monotonicity, and vulnerability to the reversal bias paradox as well as the Condorcet winner and loser paradoxes. The anti-manipulation method fails to satisfy most of these properties. A real data example, the voting of a certain jury, is used as a counterexample in most cases.
Formulaic language and translation : The case of the Hungarian dirge

The study proposes that “good clichés”, often named formulas or formulaic language in linguistics, can be best represented in another language as folklore translations. The claim is demonstrated through providing folklore translations to formulaic segments of Hungarian dirges, a genre which is characterized by formulaic composition. The dirge is an improvised folklore genre, a special lament for the deceased that is composed on the spot. First the formulaic composition of dirges is explained. In addition, various levels of mediation are compared in order to highlight the importance of folklore translations in formulaic texts. It is suggested that folklore translations occupy an intermediate position between interlinear morpheme-by-morpheme glosses and regular translations: they differ from interlinear glosses in their readability, and from regular translations mainly in their cultural content. Finally, closely resembling formulaic chains in the discourse of Hungarian dirges from the Upper-Tisza region are presented in both Hungarian and English, and thus made accessible for readers in English. The Appendix of the study contains six dirge texts in Hungarian and their translations in English. The dirges had been either recorded during field work and then transcribed in Hungarian by the author or collected from archives and published sources. In this manner, formulaic composition in Hungarian dirge texts, together with texts not translated before, are made available to an international readership in English.
Chapter 9: Workflow

Workflow refers to everything involved in producing a document except the actual writing and research. It's about how to edit and typeset a document, store and access your bibliographic references, carry out reproducible numerical experiments, produce figures, back up your files, collaborate with others, and so on. These tasks all need to be done multiple times, so small gains in efficiency, using automation where appropriate, can have a big payoff in the long run.
Topic: Scoping review of Blockchain based data storage technique in industrial IoT data management

The review study was based on the integrated network and the secured protocols and blockchain technology to detect security issues and protect the user’s data storage. The study aims to review the recently published research articles that state the data storage techniques and blockchain technology for the IIoT to manage data confidentiality. The method opted here was the literature review for the collections and knowledge of the data management. Moreover, a secondary research method was selected in which the currently published research article was gathering to review the literature. The research paper's expected finding was that data storage and data management are the two crucial factors that had been used within the industry for allocation and work management. It had helped in enhancing industrial performance. The research work's contribution is in data collection from different libraries based on data storage techniques. This work plays a huge contributory role in investigating the currently available solutions dependent on blockchain technology and data storage technique for managing the data effectively and delivering valuable insights for data storage capabilities. The review study included different factors and attributes classified by considering different instances as per the author’s interest. The major component was also evaluated in this study through a different segment, including error rate and accuracy matrix that shows the system validation. The study verifies the system by considering different terms and their related frequency in the last segment, which justifies system accuracy.
Technique, Notation

Taking an idea and notating it so that a human can play/sing it is a skill that must be developed. Computer music notation is a mixed blessing in this area. Computers can play any rhythm that you write, but that doesn’t mean humans can. Music students are also likely to study counterpoint at some point in their studies. The goal is not to limit or restrict composers, but to expose them to common practices that are pleasing to the ear. Creating an original melody or chord progression isn’t as simple as playing a series of notes and hoping it works. Conventions help guide melodic and harmonic ideas, so they make sense and flow naturally. A deep understanding of theory, counterpoint, instrumentation, orchestration, etc. will prove indispensable for the developing composer.
Deep Learning-Based Violence Detection from Videos

Violence has been one of the major concerns among human interactions. Violent activities turn out to be worse in public places like parks, halls, stadiums, and many more. The presence of efficient detection algorithms is the need of the hour as unusual events such as fights have been comparatively studied less. The existing system requires manual monitoring of videos from surveillance cameras. Recognition of violent interactions is important to develop automated video monitoring systems. Approaches based on deep Learning promise better results in recognition of images and human actions. Our proposed deep learning-based model uses CNN and LSTM based on DarkNet19 architecture as the pretrained model. The proposed model achieves 95 and 100% accuracy on the benchmark hockey and Peliculas datasets. Unlike most previous works which involve the use of single-frame models (models which do not include temporal features), our model is able to learn temporal features as well as spatial features. The two datasets, namely Hockey Fight and Movie datasets, contain motions of sudden camera recordings and are challenging datasets to work on. It is also observed that different variations in optical flow were taken into consideration for the work in hand. However, the existing system was unable to utilize an efficient pretrained model for the task of violence detection. The proposed approach puts forward a more efficient pretrained model architecture which is less studied, but is very effective than traditional methods.
Implementation of National Standards of Educational Facilities and Infrastructure

The implementation of the administration of equipment and supplies is already a routine job and people are faced with less significant difficulties, but to improve the work the experts suggest some guidelines for the implementation of the administration, the principal as an administrator should not be too busy himself directly with the affairs of the administration of equipment and supplies. teaching equipment, carrying out proper recording so that it is easy to do, administration of teaching equipment and equipment must always be reviewed from a service perspective to help facilitate the implementation of teaching programs The above conditions will be met if the administrator includes all teachers in planning the selection, distribution and use and supervision of teaching equipment and supplies.
Questions and answers

What are the ways to extract iodine from cotton wool soaked in iodine tincture, and is such extraction and further use of crystalline iodine advisable? Subscriber number 622.
Dirty Metadata: Understanding A Threat to Online Privacy

People have a certain expectation of privacy when using widespread and trusted online services. This study surveys several popular web applications to understand if uploaded user images are handled securely using open source digital forensics tools and a custom software framework.
On strong motion monitoring and recordings in Iceland

This paper describes a strong motion network run by the Engineering Research Institute of the University of Iceland. The objectives of the system are stated, and the geophysical and seismological background is explained. The network comprises ground response stations as well as structural response systems in power plants, dams, bridges and buildings. Recordings made by the network are outlined, the processing procedure discussed and the data interpreted. A short presentation of other earthquake monitoring systems in Iceland is also included.
Flower Pollination Algorithm for Convolutional Neural Network Training in Vibration Classification

A convolutional neural network (CNN) is among the branches in deep learning (DL) which is a new field of research in machine learning. This model artificially mimics the visual cortex to learn the representation of visuals from low to high levels of abstraction and representation to mining data patterns such as image, sound, text, and signal. Despite the proven CNN advantages in various applications, training this model is challenging especially in complex scenarios. Some methods to optimize CNN training have been proposed, such as stochastic and meta-heuristic algorithms. In this paper, we propose a flower pollination algorithm (FPA) to train CNN. A CWRU bearing dataset is used to ensure the accuracy and efficiency of the proposed method. Moreover, we also compare our proposed method with the original CNN. The results show that the proposed method needs to be refined to achieve the required performance of CNN.
A Revised Psychoanalytic Model of Mind and Communication

Born at the bedside of Anna O, whose plethora of symptoms had no physiological basis, the “talking cure” unfolded, uncovering that “hysterics suffer mainly from reminiscences” (1895, 7) – a product of the mind. Freud’s scientific method, Psycho-Analysis, eschewed the body-mind dilemma from the beginning, bursting in on the scene in his explosive “The Interpretation of Dreams” (1900). For Freud, the deep unconscious is biological; like Piaget after him, Freud’s mind begins in the body, the first ego, a body-ego. After taking the common dream as legitimate object of scientific scrutiny Freud produced a masterwork of observation in which he arrives at his first topographical theory of mind, the cornerstone of early psychoanalytic metatheory. Chapters 6 and 7 present a detailed analysis of the two-tiered structure, motive force, the formal properties and compositional grammar, of a ‘Primary Process’ vocabulary of the deepest unconscious, linguistic dream interpretation a bridge between Ucs. and Cs. Freud believed his theory of dreams held promise for the study of mental evolution. Yet Freud’s great edifice of observation and theoretical foundations lies on the bedrock of residues from his neurological days in a paradigm borrowed for physics, couched in metaphors of repressive forces and shifting forms of energy, a dynamic metatheory of mind in search of real functional processes as explanatory base. To the end Freud lamented that the Weltanschauung of his day could not provide concepts to answer how the unconscious becomes conscious. An overview of Freud’s early core concepts is followed by the author’s revision of his topographical model into a seamless developmental bio-Semiotic theory of mind, later expanded into a study of human communication. The epigenetic model of mind in inter-action (Aragno, 1997/2016, 2018/2016) illustrates how language and dialogues retain deep roots at less-differentiated interpersonal levels, regardless of how abstractly a semiotic system is used, generating bi-directional semantic fields. Specifically, the model uncovers a psychoanalytic epistemology articulated through different semiotic forms. The model generates a shift towards an inter-penetrative paradigm rooted in functional organizations governing experience, knowledge, and communication, illustrating the semiotic advantage unique to our species as paramount in our adaptation.
Confronting the Digital Leviathan in Education

New data-intensive “smart” technologies are now being broadly promoted and adopted in education, especially since the pandemic that began in 2020. This includes the related technologies of artificial intelligence (including machine learning), the Internet of Things (IoT) and blockchain. This furry of adoption has worked against a careful evaluation of the application of these technologies in education. Drawing on a range of critical theorists of technology and education, the first part of this chapter presents a framework for evaluating new digital technologies. Technology, it is argued, needs to be understood as a foundational human activity—it embodies and shapes the values and ideologies of a culture. Critical appraisal of any technology thus requires study of the interrelationship between that technology, and its context of development and subsequent use. Most importantly for this discussion is how technologies serve as normative analogies for social formations. For the technologies reviewed here, cybernetic theses serve as an analogy for educational theory and practice. From this presentation, the values and ideologies embodied in these technologies are examined, and alternatives are considered.
Augmented Reality Mobile Tool for Engineering Education

Abstract This paper falls under evidence-based practice analyzing the effectiveness and usefulness of Augmented Reality (AR) in engineering education practices via a mobile device and includes the design and implementation rationale of the AR-based mobile tool and its assessment through a survey. Researchers have claimed that traditional education needs to be supplemented with innovative tools and suggested that AR-based education enhanced learning. AR can be particularly helpful in learning about the structures of complex models. The use of complex physical equipment/replicas poses a challenge in teaching due to their cost, limited quantity, accessibility, and safety issues. Therefore, AR app-based learning using easily accessible smartphones in the digital world today can be a better choice for smart and effective learning. Moreover, it will have more significance with increasing online studies in the current pandemic scenario. In this paper, an AR mobile tool prototype developed for engineering education is discussed. The app includes limited opensource 3D models, interaction abilities, teaching content, and quiz feature and was evaluated with a survey. This paper will introduce the project, present an overview, design framework, implementation process, testing, and survey results. The survey results show that the app is effective and useful in learning.
Node Embedding based on the Free Energy Distance

We propose a novel unsupervised node embedding method leveraging the free energy distance. More precisely, we build a matrix to encode pairwise node similarities based on the free energy distance, which interpolates between the shortest path distance and the commute time distance, thus providing an additional degree of flexibility. Moreover, we propose a method to factorize the constructed similarity matrix by generalizing the loss function of the skip-gram model with negative sampling to arbitrary similarity matrices. The proposed matrix factorization method can be easily implemented using advanced automatic differentiation toolkits and computed efficiently by leveraging GPU resources. Numerical experiments on four real-world datasets demonstrate that the proposed embedding method outperforms five state-of-the-art alternatives in tasks of node classification and link prediction.
Divisive Clustering of Histogram Data

This chapter presents a divisive top-down clustering method designed for histogram-valued data. The method provides a hierarchy on a set of units together with a characterization of each cluster in the form of a conjunction of properties on the descriptive variables, which are necessary and sufficient conditions for cluster belonging (monothetic clustering). At each step, a cluster is chosen to be split, to minimize intra-cluster dispersion, which is measured by the sum of squared Mallows distances between pairs of members of each cluster. The criterion is minimized across the bipartitions induced by a set of binary questions. Since interval-valued variables constitute a special case of histogram-valued variables, the method applies to data described by either kind of variables. An application illustrates the proposed approach.
Collaborative Ant Colony Multi-agent Planning System for Autonomous Mobile Robots in a Static Environment

Most path planning approaches (global, reactive, or hybrid) existing in literature don’t consider the transfer of experience concept between agents operating in the same environment. A mobile robot executes any planning method to reach its goal, at the time of navigation, it explores the environment, detects the location of obstacles, makes its calculations to choose the best decisions. All this experience gained by the robot will be wasted, and if a new robot has just joined the environment, it has to repeat the entire planning process without benefiting from past experiences. Our contribution consists in: the use of a Holonic Multi-Agent system for modeling the two abstraction levels of the proposed system, an enhanced version of Ant Colony Optimization is used, where the ants are considered as autonomous and heterogeneous robots’ agents. Each ant in the system executes a distinct planning method to reach its goal, on its return path, it puts some pheromones in its experience memory table, which will be transferred to the master agent which incarnates the collective experiences of all agents that have passed through the platform. The simulation and comparison results have confirmed that using the proposed system, the execution time is significantly decreased and the robot safely achieves its goal.
Mixture Models for Distributional Data

A random distribution is a random variable taking values in a space of probability measures. We consider here distributional data as outcomes of a function of a random distribution so that interval data and histogram data are particular cases of distributional data. We are interested in the probability distribution of such data in the form of mixture models, that is convex combinations of well-known distributions such as Dirichlet Distributions, Dirichlet Process and Dependent Dirichlet Process. EM algorithm for estimating a mixture of Dirichlet Distributions is fully detailed while npEM algorithm and MCM-based algorithms can be used in the other cases.
2-norm Flow Diffusion in Near-Linear Time

Diffusion is a fundamental graph procedure and has been a basic building block in a wide range of theoretical and empirical applications such as graph partitioning and semi-supervised learning on graphs. In this paper, we study computationally efficient diffusion primitives beyond random walk. We design an near-linear time randomized algorithm for the 2-norm flow diffusion problem, a recently proposed diffusion model based on network flow with demonstrated graph clustering related applications both in theory and in practice. Examples include finding locally-biased low conductance cuts. Using a known connection between the optimal dual solution of the flow diffusion problem and the local cut structure, our algorithm gives an alternative approach for finding such cuts in nearly linear time. From a technical point of view, our algorithm contributes a novel way of dealing with inequality constraints in graph optimization problems. It adapts the high-level algorithmic framework of nearly linear time Laplacian system solvers, but requires several new tools: vertex elimination under constraints, a new family of graph ultra-sparsifiers, and ac-celerated proximal gradient methods with inexact proximal mapping computation. See https://arxiv.org/abs/2105.14629 for the full version of this paper.