Reviewerly/exHarmony huggingface dataset:
The Ripple Effect

In this book I have been a strong advocate of the PCI DSS integrated data security controls framework, its strong heritage built from other industry security controls and its effectiveness to defend your business against your ever-present threats. I am still amazed to hear of those payment card security businesses who
A Study on Students' Cognitive Development in Answering English Task

This study focuses on students’ cognitive development in answering English tasks based on Piagetian theory. The design of this study is descriptive quantitative with a pilot study to ensure that the tasks are reliable and valid. The subject of this study were 26 students at the third grade of MIN 3 Pekanbaru. While the object of this study is the students’ cognitive development. Data collection techniques used in this study are tests and documentation. Data analysis techniques used are editing, coding and tabulating. After conducting the study, the authors found conclusions on the students’ cognitive development at the third grade of MIN 3 Pekanbaruare in various levels, namely seriation (57.69%), transitivity (43.59%), composition (65.38%), decentering ( 50.00%), reversibility (52.56%), elimination (61.54%) and egocentrim elimination (24.36%). After calculation, the average of students’ cognitive development is in the medicore level (50.73%), whereas their agesare in the middle of the concrete operation stage, that is between 8 and 9 years old.&#x0D;
JACOBSON’UN ÖNERİLERİ İLE NESNEYE YÖNELİK UYGULAMA GELİŞTİRMENİN TÜM YAŞAM DÖNGÜSÜ

Jacobson suggested OOSE to implement for full life cycle software engineering. Aim of this paper; to introduce Jacobson’s m etli od which is called OOSE (Object Oriented Software Engineering).
Composing with Sound-Objects: A Methodology

Author(s): Wheeler, George Stockton | Advisor(s): Dobrian, Christopher | Abstract: Technology presents us with the ability to record and manipulate the entire universe of sound in a musical composition. As a result, composers are faced with an overwhelming—often paralyzing—amount of available musical options. My methodology focuses on how a sound-object informs the organization, collection, manipulation, and culmination of a work of electronic music. I believe that breaking down the number of choices to manageable bite-sized portions helps minimize ambiguity, and imposing limits on musical parameters helps the composer focus on productive musical options. This is a methodology where the sound-object holds primacy over the work and serves as the motivic touchstone from which to make all compositional decisions.Part one of the dissertation provides a definition of a sound-object and an historical overview. Part two is my methodology, which is divided into three working stages: onset, continuant, and termination. The onset stage discusses a compositional approach to organizing a piece of music based on the sound-object as motivic touchstone; it introduces the organizational process according to functional considerations as well as conceptual approaches. The continuant stage is the composer’s playground where sound is transformed. It includes the technical and practical approaches used to assess the many parameters of a sound-object, as well as how the object itself informs the transformations. Additionally, the continuant stage represents an approach to composition and improvisation—informed by the sound-object—that uses acoustic instruments. Finally, the termination stage brings all these elements together in order to finish the piece. This stage explores how the sound-object can inform the structure of the piece at the subsequent levels of event, phrase, section, and overall form. I will demonstrate this methodology by explaining how I composed five original pieces with sound-objects—Acoustic Memories (2019), Modular Voices (2019), Interconnected (2019), Synthetic Objects (2019), and Gucci Concrete (2019). Through the framework presented in this methodology, composers of electronic music will better understand this flexible medium of composition, by moving beyond the traditional grid of discrete pitches and rhythms, in order to control the entire universe of sound for their palette of inspiration.
Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Menggunakan Metode Weighted Product

Employees are one of the most important factors in a company, without it a company will not be able to run smoothly, but over time an employee needs encouragement in carrying out his duties.With the holding of the best employee selection on CV.Laatansa Agung Tangerang will re-ignite the enthusiasm of all employees but because the selection that has been done is chosen subjectively then the desired results are not appropriate.This is where the adoption of a decision support system can be used and one method that can be easily understood and used is a weighted product method which will calculate each alternative and raised with its weight the greater the value of an alternative, the greater the chance to be chosen.This application is in accordance with the problems experienced by CV.Laatansa Agung Tangerang where the results obtained will be objective because they are carried out based on the assessment that occurs in the field not based on a unilateral or subjective assessment and from here the results obtained will trigger employees of CV.Laatansa Agung Tangerang to competing to be the best employee
Factual Power Loss Diminution by Enriched Artificial Fish Swarm Algorithm

In this work Enriched Artificial Fish swarm (EAFS) algorithm is projected to solve optimal reactive power problem.In the proposed algorithm, food concentration function, bulletin board approach, target position search mechanism, and position move method are utilized.Subsequently, an adjustment strategy of exploration range of artificial fish, which merge the global search with local search, is projected to enhance the explore capability of the projected algorithm.Every artificial fish will execute the swarming behavior, following behavior and foraging behavior in order to discover the goal move position th with the superior food concentration.The position with the uppermost food concentration of the new-fangled positions (Xnext1, Xnext2 andXnext3) are found by these behaviours' is used as th .Proposed Enriched Artificial Fish swarm (EAFS) algorithm has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show the projected algorithm reduced the real power loss comprehensively.
Complex-order Derivative and Integral Filters and its Applications

In this paper, complex-order derivative and integral filters are proposed, which are consistent with the filters with fractional derivative and integral orders. Compared with the filters designed only with real orders, complex order filters can reveal more details of input signals, and this can benefit a large number of tasks. The tremendous effect of the proposed complex-order filters, has been verified by several image processing examples. Especially, for the challenging problem indicating the diseased regions in prostate TRUS images, the proposed complex order filters look very promising. It is very astonishing that, indicating the diseased regions is fulfilled by complex-order integral, not by derivative. This is completely against the traditional views that using derivative operators to achieve the goal.
Object Attribute Recognition Based on Hard Negative Mining and Convolutional Neural Network

At present, most of the object attribute recognition methods operate directly on the original map, which is vulnerable to the interference of complex background information, thus affecting the recognition accuracy. In order to improve the accuracy of image recognition and classification, this paper divides object attribute recognition into two coupled subproblems: object location and object classification, establishes CS-CNN (Cascading Convolutional Neural Network) which combines Faster R-CNN (Region-CNN) and CNN, and proposes an improved model CS-CNN-PF. In the CS-CNN-PF localization part, in order to remedy the defect of imbalance between positive and negative samples in Faster R-CNN, an improved Faster R-CNN based on hard negative mining strategy is designed to locate the object. In the CS-CNN classification part, PReLU and Focal Loss are introduced to improve the activation function and loss function. The experimental results show that compared with the original CNN, CS-CNN, the accuracy of object attribute recognitions of CS-CNN-PF is improved by 29.73%, 6.82%, respectively.
Sensing Analysis of Feature Extraction Types for Handwritten Character Recognition

handwritten character recognition, line density
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning

In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions to attack them, making even state-of-the-arts involve at least two explicit hyper-parameters such that model selection is quite difficult. More roughly, they will fail in handling the third challenge, let alone addressing the three jointly. In this paper, we aim at meeting these under the least assumption by building a concise yet effective model with just one hyper-parameter. To ease insufficiency of available labels, we exploit not only the consensus of multiple views but also the global and local structures hidden among multiple labels. Specifically, we introduce an indicator matrix to tackle the first two challenges in a regression form while aligning the same individual labels and all labels of different views in a common label space to battle the third challenge. In aligning, we characterize the global and local structures of multiple labels to be high-rank and low-rank, respectively. Subsequently, an efficient algorithm with linear time complexity in the number of samples is established. Finally, even without view-alignment, our method substantially outperforms state-of-the-arts with view-alignment on five real datasets.
Reinforcement learning of minimalist grammars

Speech-controlled user interfaces facilitate the operation of devices and household functions to laymen. State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords that are subsequently inserted into semantic slots to interpret the user's intent. In order to develop proper cognitive information and communication technologies, simple slot-filling should be replaced by utterance meaning transducers (UMT) that are based on semantic parsers and a mental lexicon, comprising syntactic, phonetic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. We outline a reinforcement learning algorithm for the acquisition of syntax and semantics of English utterances, based on minimalist grammar (MG), a recent computational implementation of generative linguistics. English declarative sentences are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as formulas of predicate logic. Since MG codifies universal linguistic competence through inference rules, thereby separating innate linguistic knowledge from the contingently acquired lexicon, our approach unifies generative grammar and reinforcement learning, hence potentially resolving the still pending Chomsky-Skinner controversy.
EVALUASI TATA KELOLA TEKNOLOGI INFORMASI PADA RUMAH SAKIT DR. H. IBNU SUTOWO BATURAJA BERDASARKAN FRAMEWORK COBIT 5

Control of information technology governance at this time is very influential for the organization in achieving its strategic objectives. Technology has become an important part in supporting a service, including in Dr.H. Ibnu Sutowo Baturaja. The hospital has SIMRS in supporting its services. So that governance should be well planned. This research was conducted to evaluate the management of information technology that supports the SIMRS. The results of this study are to find out the value of the level of maturity in Dr. H Ibnu Sutowo Baturaja. Evaluations were carried out using the standard COBIT 5 measurement framework. The focus of this study was the domain of Ensure Resource Optimization (EDM01), Manage the IT Management Framework (APO01) (APO07), Build, Acquire and Implement (BAI04) and Monitoring, Evaluation and Access (MEA01).
Library Services: Library Resources & Services for Remote Research: Find E-Books

This Guide provides tips for remote access to library resources and services needed for your classes and doing research.
LibGuides: Pandemic Library Pedagogy: Technology

Adjusted Perspectives, Outcomes, Assessment, Design, and Instruction during prolonged periods of remote work. The technologies UWF Libraries will use to maximize remote access
Group Based Unsupervised Feature Selection

Unsupervised feature selection is an important task in machine learning applications, yet challenging due to the unavailability of class labels. Although a few unsupervised methods take advantage of external sources of correlations within feature groups in feature selection, they are limited to genomic data, and suffer poor accuracy because they ignore input data or encourage features from the same group. We propose a framework which facilitates unsupervised filter feature selection methods to exploit input data and feature group information simultaneously, encouraging features from different groups. We use this framework to incorporate feature group information into Laplace Score algorithm. Our method achieves high accuracy compared to other popular unsupervised feature selection methods (\(\sim \)30% maximum improvement of Normalized Mutual Information (NMI)) with low computational costs (\(\sim \)50 times lower than embedded methods on average). It has many real world applications, particularly the ones that use image, text and genomic data, whose features demonstrate strong group structures.
HIGH-SPEED LOCOMOTIVE XBOM ONTOLOGY MODELING RESEARCH SUPPORTING MRO SEMANTIC KNOWLEDGE REPRESENTATION

The new trends of high-speed rail locomotive manufacturing set higher requirement for MRO knowledge representation and XBOM. Therefore, XBOM ontology modeling supporting MRO semantic knowledge representation has become an important research topic of the high speed railway locomotive manufacturing industry. This paper firstly expounds the model frame of high-speed locomotive XBOM ontology, then take the global ontology, the construction ontology and the maintenance plan ontology as examples; respectively carding and analyzing four approaches each ontology contains that class (concept), object, data attribute, and constraint rule and central with the supporting MRO semantic knowledge representation, the XBOM ontology conceptual model can be created; At last, High speed rail train MRO semantic knowledge representation was realized by using protégé4.1 ontology modeling of XBOM for high-speed locomotive
BASIC LITERACY SKILLS THROUGH THE SCHOOL LITERACY MOVEMENT (GLS) AT SDN 6 PEKANBARU

The School Literacy Movement (GLS) is a policy issued by the government to overcome the problem of education and improve the basic literacy abilities of students. SDN 6 Pekanbaru is one of the schools that has implemented GLS at the familiarization stage well. Therefore, this study aims to determine the basic literacy skills of students in low classes through the implementation of GLS in SDN 6 Pekanbaru. This study used qualitative research conducted through observation and interviews about the implementation of GLS and students’ basic literacy skills with teachers and students in classes I and II. The results showed that GLS was able to improve the phonetic abilities of students by achieving a very good level of the ability to read and pronounce combinations of letters. The increase of students' vocabulary comprehension ability was seen where most students were able to know the meaning of words and retell the contents of books with theme daily activities. From the research findings, it was concluded that GLS improved the basic literacy ability of low-class students at SDN 6 Pekanbaru.
Encouraging Adoption, Adaptation, and Creation of OER at UNT

To complement UNT’s limited inclusive-access program, the UNT Libraries has taken the lead on two programs designed to encourage adoption, adaptation, and creation of OER at UNT. The first, OER Summer Grants, provides research funding for faculty who adopt an open textbook, modify an existing one, or create new supplemental material. The second, UNT Open Texts, is a partnership with the UNT Press that provides research funding for faculty who author a new open textbook to be published jointly by the Press and Libraries. These specifics of these programs will be presented.
Improved K-Means Algorithm for Optimizing Initial Centers

K-means clustering algorithm is a clustering analysis algorithm. In practical clustering applications, clustering results often fluctuate greatly. This fluctuation will affect the stability of clustering and even the efficiency of clustering. This paper presents an improved K-means clustering algorithm, which retains the advantages of the traditional algorithm. The method of selecting initial clustering centers is improved, and the shortcomings of traditional algorithms are eliminated effectively. The improved algorithm effectively absorbs the advantages of the grid-based clustering algorithm and combines the concept of grid density. Through the selection of initial clustering centers, the number of iterations of the improved algorithm decreases significantly. It effectively reduces the complexity of clustering, improves the efficiency and accuracy of clustering, improves the convergence of the algorithm, and achieves better clustering results.
Free lattices proof-theoretically

&#x0D; &#x0D; &#x0D; A sequent system is used to give alternative proofs of two well known properties of free lattices: Whitman’s condition and semidistributivity. It demonstrates usefulness of such proof systems outside logic. &#x0D; &#x0D; &#x0D;
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion

A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph. The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization, and completion (KACC), which are crucial for human to recognize the world and manage learned knowledge. Existing studies mainly focus on partial aspects of KACC. In order to promote thorough analyses for KACC abilities of models, we propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty. Specifically, we collect new datasets that contain larger concept graphs, abundant cross-view links as well as dense entity graphs. Based on the datasets, we propose novel tasks such as multi-hop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark. For MKA and MKC tasks, we further annotate multi-hop hierarchical triples as harder samples. The experimental results of existing methods demonstrate the challenges of our benchmark. The resource is available at https://github.com/thunlp/KACC.
Role Equivalence Attention for Label Propagation in Graph Neural Networks

Semi-supervised relational learning methods aim to classify nodes in a partially-labeled graph. While popular, existing methods using Graph Neural Networks (GNN) for semi-supervised relational learning have mainly focused on learning node representations by aggregating nearby attributes, and it is still challenging to leverage inferences about unlabeled nodes with few attributes—particularly when trying to exploit higher-order relationships in the network efficiently. To address this, we propose a Graph Neural Network architecture that incorporates patterns among the available class labels and uses (1) a Role Equivalence attention mechanism and (2) a mini-batch importance sampling method to improve efficiency when learning over high-order paths. In particular, our Role Equivalence attention mechanism is able to use nodes’ roles to learn how to focus on relevant distant neighbors, in order to adaptively reduce the increased noise that occurs when higher-order structures are considered. In experiments on six different real-world datasets, we show that our model (REGNN) achieves significant performance gains compared to other recent state-of-the-art baselines, particularly when higher-order paths are considered in the models.
Answer Generation through Unified Memories over Multiple Passages

Machine reading comprehension methods that generate answers by referring to multiple passages for a question have gained much attention in AI and NLP communities. The current methods, however, do not investigate the relationships among multiple passages in the answer generation process, even though topics correlated among the passages may be answer candidates. Our method, called neural answer Generation through Unified Memories over Multiple Passages (GUM-MP), solves this problem as follows. First, it determines which tokens in the passages are matched to the question. In particular, it investigates matches between tokens in positive passages, which are assigned to the question, and those in negative passages, which are not related to the question. Next, it determines which tokens in the passage are matched to other passages assigned to the same question and at the same time it investigates the topics in which they are matched. Finally, it encodes the token sequences with the above two matching results into unified memories in the passage encoders and learns the answer sequence by using an encoder-decoder with a multiple-pointer-generator mechanism. As a result, GUM-MP can generate answers by pointing to important tokens present across passages. Evaluations indicate that GUM-MP generates much more accurate results than the current models do.
Research Guides: Library Services for Rabb GPS Programs: Find Articles

This research guide highlights the services and resources that will be most helpful to you throughout your GPS program. The guide also includes tips on searching more effectively and ways to get help with research, writing, and citations.
OTHR multitarget tracking with a GMRF model of ionospheric parameters

The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere in OTHR target tracking. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, called ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm.