Reviewerly/exHarmony huggingface dataset:
Extracting Domain Information using Deep Learning

Across various scientific domains, digital publication of technical documents, often in the form of conference/journal article submissions, are the first accessible instance of new human knowledge in these respective fields. Synthesizing and curating this information is a slow and difficult process and often requires non-trivial human expertise. Given the ever increasing rate of these publications and the natural limitations of manual approaches, a computational solution to this problem is the paramount need of the hour. One of the central tasks is to extract important phrases and terminologies from scientific article. Although many tools are available to extracting keywords and named entities from a document, a key challenge is to determine how important they are with regard the context of the entire document. In some cases, important entities to an article might be a new vocabulary that haven't, or rarely, appeared from previous data. In other cases, there are also many other existing entities that are less important for the particular article but weighted more significantly from models based on prior knowledge. In this paper, we investigate how deep learning methods may be used to address this issue.
Kesantunan berbahasa para Kader Posyandu dalam pelaksanaan layanan informasi

The purpose of this study was (1) to describe the type of speech acts of Posyandu cadres Mawar VII and Posyandu Mawar IX Pademangan Barat North Jakarta in the information service process. (2) describe the principle of courtesy in Posyandu Mawar VII and Posyandu Mawar IX Pademangan Barat North Jakarta when the information service process takes place. The data used in this study are oral and written. Oral and written data is obtained by recording and recording the utterances of the cadres when carrying out information services. Data collection techniques in this study were carried out by referring to proficient free technique, recording techniques, and note taking techniques. The results of this study are that cadres do more polite speech acts using directive, representative, commissive, expressive, and declaration speeches. Second, cadres use four maxims, namely (1) wisdom maxim, (2) generosity maxim, (3) praise maxim, and (4) consensus maxim.
Corruption &amp; Bank Profitability: Understanding Sustainable Financial Inclusiveness in ECOWAS Region

This study examines the effects of corruption on banks profitability and the impact this may have on banks effectiveness in achieving their role in economic development through organizational inclusiveness. Using a dynamic panel model of all the commercial banks in the Economic Community of West African States (ECOWAS), the study deepens our understanding of how institutional and regional contexts impact on organizational inclusiveness. The study reports a significant positive relationship between the perception of the level of national corruption and banks profitability. The study suggests that the control of corruption is important in reducing financial exclusion and enhances financial market development in Africa.
Importance of use of High-performance work system and effectiveness of employee’s role on organizational performance

Human Performance Work System is an important part of every organization. It not only manages the system practices for employees but is also dedicated towards employee effectiveness. There are some factors which might be effective towards organizational performance. For example, employee’s performance is the most important part of an organization’s success, relationship between employee and supervisor, sales growth, voluntary turnover, knowledge sharing behavior, employee attitude, and effect on top performers. This literature review reveals that all the factors mentioned above are helpful in contributing towards making organizational performance better except voluntary turnover.
Cancer Metastasis Detection Through Multiple Spatial Context Network

Breast cancer is one of the leading causes of death by cancer in women, and it often requires accurate detection of metastasis in lymph nodes through Whole-slide Images (WSIs). At present, there are many algorithms of cancer metastasis detection based on CNN, which are generally patch-level models, aiming for increasing the sensitivity, speed, and consistency of metastasis detection. However, most of these algorithms use patch as an independent individual to train, which leads to the neglect of much important spatial context information in WSI
Drawing sound in MR space

What if we can draw sound in mid-air? We propose a multi-participant, interactive, audiovisual art experience using mixed reality (MR). In the MR space, participants can draw virtual lines in mid-air to create audiovisual effects.
On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

Recently, several authors have advocated the use of rule learning algorithms to model multi-label data, as rules are interpretable and can be comprehended, analyzed, or qualitatively evaluated by domain experts. Many rule learning algorithms employ a heuristic-guided search for rules that model regularities contained in the training data and it is commonly accepted that the choice of the heuristic has a significant impact on the predictive performance of the learner. Whereas the properties of rule learning heuristics have been studied in the realm of single-label classification, there is no such work taking into account the particularities of multi-label classification. This is surprising, as the quality of multi-label predictions is usually assessed in terms of a variety of different, potentially competing, performance measures that cannot all be optimized by a single learner at the same time. In this work, we show empirically that it is crucial to trade off the consistency and coverage of rules differently, depending on which multi-label measure should be optimized by a model. Based on these findings, we emphasize the need for configurable learners that can flexibly use different heuristics. As our experiments reveal, the choice of the heuristic is not straight-forward, because a search for rules that optimize a measure locally does usually not result in a model that maximizes that measure globally.
SISTEM PAKAR DIAGNOSA PENYAKIT SALURAN PERNAFASAN DENGAN METODE FUZZY TSUKAMOTO

Respiratory diseases are one of the most common diseases in Indonesia. Respiratory diseases increase the risk of fatal if not treated immediately. However, it is unfortunate that knowledge about the risk of respiratory disease is still lacking. The search method used in making this expert system is forward chaining with binary tree structure. Namely doing the processing of a set of data, then conducted inference in accordance with the rules applied to find the optimal conclusion. Experts provide rules for determining symptoms and illness. While the calculation and ranking of diseases that may suffer patients using the method fuzzy tsukamoto to provide the results of calculations that are certain based on the parameters. Then the patient's diagnostic process is done by the system. The Diagnostic Expert System for Respiratory Disease has been successfully established and can be used to assist in estimating the illness suffered by the patient as the result of the developed system is not much different from the running system. Based on the comparison of disease diagnosis result in expert system with manual system then the system accuracy level is 90,9%. Based on the website view has the largest percentage of 71.42 in good description, for user friendly / ease of respiratory system experts get the largest percentage of 85.71 in good information, to assist in the process of disease information and treatment get the largest percentage of 57.14 in a good description, to help the diagnosis process becomes easier to get the largest percentage of 71.42 in good information, for this expert system provides information on respiratory disease treatment accurately get the largest percentage of 57.14 in either.
Learning Motion-Aware Policies for Robust Visual Tracking

Visual object tracking aims to locate a moving target specified at the initial frame. Although this task is closely related to the temporal motion information, the motion model typically draws limited attention. In this paper, we propose a motion-aware multi-domain network for robust visual tracking. In our approach, a motion-aware agent is trained via reinforcement learning, which can infer the parameters of the particle filter in a continuous action space. Different from existing tracking-by-detection frameworks that the particle filter merely relies on the previous target state, our motion-aware agent, after receiving the current state, can adaptively change the parameters of the particle filter (e.g., particle location and scale range). As a result, our approach samples high-quality candidates for further classification/tracking, thus can better handle challenges such as fast motion and scale variation. Extensive experiments on large-scale benchmarks verify the effectiveness of our method.
Residual Frame for Noisy Video Classification According to Perceptual Quality in Convolutional Neural Networks

Perceptual quality of a video describes the quality consistent with human perception. The growing popularity of short video sharing on mobile platforms such as Tik Tok and WeSee makes the video assessment system based on perceptual quality a necessity. In practice, short videos captured by mobile devices often contain different types of distortions incurred by sensor noise or compression noise, which potentially makes the videos visually unpleasing to users and may degrade the performance of deep neural networks when applied to these noisy videos. Thus, it is necessary to identify noisy videos based on video perceptual quality. However, traditional video/image noise estimation methods are designed to estimate the variance of homogeneously distributed synthetic noise, not real noise. In this paper, we propose a simple yet effective method to recognize the noisy videos using their residual frames. Since the original video frame contains rich content information, which may result in under-or over-estimation of the noise, we construct residual frames to reduce the influence of the content information while maintaining the main noise information in the video. We also create a new data set with more than 30 thousand images captured from videos with real noise. Experimental results demonstrate the effectiveness of our proposed method.
Central-distributed Control Model of UAV Group and its application in Perception Module

Control model is the basis of UAV group. It determines the direction of control data flow as well as the structure of UAV group. In this paper, an improved central-distributed UAV group control model is proposed and the structure of UAV group under this model is derived. This model overcomes the problem of lacking robustness of centralized model and the complexity of fully distributed model as well as has the characteristics of modularization and autonomy. At the same time, the existing algorithms for the perception module of UAV group under this structure can be greatly simplified. The computing tasks that need to be passed to the ground control station can be done inside the UAV group.
Robot Programming Language Based on VB Scripting for Robot Motion Control

The limitations such as non-universal, interactive blocking and application specificity are common in existing robot programming languages. So, a universal robot programming language based on VB(Visual Basic) scripting language is designed in this paper. The language is aim to achieve the mixed programming of robot instruction and VB scripting language by doing the constructible and interactive design for robot command functions. Finally, a robot language programming motion simulation platform was designed to verify the implementation process of the programming language. Simulation results show the designed robot programming language can realize flexible programming under the Windows platform and further realize complex robot motion control and sensing interaction function, which provides a new way for the practice and extension of robot programming language system.
Suggestive GAN for supporting Dysgraphic drawing skills

&lt;span lang="EN-US"&gt;The squat competence of dysgraphia affected students in drawing graphics on paper may deter the normal pace of learning skills of children. Convolutional neural network may tend to extract and stabilize the action-motion disorder by reconstructing features and inferences on natural drawings. The work in this context is to devise a scalable Generative Adversarial Network system that allows training and compilation of image generation using real time generated images and Google QuickDraw dataset to use quick and accurate modalities to provide feedback to empower the guiding software as an apt substitute for human tutor. The training loss accuracy of both discriminator and generator networks is also compared for the SGAN optimizer.&lt;/span&gt;
Constraint Solving and Optimization Using Evolutionary Techniques

Constraint Solving and Optimization is very relevant in many real world applications including scheduling, planning, configuration, resource allocation and timetabling. Solving a constraint optimization problem consists of finding an assignment of values to variables that optimizes some defined objective functions, subject to a set of constraints imposed on the problem variables. Due to their high dimensional and exponential search spaces, classical methods are unpractical to tackle these problems. An appropriate alternative is to rely on metaheuristics. My thesis is concerned with investigating the applicability of the evolutionary algorithms when dealing with constraint optimization problems. In this regard, we propose two new optimization algorithms namely Mushroom Reproduction Optimization algorithm (MRO) and Focus Group Optimization algorithm (FGO) for solving such problems.
Ornstein Auto-Encoders

We propose the Ornstein auto-encoder (OAE), a representation learning model for correlated data. In many interesting applications, data have nested structures. Examples include the VGGFace and MNIST datasets. We view such data consist of i.i.d. copies of a stationary random process, and seek a latent space representation of the observed sequences. This viewpoint necessitates a distance measure between two random processes. We propose to use Orstein's d-bar distance, a process extension of Wasserstein's distance. We first show that the theorem by Bousquet et al. (2017) for Wasserstein auto-encoders extends to stationary random processes. This result, however, requires both encoder and decoder to map an entire sequence to another. We then show that, when exchangeability within a process, valid for VGGFace and MNIST, is assumed, these maps reduce to univariate ones, resulting in a much simpler, tractable optimization problem. Our experiments show that OAEs successfully separate individual sequences in the latent space, and can generate new variations of unknown, as well as known, identity. The latter has not been possible with other existing methods.
Bounded and Shielded: Assessing Security Aspects and Trustworthiness of Smart Contracts

This is an in-progress research project that aims to explore how archival science and cybersecurity can be applied to evaluate the trustworthiness and security of smart contracts. The analysis will be made using the requirements of trustworthy records and the investigation of vulnerabilities related to the development and implementation of smart contracts. The expected contribution is to improve smart contracts’ trustworthiness as archival records and evidence.
Learning and Utilizing Interaction Patterns among Neighborhood-Based Heuristics

This paper proposes a method for learning and utilizing potentially useful interaction patterns among neighborhood-based heuristics. It is built upon a previously proposed framework designed for facilitating the task of combining multiple neighborhood-based heuristics. Basically, an algorithm derived from this framework will operate by chaining the heuristics in a pipelined fashion. Conceptually, we can view this framework as an algorithmic template that contains two user-defined components: 1) the policy H for selecting heuristics, and 2) the policy L for choosing the length of the pipeline that chains the selected heuristics. In this paper, we will develop a method that automatically derives a policy H by analyzing the experience collected from running a baseline algorithm. This analysis will distill potentially useful patterns of interactions among heuristics, and give an estimate for the frequency of using each pattern. The empirical results on three problem domains show the effectiveness of the proposed approach.
On the Role of Syntactic Graph Convolutions for Identifying and Classifying Argument Components

This paper focuses on fundamental research that combines syntactic knowledge with neural studies, which utilize syntactic information in argument component identification and classification (AC-I/C) tasks in argument mining (AM). The following are our paper’s contributions: 1) We propose a way of incorporating a syntactic GCN into multi-task learning models for AC-I/C tasks. 2) We demonstrate the valid effectiveness of our proposed syntactic GCN in fair experiments in some datasets. We also found that syntactic GCNs are promising for lexically independent scenarios. Our code in the experiments is available for reproducibility.1
Web personalization issues in big data and Semantic Web: challenges andopportunities

Web personalization is a process that utilizes a set of methods, techniques, and actions for adapting the linking structure of an information space or its content or both to user interaction preferences. The aim of personalization is to enhance the user experience by retrieving relevant resources and presenting them in a meaningful fashion. The advent of big data introduced new challenges that locate user modeling and personalization community in a new research setting. In this paper, we introduce the research challenges related to Web personalization analyzed in the context of big data and the Semantic Web. This paper also introduces some models and approaches that can bridge the gap between the two. Future challenges and opportunities related to Web personalization, analyzed from the big data and Semantic Web perspective, are also presented. The research challenges outlined in this paper involve the scrutability of user models in personalization, generic personalization, meta-personalization, open corpus personalization, and semantic data modeling.
The Feasibility of Archipelago Children Literature Teaching Materials on Text-Based of Class V SDN 067091 Medan Kota Sub-district, Indonesia

This study aims to know the feasibility of archipelago children literature teaching materials on text-based of class V SDN 067091 Medan Kota sub-district, Indonesia. The type of this study is research and development which is the development of archipelago children literature teaching materials on text-based that deserve to support literature learning in elementary school. The product of archipelago children literature teaching materials is developed to support literary learning in elementary schools have met the requirements and are feasible to use based on the validation of material experts and design. The response to archipelago children literature teaching materials on text-based in literary material is very good and in accordance with the needs of students. It concludes that the use of archipelago children literature teaching materials on text-based is more effective in improving student learning outcomes. This was revealed from the student learning outcomes after being taught with developed teaching materials, namely an increase in learning outcomes from the average 78.98 with a good category to 86.11 with a very good category.
Planting Base Value of Hardworking Character Education through Ulid Novel

Important hard work characters are taught for the younger generation today so that researchers are interested in describing and explaining the values of character education in hard work in Ulid's novel by Mahfud Ikhwan. This novel tells the story of the struggle of poor families to send their eldest children from kindergarten to high school with various struggles and hard work in his life. The actions that the characters to bring out the character values of hard work that are proven through the attitude and every dialogue of the characters. This study included descriptive qualitative with note-taking techniques and literature studies. The results of data analysis of hard work character values are known to 9 frequencies, including unyielding characters, passionate characters, active working characters, earnest characters, the full character of confidence in acting, the full character of the struggle for life, independent practice character, cooperative character, and characters fight together.
Frequent Patterns Algorithm of Biological Sequences based on Pattern Prefix-tree

In the application of bioinformatics, the existing algorithms cannot be directly and efficiently implement sequence pattern mining. Two fast and efficient biological sequence pattern mining algorithms for biological single sequence and multiple sequences are proposed in this paper. The concept of the basic pattern is proposed, and on the basis of mining frequent basic patterns, the frequent pattern is excavated by constructing prefix trees for frequent basic patterns. The proposed algorithms implement rapid mining of frequent patterns of biological sequences based on pattern prefix trees. In experiment the family sequence data in the pfam protein database is used to verify the performance of the proposed algorithm. The prediction results confirm that the proposed algorithms can’t only obtain the mining results with effective biological significance, but also improve the running time efficiency of the biological sequence pattern mining.
Impact of Consuming Suggested Items on the Assessment of Recommendations in User Studies on Recommender Systems

User studies are increasingly considered important in research on recommender systems. Although participants typically cannot consume any of the recommended items, they are often asked to assess the quality of recommendations and of other aspects related to user experience by means of questionnaires. Not being able to listen to recommended songs or to watch suggested movies, might however limit the validity of the obtained results. Consequently, we have investigated the effect of consuming suggested items. In two user studies conducted in different domains, we showed that consumption may lead to differences in the assessment of recommendations and in questionnaire answers. Apparently, adequately measuring user experience is in some cases not possible without allowing users to consume items. On the other hand, participants sometimes seem to approximate the actual value of recommendations reasonably well depending on domain and provided information.
Improved Method for Noise Detection by DBSCAN and Angle Based Outlier Factor in High Dimensional Datasets

Various data mining methods are used to detect outliers from different databases. It is essential to detect outliers in different kinds of real time application areas, such as the health care sector and insurance, marketing, banks and finance et c. The proposed method in this paper is a new outlier detection method from a high dimensional data sets which combines the angle based outlier detection (ABOD) with the classical density based clustering method DBSCAN. The algorithm consists of three stages in which the first stage consists of applying of the PCA on the data set which will result in a subset of attributes, to this subset of attributes the dbscan algorithm is applied which results in detection of a set of outliers. In the third and final stage ABOD is applied to the set of outliers. Experimental analysis conducted state that the result improves the detection accuracy and decreases the number of false positives.