Reviewerly/exHarmony huggingface dataset:
A Hidden Danger to Our Children’s Classrooms within Educational Leadership & Peering Practices

This comprehensive body of research is an empirical attempt to purposely examine if our offspring: children, teenagers to adulthood, [Treasures] are, being subjected to political interference [indoctrination] within their respected theatres of education, and therefore, be a negative impact for their respective future endeavours, emanating from the West and quickly heading into cultures of the Eastern regions demographically. Besides, the research has included how it has impacted the current stasisin the Western educational system? The sample consists of the history involved, current statistics from global sourcing methodology, biases notated on within the political equation, and how has this has affected business. Both quantitative and qualitative analysis models are illustrated within extensivity. Two new philosophies are being introduced, named “Miller’s Social Cultivation Paradox SCP and the second being Eight Dominions to Dystopianism. The data therein would confirm research findings within empirical validity via standard error mean for difference projections within the subject matter’s hypothesis, thus, subsequently determine data mining findings. The reader ought to discern, then consider from this paper, if our treasures are in danger within their learning domains, or is this current pedagogical practice in kindergartens, K-12 schools, and higher institutions of learning, are indeed acceptable to both academics and parents alike, for the years to come? Wherefore, within the subject hypothesis – have teaching methodologies changed for the worst or the better? The premise of this investigation and resulting study, has attempted to be simplified and unambiguous, and to expose a 21st century’s western education ideological system, and heretofore emanating from the early years’ classrooms, percolating through both high schools to higher educational institutions of note. Moreover, the exposition of the false unproven narratives on global warming, being enforced onto our children via their educators’ classroom practice, devoid of empirical facts, inclusive of how public leadership and policy, have been both physically and emotionally endangers our school-aged children. Thus, culminating in being shown that a politically culturally biased teaching method over decades, has indeed infiltrated student theatres of learning, resulting in alumni [treasures] eventual disaffection with their own respective country, family values, inclusive of law and order. This body of research would illustrate the possible future outcomes and advisories of vigilance, for both parents and researchers concerned. Keywords: Education, Globalization, Society, Methodology, Public Policy, Materials, Faculty, Race, Ideology, Parent, Treasures, (Children) Left-Leaning, Global Warming and Misinformation..
SELF-COLLISION AVOIDANCE OF ARM ROBOT USING GENERATIVE ADVERSARIAL NETWORK AND PARTICLES SWARM OPTIMIZATION (GAN-PSO)

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of 3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator always gets the same output point which mapped to different input values. The discriminator produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis). The PSO was successfully reduced the number of training epochs of the generator only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE).
Continuous-time dynamics and error scaling of noisy highly entangling quantum circuits

We investigate the continuous-time dynamics of highly entangling intermediate-scale quantum circuits in the presence of dissipation and decoherence. By compressing the Hilbert space to a time-dependent ``corner'' subspace that supports faithful representations of the density matrix, we simulate a noisy quantum Fourier transform processor with up to 21 qubits. Our method is efficient to compute with a controllable accuracy the time evolution of intermediate-scale open quantum systems with moderate entropy, while taking into account microscopic dissipative processes rather than relying on digital error models. The circuit size reached in our simulations allows to extract the scaling behavior of error propagation with the dissipation rates and the number of qubits. Moreover, we show that depending on the dissipative mechanisms at play, the choice of the input state has a strong impact on the performance of the quantum algorithm.
The Ontologies Community of Practice: An Initiative by the CGIAR Platform for Big Data in Agriculture

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labelling to be interoperable. As recommended by the FAIR principles, data, labels and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no agreed full set of ontologies recommended for data annotation across agricultural research disciplines, which may span genetics, environment, agroecology, biology and socioeconomics. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant ontology expertise. This CoP aims to stimulate knowledge sharing and directly support platform development teams by producing ontologies or contributing missing concepts, recommending best practices and identifying mitigation solutions when gold standard datasets are difficult to attain.
Hyperparameter tuning of AdaBoost algorithm for social spammer identification

Purpose The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters. Design/methodology/approach In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm. Findings The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency. Originality/value The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.
The four qubits deletion code is the first quantum insertion code

The classical insertion codes were discovered at the same time as the classical deletion codes. The quantum deletion code was presented at IEICE Communications Express last year, but quantum insertion codes were never constructed. In this article, we provide the first quantum insertion code. Its encoding is the same as the four-qubits deletion code, known as the theoretically shortest deletion code.
A New Artificial Intelligence-Based Strategy for Digital Marketing Reinforcement

The success of any economy strategy is relying on the well understanding of the flow of data generated during transactions, consultations, and feedback on multiple levels, and using different tools and techniques to process and analyze this data and helps in decision making to either improve productivity and provide a customer’s service responses that fit their expectation or create a personalized profile to target a specific range of customers for specific products or services. In recent years Artificial Intelligence as an emergent technology has become a key driver in the success of many domains such as healthcare, security, industry and robotic, big data, and modern economy and digital marketing. Artificial Intelligence has proven its efficiency in data analysis and decision making. In this paper we present an overview of Artificial Intelligence and its applications in the modern economy and Digital marketing, based on that we propose a novel Artificial Intelligence-based strategy to improve success in digital marketing, we divide our strategy into multiple phases: Data acquisition, Data classification and processing, AI engine (presented on multiple levels: Detection level which is Anomaly detection in collected and treated data to sort and eliminate anomalous point in data that could lead to false decisions, Prediction level that relies on implementing techniques for prediction of customer behavior, pricing and Reporting level in which the engine generate graphs and report that helps in making a decision in the next phase) and the final phase making decision that includes a human factor since AI in our approach is only a tool based on multiple machine learning technique and not a replacement for the human intelligence.
Human factors affecting information security in libraries

Purpose Information security is a critical issue in all organizations. The success of information security in libraries depends, to a large extent, on the effective behavior of administrators, librarians, users and all human staff. Accordingly, this study aims to design a model for identifying human factors affecting information security in libraries. Design/methodology/approach This study is applied in terms of research objectives and is a survey in terms of data collection. Moreover, it goes under the rubric of structural equation modeling in terms of the relationship between variables. The statistical population consisted of 100 managers and librarians of academic and public libraries of Hamadan in Iran. A questionnaire was used for data collection. The face and content validity of the questionnaire were examined using the expert’s opinions in the field of Iranian libraries. Also, the reliability of the questionnaire was calculated through Cronbach’s alpha coefficient. Data were analyzed using SPSS 16 and Smart PLS 2. Findings The results showed that among the components of information security, the highest score was designated to self-esteem (4.11 ± 0.57) and level of skill (4.07 ± 0.59), whereas the lowest score belonged to the level of education (3.51 ± 0.74). Ranking human factors affecting information security showed that experience with Rank 1 had the most impact, whereas the level of skill with Rank 6 had the least impact on information security. Originality/value In this study, for the first time, a model was designed and tested for human factors affecting information security in libraries. Information security professionals, librarians and library and information science researchers can exploit this model in the future.
Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence

Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations using a forward object decoder. Then, it finds 1-to-n subject-object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE-2005 corpus and an F1-score of 78.3% for the NYT corpus.
SemanticAxis: exploring multi-attribute data by semantic construction and ranking analysis

Mining the distribution of features and sorting items by combined attributes are 2 common tasks in exploring and understanding multi-attribute (or multivariate) data. Up to now, few have pointed out the possibility of merging these 2 tasks into a united exploration context and the potential benefits of doing so. In this paper, we present SemanticAxis, a technique that achieves this goal by enabling analysts to build a semantic vector in two-dimensional space interactively. Essentially, the semantic vector is a linear combination of the original attributes. It can be used to represent and explain abstract concepts implied in local (outliers, clusters) or global (general pattern) features of reduced space, as well as serving as a ranking metric for its defined concepts. In order to validate the significance of combining the above 2 tasks in multi-attribute data analysis, we design and implement a visual analysis system, in which several interactive components cooperate with SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We prove the effectiveness of our system and the SemanticAxis technique via 2 practical cases.
The centered ternary balance scheme: A technique to visualize surfaces of unbalanced three-part compositions

The ternary balance scheme is a visualization technique that encodes three-part compositions as a mixture of three primary colors. The technique works best if the compositional data are well spread out across the domain but fails to show structure in very
IMA Commission on New Minerals, Nomenclature and Classification (CNMNC) NEWSLETTER 49

Tetragonal: P4 2
The Spirit of Node Replication

Abstract We define and study a term calculus implementing higher-order node replication. It is used to specify two different (weak) evaluation strategies: call-by-name and fully lazy call-by-need, that are shown to be observationally equivalent by using type theoretical technical tools.
Perspective Analysis of Anti-aging Products Using Voting-Based Ensemble Technique

The customer review is always beneficial for a company for making its product at the next level. There are thousands of products and their reviews which are impossible to analysis manually. Perspective analysis or sentiment analysis is an automated process which retrieves subjective opinion from the review and categorized as positive and negative. Through which an organization can apprehend that where their invention goes on. Living life with uncomfortable looks has a negative impact on our daily life. So, day by day increasing the use of anti-aging products is appreciable. Major portion of such data are available on Web sites like twitter, Facebook, Linked-In and also from e-commerce sites like Amazon, Flip-kart, and various types of blogs. Hence, this research paper aims to guide a sentimental analysis approach to customer reviews of anti-aging products from Amazon. Classify each review as an affirmative review; otherwise, neglect review by adopting several machine learning strategies. This research builds a model using supervised and ensemble machine learning approaches. The method of ensemble machine learning approach applied in this analysis is voting which altogether seven different classifiers: Gaussian Naive Bayes, logistic regression, random forest, bagging using decision tree algorithm, and boosting using extreme gradient boosting (XGB), SVM, k-nearest neighbor algorithm and give output according to best accuracy. The data have also been analyzed using text blob and VADER and accuracy score also compares with this model. The experiment in this research done using Python and NLTK and have created different scenarios for the evaluation of proposed methodology against the classifiers. The scenarios in feature extraction steps such as by using different types of N-grams and analysis the accuracy for each and every classifier. The result which achieved the highest accuracy in logistic regression for using unigram but for some scenario, voting classifier also gives an optimum output.
Representing Archeological Excavations Using the CIDOC CRM Based Conceptual Models

This paper uses CIDOC CRM and CRM-based models (CRMarchaeo, CRMsci) to represent archaeological excavation activities and the observations of archaeologists during their work in the excavation field. These observations are usually recorded in documents such as context sheets. As an application of our approach (case study), we used the records of the recent archaeological excavations in Fuwairit in Qatar, part of the Origins of Doha and Qatar Project. We explore issues related to the application of classes and properties as they appear in the latest versions of the aforementioned models, i.e. CIDOC CRM, CRMarchaeo, and CRMsci. The proposed data model could be used as the basis to create an automated system for archaeological documentation and archeological data integration.
Symbolic-regression boosting

Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: symbolic-regression boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages—between 2 and 5—to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.
Research on the impact of artificial intelligence on the business model of an organization

The digital transformation of our world and the inevitable interaction between people, digital technologies and physical assets create a rapidly changing and complex environment that requires organizations to be more flexible, better fit and ready to accept new ways of working. Businesses are coming to realize the need for change to operate successfully in the digital age. In the period of global digitalization, information and communication technologies are one of the most important aspects of existence for a business, which makes it more efficient, efficient and allows you to respond quickly to a rapidly changing external environment, as well as customer needs. At the moment, there is a high interest in the possibilities of artificial intelligence for use in business tasks in the world, as there are already examples of successful implementation, when Artificial Intelligence and machine learning radically change the way they work and increase the profit of organizations in different countries. The purpose of this study is to consider how artificial intelligence affects the value proposition and how the elements of the business model change when using this technology. The paper gives the existing examples of the use of technology, the consequences of its application and the emerging prospects for the use of Artificial Intelligence as one of the advanced technologies of digital transformation.
Features of Teacher’s Synchretic Activity in the Conditions of Modern Higher Education

Effective feedback is an art that every leading teacher must improve. The student, who receives excellent feedback during the lesson, walks out of the door feeling confident and motivated for further improving. A student who receives poor feedback will feel the opposite. The feedback should be defined like "great work" with the meaning not only about how to correct the mistakes of students so that they can study in the future without undermining their confidence but also about the modern teacher’s role in educational process. The article has been raised the issue of the specific features of the teaching activities, the essence of the concept of "syncretic activity of the teacher "is reveald, in particular the concept of "role functions" is clarified, approaches to interpreting the features of the role functions of a teacher are considered.
Generative adversarial network guided mutual learning based synchronization of cluster of neural networks

Abstract Neural synchronization is a technique for establishing the cryptographic key exchange protocol over a public channel. Two neural networks receive common inputs and exchange their outputs. In some steps, it leads to full synchronization by setting the discrete weights according to the specific rule of learning. This synchronized weight is used as a common secret session key. But there are seldom research is done to investigate the synchronization of a cluster of neural networks. In this paper, a Generative Adversarial Network (GAN)-based synchronization of a cluster of neural networks with three hidden layers is proposed for the development of the public-key exchange protocol. This paper highlights a variety of interesting improvements to traditional GAN architecture. Here GAN is used for Pseudo-Random Number Generators (PRNG) for neural synchronization. Each neural network is considered as a node of a binary tree framework. When both i -th and j -th nodes of the binary tree are synchronized then one of these two nodes is elected as a leader. Now, this leader node will synchronize with the leader of the other branch. After completion of this process synchronized weight becomes the session key for the whole cluster. This proposed technique has several advantages like (1) There is no need to synchronize one neural network to every other in the cluster instead of that entire cluster can be able to share the same secret key by synchronizing between the elected leader nodes with only logarithmic synchronization steps. (2) This proposed technology provides GAN-based PRNG which is very sensitive to the initial seed value. (3) Three hidden layers leads to the complex internal architecture of the Tree Parity Machine (TPM). So, it will be difficult for the attacker to guess the internal architecture. (4) An increase in the weight range of the neural network increases the complexity of a successful attack exponentially but the effort to build the neural key decreases over the polynomial time. (5) The proposed technique also offers synchronization and authentication steps in parallel. It is difficult for the attacker to distinguish between synchronization and authentication steps. This proposed technique has been passed through different parametric tests. Simulations of the process show effectiveness in terms of cited results in the paper.