Reviewerly/exHarmony huggingface dataset:
Ranking and benchmarking framework for sampling algorithms on synthetic data streams

In the fields of big data, AI, and streaming processing, we work with large amounts of data from multiple sources. Due to memory and network limitations, we process data streams on distributed systems to alleviate computational and network loads. When data streams with non-uniform distributions are processed, we often observe overloaded partitions due to the use of simple hash partitioning. To tackle this imbalance, we can use dynamic partitioning algorithms that require a sampling algorithm to precisely estimate the underlying distribution of the data stream. There is no standardized way to test these algorithms. We offer an extensible ranking framework with benchmark and hyperparameter optimization capabilities and supply our framework with a data generator that can handle concept drifts. Our work includes a generator for dynamic micro-bursts that we can apply to any data stream. We provide algorithms that react to concept drifts and compare those against the state-of-the-art algorithms using our framework.
Multi-robot task assignment algorithm based on improved self-organizing mapping network

Abstract In order to solve the problem of slow convergence speed and easy task conflict, a multi-robot task assignment algorithm based on improved self-organizing mapping network is proposed in this paper. Considering the redundancy generated in the solution, the self-locking factor is introduced to optimize. The neuron is then reselected when the path intersects to avoid the risk of collision. The simulation results show that the proposed algorithm can effectively reduce the solution time and the distance of multi-robot navigation compared with the traditional algorithm.
The description of lists and sets of meta-language of normal forms of knowledge

Lists use for representation of various knowledge. As lists it comfortably to present formulas, functions, trees, columns, great numbers and many other difficult objects. Great number - one of the most essential structures of data, used both in mathematics, and in programming. The formalization of lists, list-based predicates and set-based predicates in the meta-language of normal forms of knowledge is presented, based on the known Prolog-formalizations of these concepts, which use a list-domain. Among the described list-based predicates are the following: adding an element to the list, removing an element, finding the last element of a list, finding adjacent elements in a list, concatenation of lists, reversing a list, palindrome, etc. Using the list-domain, the set-based predicates are described as follows: converting a list into a set, checking if an element is in a set, concatenation, intersection, difference, symmetrical difference, identity, complement of sets, relation of subset, proper subset. Problems in programming 2020; 1: 03-16
QUESTIONNAIRE SURVEY ON CREATING OF SHOP DRAWING AND FABRICATION BOOK, AND CONSTRUCTION CONTROL AND INSPECTION OF BAR PLACEMENT

In reinforcement construction, making a shop drawing and inspection of bar placement are matter of cardinal importance. However, there are no reports investigating these actual states and problems. Therefore, the purpose of this study is to investigate problems and actual state of shop drawing and fabrication book, and construction control and inspection of bar placement. As a result, this paper showed the actual state of reinforcement have worked for under the leadership of the reinforcement contractors. And, there has been a difference of instruction contents for each position who carry out inspection of bar placement.
Orthogonal Local Image Descriptors with Convolutional Autoencoders

This work proposes the use of deep learning architectures, and in particular Convolutional Autencoders (CAE’s), to incorporate an explicit component of orthogonality to the computation of local image descriptors. For this purpose we present a methodology based on the computation of dot products among the hidden outputs of the center-most layer of a convolutional autoencoder. This is, the dot product between the responses of the different kernels of the central layer (sections of a latent representation). We compare this dot product against an indicator of orthogonality, which in the presence of non-orthogonal hidden representations, back-propagates a gradient through the network, adjusting its parameters to produce new representations which will be closer to have orthogonality among them in future iterations. Our results show that the proposed methodology is suitable for the estimation of local image descriptors that are orthogonal to one another, which is often a desirable feature in many patter recognition tasks.
ASPECT CATEGORY POLARITY DETECTION USING MULTI CLASS SUPPORT VECTOR MACHINE WITH LEXICONS BASED FEATURES AND VECTOR BASED FEATURES

Sentiment Analysis (SA) is the classification and interpretation of the opinions or emotions within the text data using text analysis methods.Aspect Level Sentiment Analysis (ALSA) is a type of SA technique which uses different types of algorithms to extract the aspects of theentities from the text data and determining the sentiment of the aspects. ALSA is divided into two subtasks such as Aspect Category Detection(ACD) and Aspect Category Polarity Detection (ACPD). ACD determines the aspect categories discussed in each sentence from a set of reviewsentences and a given predefined set of aspect categories. Aspect categories need not occur as terms in the sentences and arecharacteristically coarser than the aspect terms. For every review sentence the aspect categories are provided to identify the polarity ofaspect category. The objective of ACPD is to identify the polarity such as negative, positive, neutral or conflict of every aspect categorymentioned in each sentence. In this paper, the task aspect category polarity detection is addressed. The three types of features such aslinguistic features, Lexicons features and Vector Space features are extracted from the reviews. The classifier is trained with these features.The learned model is used to detect the polarities of the categories that are present in the test review sentences. The combination ofproposed features with the existing features improves the accuracy of the system. The accuracy of the proposed system has enhanced by 1.3% compared with best performing system.
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency

Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-intensive to generate. In this paper, we introduce Scribble2Label, a novel weakly-supervised cell segmentation framework that exploits only a handful of scribble annotations without full segmentation labels. The core idea is to combine pseudo-labeling and label filtering to generate reliable labels from weak supervision. For this, we leverage the consistency of predictions by iteratively averaging the predictions to improve pseudo labels. We demonstrate the performance of Scribble2Label by comparing it to several state-of-the-art cell segmentation methods with various cell image modalities, including bright-field, fluorescence, and electron microscopy. We also show that our method performs robustly across different levels of scribble details, which confirms that only a few scribble annotations are required in real-use cases.
Uncertainty as key element in the analysis of X–ray angiography images

Abstract The X-ray angiography images are routinely used to assess the blood vessels. The acquisition procedure considers a medical imaging system which allows obtaining views of the vessel while the blood flows thought them. The X-ray source is influenced on the region to be viewed and then, the projection of the all anatomical structures in the champ of view is shown through an image intensifier. The information of the blood vessel is impacted for the other structures. Additionally, the blood and the contrast product required in the acquisition are not mixed homogeneously, producing artifacts in the images. Finally, the noise is also an impact factor in the quality of the angiography images. In the coronary vessel case, the branches of the network are superposed. In this paper, an enhancement procedure to diminish the uncertainty associated to X-ray angiography images is reported. The relation between two versions of the angiograms is determined using a fuzzy connector considering that this relation diminishes the images intrinsic uncertainty. These versions correspond with images filtered with low-pass and high-pass image filters, respectively. The technique is tested with images of the coronary and kidney vessels. The qualitative results show a good enhanced of the angiography images.
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness

Neural Tensor Networks (NTNs), which are structured to encode the degree of relationship among pairs of entities, are used in Logic Tensor Networks (LTNs) to facilitate Statistical Relational Learning (SRL) in first-order logic. In this paper, we propose Randomly Weighted Tensor Networks (RWTNs), which incorporate randomly drawn, untrained tensors into an NTN encoder network with a trained decoder network. We show that RWTNs meet or surpass the performance of traditionally trained LTNs for Semantic Image Interpretation (SII) tasks that have been used as a representative example of how LTNs utilize reasoning over first-order logic to exceed the performance of solely data-driven methods. We demonstrate that RWTNs outperform LTNs for the detection of the relevant part-of relations between objects, and we show that RWTNs can achieve similar performance as LTNs for object classification while using fewer parameters for learning. Furthermore, we demonstrate that because the randomized weights do not depend on the data, several decoder networks can share a single NTN, giving RWTNs a unique economy of spatial scale for simultaneous classification tasks.
Guides: Data Literacy Instruction for Undergraduate Chemistry: Survey for Undergraduate Chemistry Students

Our study will provide insights into which data literacy competencies are being taught in chemistry undergraduate curricula, in order to explore potential opportunities for advancing data instruction in chemistry and other STEM disciplines.
PEMBINAAN KESADARAN MORAL OLEH GURU PENDIDIKAN KEWARGANEGARAAN DALAM MENGATASI KENAKALAN REMAJA DI SMA KECAMATAN DELTA PAWAN KABUPATEN KETAPANG

The purpose of this study was to determine the formation of student moral awareness.The study was conducted at seven high schools in Delta Pawan sub-district, Ketapang district using descriptive qualitative research methods.The results of the research are the implementation of Civics teachers in fostering moral awareness to overcome juvenile delinquency in students that have been carried out well as in the process of learning using the school discipline media, providing motivation and encouragement so that awareness is inherent in students, and giving good examples, and give a reprimand if it violates the code of conduct.Factors that influence in fostering moral awareness to overcome juvenile delinquency are internal factors of students themselves such as biological-psychological hereditary defects, negative traits that lead to misbehavior, imbalance of meeting basic needs with desires, and weak self-control and social perception.External factors are problems that come from the family environment, community environment, and problems that come from formal education institutions in general.
Pengaruh Media Buku Cerita Bergambar Terhadap Pemahaman Membaca Bahasa Mandarin Murid SD kelas 1 W.R. Supratman 2 Medan

This study was aimed at describing the types of process and the reason of using those types in two narrative texts. It was conducted by using descriptive qualitative design. The data were collected by using documetary technique. There were 170 clauses in the story of Sleeping Beauty which contained material process (120/70,5%),mental process (9/5,3%), relational process (17/10,5%), verbal process (8/4,7%), behavioral process (12/7,1%) and existential process (4/2,3%). In the story of Zeus, there were 112 clauses which contained material process (81/72%), mental process (3/2,7%), relational process (23/20,9%), verbal process (2/1,8%),behavioral process (2/1,8%) and existential process (1/0,9%). From the data, it was concluded that all the types of processes were found in both strories. The reason of using those types because there was some descriptions of the events and were dominated by the act of doing performed by the participants.
The Dilemma Between Dimensionality Reduction and Adversarial Robustness.

Recent work has shown the tremendous vulnerability to adversarial samples that are nearly indistinguishable from benign data but are improperly classified by the deep learning model. Some of the latest findings suggest the existence of adversarial attacks may be an inherent weakness of these models as a direct result of its sensitivity to well-generalizing features in high dimensional data. We hypothesize that data transformations can influence this vulnerability since a change in the data manifold directly determines the adversary's ability to create these adversarial samples. To approach this problem, we study the effect of dimensionality reduction through the lens of adversarial robustness. This study raises awareness of the positive and negative impacts of five commonly used data transformation techniques on adversarial robustness. The evaluation shows how these techniques contribute to an overall increased vulnerability where accuracy is only improved when the dimensionality reduction technique approaches the data's optimal intrinsic dimension. The conclusions drawn from this work contribute to understanding and creating more resistant learning models.
Design of Application To Tracking System and Temperature Monitoring During Delivery Based on IoT Device

In the industry 4.0 era – a variety of new technologies to added value. Industry 4.0 is the end – to – end digitalization of all physical assets and integration into the digital supply chain with value chain technologies. Digital supply chain brings down those walls and the chain becomes a completely connected that is transparent to all the actors involved from the shippers of goods, delivery service companies, and finally to the customers. Logistics as part of the supply chain has an important role in Indonesia. Specifically, in delivery services. Currently, the tracking of the delivery service company can only be executed from one drop point to another drop point. A trip between drop points cannot be traced so that the tracking becomes not transparent. Logistics roles in delivery service company should utilize the Internet of Things (IoT) in handling customer who needs real-time transparency routes and monitoring of temperature-sensitive pharmaceutical cargo and perishable cargo. If this is implemented, delivery service companies in Indonesia can be crowned as the most advanced delivery service company in the world.  The research information system uses a quantitative method. The data collection technique used in this research was collected by filling out a questionnaire. The result shown indicates that delivery service companies in Indonesia may use Traceable e-Seal that are connected to the MyTraceable application as a solution to transparency route, real-time monitoring of temperature-sensitive goods. We prompt to use MyTraceable application and smart lock because it can increase customer satisfaction and delivery service company performance.
Virtual Reality Holography—A New Art Form

Virtual reality holography (VRH) is a new art form that synthesizes the qualities of traditional hand drawing with the unique features of holography and virtual reality (VR) art. As a new art-making tool, it offers the possibility to develop artistic ideas and concepts that could not be materialized with any other medium, whilst from a practical point of view it has the unique ability to showcase VR artworks external to VR space, without the need for a headset. Starting with a brief historical overview of holography art and VR art, this chapter presents an exploration of the VRH medium and the artworks which were generated. The various contexts in which these artworks are disseminated are presented, and the dialogues arising from the material specificity of VRH as a new medium are discussed. VRH is envisaged to extend the reach of audience engagement.
Experimental demonstration of channel order recognition in wireless communications by laser chaos time series and confidence intervals

High-bandwidth irregular oscillations caused by optical time-delayed feedback subjected to the laser cavity, known as laser chaos, have been investigated for various engineering applications. Recently, a fast decision-making algorithm for a multi-arm bandit problem by utilizing laser chaos time series has been demonstrated. Furthermore, the arms order recognition of the reward expectation for each arm has been successfully developed by incorporating the notion of the confidence interval regarding the reward estimate. However, in previous studies, the verification was limited to numerical experiments; real-world demonstrations were not conducted. This study experimentally demonstrated that the arm-order recognition algorithm is successfully operated in channel order recognition in wireless communications while revising the original strategy to take into account the wireless application requirements. Such accurate arm rank recognition involving non-best arms would be useful for various real-world applications such as channel bonding, among others.
Library Guides: Shared Library Services Platform project: Migration Stories

A change is coming to search, including the Libraries catalog and databases. Learn more about the project and what to expect.
디지털 문학, 무엇을 가르칠 것인가?

This paper discusses the following questions: What do readers read in digital literature? Do they read programming languages that make it possible for digital literature to be displayed on a computer screen or the texts themselves on the screen? How can readers interpret literary texts when they constantly change? How do teachers teach them? Do they have to teach computer programming languages to humanities students? To investigate these questions, the author reviews the arguments of the two digital literature academic groups; one advocates teaching computer programming languages to students who learn digital literature, and the other focuses on close reading of the texts like traditional literary texts. This paper will analyze works of Australian media artist, Mez Breeze, of digital poet and professor of SUNY Buffalo, Loss Glazier and of Korean net artist, Young Hae Chang. Lastly, for the constructive discussion on whether it is a good idea or bad to teach computer programming to humanities students, the author introduces her digital humanities pedagogy through which she teaches how to write digital literature using Twine and Twitter Bot. (Hanbat National University)
Evaluating Random Forest and a Long Short-Term Memory in Classifying a Given Sentence as a Question or Non-Question

Natural language processing and text classification are topics of much discussion among researchers of machine learning. Contributions in the form of new methods and models are presented on a yearl ...
COMPARISON OF EDUCATIONAL MANAGEMENT OF CHINA AND USA

With the development of world economy and political culture, there are different forms of modern education, the teaching method also emerge in endlessly.Education management is the important force to promote the development of education and change, it is through education management organization education team is coordinated, full play to the role of human education, financial, and other resources, and information is given.Using internal various favorable conditions, education management activities goal is to achieve high efficiency of education.Today, there are many problems existing in the education management, such as laws and regulations, education management system, education investment, education management is only skin deep, it has no substantial effect, so we will research China and the United States to find them in the higher education, science, education management research system and measures, and to find them in education management is worthy of our learning place.
Agranulocyte &amp; neutrophil nucleus enhancement through 510nm wavelength of light

Enhanced features can be easily segmented and used for classification algorithms. Light wavelength of 510nm is used as illumination source for bright field microscope. This specific wavelength of light enhances nucleus, granules in White blood Cells (WBCs). Eosinophil and basophil has coarse granules which also get enhanced hence nucleus segmentation gets difficult. This method is effective in enhancing nucleus of agranulocytes viz. Lymphocyte, Monocyte and Neutrophil of normal person. This modification in image acquisition of pathological images will further help in quick segmentation of features.
THE EFFECT OF ALOE VERA TOWARD THE NUMBER OF FIBROBLASTS ON THE WOUND INCISION OF WISTAR RAT’S (RATUS NORVEGICUS)

Introduction: Incision is a wound caused by a sharp object, for example in surgery. The characteristics of the wound are pain, open wounds, and the length of the wound is greater than in it. Handling the wound there are various ways, one of which is using Aloe Vera. The aim of this study was to determine the effect of topical Aloe vera gel concentration of 10%, 20%, and 40% to determine the amount of fibroblast tissue in wistar strain rats (Rattus orvegicus) in incisions. Methods: This study uses an experimental design with a sample of 75 rats divided into 5 groups; 0.9% NaCl group, 1% tulle-framycetin sulfate group, 10% Aloe vera group, 20% Aloe vera group and 40% Aloe vera group. Samples of incisional wound skin were performed on days 3.7, and 12. All data were processed and analyzed statistically using SPSS 20. Based on microscopic observations at 10 times per field of view, the highest number of fibroblasts given at NaCl was given on the day 12th is 28.98 per field of view, treatment using tulle-framycetin sulfate 1% is 34.5 per field of view, treatment using Aloe vera 10% is 17.32 per field of view, treatment using Aloe vera 20% is 33, 9 per field of view while the treatment using Aloe vera was 40% ie 18.7 per view. Results : The results of the study with the Independent T-test and Paired T-test showed that the results of fibroblast tissue between 0.9% NaCl group, 1% tulle-framycetin sulfate, 10% Aloe vera, 40% Aloe vera with 20% Aloe vera there were differences which were significant (p &lt;0.05) where 20% Aloe vera gave effective results in the wound healing process. Conclusion : This study concluded that 20% of Aloe vera can be used as an alternative choice of wound healing. In the next study it is expected to examine the variable TGF (Transforming Growth Factor) that affects cell growth in wound healing.