Reviewerly/exHarmony huggingface dataset:
Drbox Family: A Group of Object Detection Techniques for Remote Sensing Images

Objects in remote sensing images are difficult to detect due to arbitrarily rotated angles and the wide variance of scales. As bounding box plays an important role in object detection, we proposes a new bounding box type named rotated bounding box (rBox). With the application of rBox, we have proposed a series of detection techniques (DrBox, DrBoxLight, DrBoxSemi, DrBoxPro) to effectively handle the situation where the orientation angles of the objects are arbitrary. This article is a brief overview of these techniques. The original DrBox detector applies VGG-net as its main network framework, with image pyramid input to address multi-scale problem. DrBoxLight is a mini version of DrBox, which applies MobileNet and knowledge distillation to be deployed on embedded devices. DrBoxSemi is a semi-supervised version of DrBox, so annotation of all training samples is no longer necessary. DrBoxPro is the most important update for DrBox with professional designing of abundant prior-rBoxes on feature pyramid networks. In our experiments, we demonstrated how rBox helps to improve the performance of object detection compared with traditional bounding boxes. Besides, we evaluated the performance of our DrBox family on a series of object detection tasks.
Performance Assessment of Various Text Document Features through K-Means Document Clustering Approach

The text documents are very important in the usage of www.Many users require so much text document to gather the information in their required field of interest.To serve the internet surfers the appropriate required topic documents are to be retrieved.For this purpose for indexing and retrieving the text document the researchers tend to produce many algorithms in the field of text document mining.The entire effort of clustering is achieved relying on the selection of appropriate similarity metrics.The document clustering is performed by two steps which begin with feature extraction prior to clustering operation.The features from the text document are extracted through various operations like preprocessing, tokenization, Stop word removal, streaming and bag of Words were performed.By performing the previous mentioned operations successively the Document representing features namely WordCount, TF_IDF and probability of words were determined to perform the next process with clustering algorithm.In the clustering phase the three features and some of the similarity measures were used to perform the clustering operation.The proposed method yields better results for Probability based features compared with other two TFIDF and WordCount.
Analysis and Design of Restaurant Information System using Unified Modeling Language

This research was done with the aim of facilitating the Admin access to process raw material inventory data which outlets and warehouses are located in different cities. The method used is the SDLC method, which is a method that uses several stages in system development ranging from System Planning, System Analyst, System Design, System Implementation, System Implementation, and System Maintenance. The result of this research is a system that can be accessed by admins to facilitate the work process.
CoSSAT: Code-Switched Speech Annotation Tool

Code-switching refers to the alternation of two or more languages in a conversation or utterance and is common in multilingual communities across the world.Building codeswitched speech and natural language processing systems are challenging due to the lack of annotated speech and text data.We present a speech annotation interface CoS-SAT, which helps annotators transcribe codeswitched speech faster, more easily and more accurately than a traditional interface, by displaying candidate words from monolingual speech recognizers.We conduct a user study on the transcription of Hindi-English codeswitched speech with 10 annotators and describe quantitative and qualitative results.
University as a Medium of Promoting Media Literacy among Schoolchildren

In the present article, the relevant alterations in schoolchildren's media educational training is discussed.A couple of questionnaires (initial and final) were conducted to recognize the objective assessment of the media school functioning, according to the methodology developed by Fedorov, 2004.The results from the initial and final assessments demonstrate the working effectiveness of the media school.It is suggested that media education is a compulsory element in the conceptual core of the University discourse.
Estimasi Nilai Tukar Petani Subsektor Tanaman Pangan Menggunakan JST pada Provinsi Sumatera Utara

One way the government determines the level of welfare of farmers is to look at the development of the farmers' exchange rate index. Farmer exchange rate index (NTP) is useful in determining, measuring the ability to exchange products sold by farmers with products needed by farmers in household production and consumption. This study produced an estimate of 4 (four) years ahead from the farmer subsector index of the exchange rate index while the data was obtained from Official News published monthly by the Central Sumatra Provincial Statistics Agency (BPS). The estimation of the farmer exchange rate index will use the backpropagation algorithm, as one algorithm that is quite accurate in estimating and predicting. The problem in this study was that there was a decrease in the percentage of the farmers' exchange rate index of the food crop sub-sector in 2017 which caused a decline in the level of welfare index of farmers in the food crop subsector. This study uses 5 architectural models, that is 3-7-1, 3-15-1, 3-20-1, 3-28-1,3-40-1. Of the 5 architectures, the best is the 3-28-1 architectural model producing an epoch of 7060 with the time needed 01:19 seconds.
Converting an Electric Power Utility Network to Defend Against Crafted Inputs

This chapter proposes a roadmap that employs secure parsers to eliminate the possibility of input-handling vulnerabilities in industrial control systems. Industrial control systems are responsible for maintaining the integrity of power grids. Complex communications networks constitute the backbones of these systems. Communications in industrial control networks must be processed correctly and they should not crash devices or enable attackers to access networked devices. Language-theoretic security is the practice of comprehensive input handling using secure parsers. This chapter demonstrates that the existing collection of secure parsers for industrial control protocols can cover the communications needs of industrial control networks. It discusses the merits of guarding industrial control networks using secure parsers, proposes a triage procedure for implementation and summarizes the security benefits and lessons learned.
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning

The prediction of the relationship between the disease with genes and its mutations is a very important knowledge extraction task that can potentially help drug discovery.In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.Task 1 can be regarded as the traditional name entity recognition (NER), which cultivates molecular phenomena related to gene mutation.Task 2 can be regarded as relation extraction which captures the thematic roles between entities.For two tasks, we exploit the pre-trained biomedical language representation model (i.e., BERT) in the pipe of information extraction for the collection of mutation-disease knowledge from PubMed.And also, we design a fine-tuning technique and extra features by using multi-task learning.The experiment results show that our proposed approaches achieve 0.60 (ranks 1) and 0.25 (ranks 2) on task 1 and task 2 respectively in terms of F 1 metric.
In-home monitoring evaluation by home-dwelling older adults, caregivers and healthcare providers

Abstract Background In Europe, population ageing is increasing the healthcare needs and costs. Both frailty and chronic diseases affecting older people reduce their ability to live independently. However, most older people prefer to age in their own homes. New development of in-home monitoring can play a role in staying independent, active and healthy. Our objective was to evaluate a new in-home monitoring system among home-dwelling older adults (OA), their family caregivers (FC), and their healthcare providers (HCP) for the support of home care. Methods The system continuously monitored OA’s daily activities (e.g., mobility, sleep habits, fridge visits, door events) at home by ambient sensor system (DomoCare®) and health-related events by wearable sensors (Fitbit®, ECG). In case of deviations in daily activities (e.g., changes in mobility), alerts were transmitted to HCP via email. Using specific questionnaires, the opinion of 13 OA, 13 FC, and 20 HCP were collected at the end of 12-month of follow-up focusing on their user experience and the impact of in-home monitoring on home care services. Results These preliminary results underlined that the majority of OA, FC, and HCP consider that in-home sensors can help staying at home, improving home care, reducing family stress, and preventing domestic accidents. The opinion tended to be more favourable toward ambient sensors (80%) than toward Fitbit® (67%) and ECG (64%). On average, OA (80%) and FC (73%) tended to be more enthusiast than HCP (63%). Some barriers reported by HCP were a fear of weakening of the relationship with OA and an excessive surveillance. Conclusions Overall, the opinion of OA, FC and HCP were positive related to in-home sensors, with HCP being more mixed about their use in clinical practice. Key messages In-home monitoring technologies seem to be generally well accepted. In-home monitoring technologies could be help facilitating home care of older people.
Penyebaran Konsentrasi Polutan Dengan Pemodelan Dispersi Gauss Menggunakan Matlab

In the era of development of science and technology until 2019 it has become increasingly sophisticated and makes life easier for humans. This progress has led to increasingly sophisticated technology with the intention of making life easier, practical, efficient and not experiencing much difficulty. But the development of these technologies, have positive and negative impacts. Positive impacts such as convenience, while negative impacts such as air pollution. Contamination of pollutants into the air resulting from factory activities, power plants and motor vehicles spread to the environment. This substance is very damaging to the environment which causes the air we breathe to be polluted and very influential on health. This study aims to describe the process of spreading pollutant concentrations in the air originating from factory chimney fumes. Pollutants that spread in the air can be modeled with the dispersion gauss model which is processed using the MATLAB programming language. The expected results from modeling are the distance of the highest pollutant concentration area and the range of pollutant coverage that spread, so that later will be done variations of the effective height of the chimney emission (H) to get results that vary according to the height of the chimney.
VSP at PharmaCoNER 2019: Recognition of Pharmacological Substances, Compounds and Proteins with Recurrent Neural Networks in Spanish Clinical Cases

This paper presents the participation of the VSP team for the PharmaCoNER Tracks from the BioNLP Open Shared Task 2019.The system consists of a neural model for the Named Entity Recognition of drugs, medications and chemical entities in Spanish and the use of the Spanish Edition of SNOMED CT term search engine for the concept normalization of the recognized mentions.The neural network is implemented with two bidirectional Recurrent Neural Networks with LSTM cells that creates a feature vector for each word of the sentences in order to classify the entities.The first layer uses the characters of each word and the resulting vector is aggregated to the second layer together with its word embedding in order to create the feature vector of the word.In addition, a Conditional Random Field layer classifies the vector representation of each word in one of the mention types.The system obtains a performance of 76.29%, and 60.34% in F1 for the classification of the Named Entity Recognition task and the Concept indexing task, respectively.This method presents good results with a basic approach without using pretrained word embeddings or any hand-crafted features.
Implementasi Algoritma Clustering K-Means Untuk Mengelompokkan Mahasiswa Baru Yang Berpotensi (Studi Kasus: STMIK Budi Darma)

Every year STMIK Budi Darma accepts new students through the selection test path. The process of student admission at STMIK Budi Darma begins with socialization to the community or school, registration, selection examinations, announcement of exam results, Re-registration, Campus Recognition System (CRS) and the lecture process. The value of the selection test results, the average National exam score, the mathematics National exam scores and the English National exam scores are combined and used in ranking prospective students who are accepted based on the capacity of the study program. So far, STMIK Budi Darma lecturers sometimes find it difficult to carry out classroom actions because each student has unequal potential so the learning outcomes are not optimal. One of them is caused by grouping new students into one class based on ranking, not based on the potential of each new student. In order to group prospective new students based on their potential, K-Means clustering algorithm is used. The basis of grouping with the K-Means clustering algorithm is based on the closest distance values of numerically based criteria with output in the form of clusters. The cluster that has been formed is used as the basis for grouping new students into one class.
Weakly Supervised Attention Networks for Entity Recognition

Barun Patra, Joel Ruben Antony Moniz. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Pedagogical Components of Professional Activity of University Professors: Complex Analysis

Modern trends and qualification requirements to pedagogues in terms of the competency-based approach have foregrounded the issue of training of university professors.The article includes the analysis of pedagogical components of professional activity of university professors.The necessity of pedagogical training of professors without basic pedagogical education is substantiated.Theoretical analysis of numerous scientific papers provided the possibility to reflect views of scholars on this issue.In order to fulfill their mission, pedagogues need to be ready to solve professional pedagogical tasks, have a basic level of professional pedagogical competence.Diagnostic methods of research included: analysis of scientific publications and regulatory legal documents, as well as a questionnaire survey with the use of the application developed by Google Company, Google Forms.The resulting conclusions of the said research are used by the authors as exploratory research.The analysis of the results of the survey reveals that the majority of professors, irrespective of the length of their pedagogical activity, understand the necessity to constantly update their ideas about methods and technologies of training at university, introduce modern educational technologies into the training process and need the corresponding knowledge.
ANALISIS NILAI PENDIDIKAN KARAKTER PADA NOVEL “SEPATU DAHLAN“ KARYA KHRISNA PABICHARA

This study aims to describe the value of character education contained in the novel “Sepatu Dahlan” by Khrisna Pabichara. The subject of this research is the novel “Sepatu Dahlan” Karya Khrisna Pabichara published by the publisher Noura Books (Mizan Group) in 2012. This type of research is descriptive qualitative aimed to describe or describe the phenomena that exist, both natural phenomena or human engineering. Data collection techniques in this study used reading, note-taking, and library techniques. The results showed that the character values contained in the novel “Sepatu Dahlan” by Khrisna Pabichara based on the most widely appear character quotations there are 3 characters that are the most dominant, is the value of religious character education, discipline and hard work. The values of character education found in the novel “Sepatu Dahlan” by Khrisna Pabichara are in the form of behaviours performed by a character in the face of events and in various forms of interaction between a character and another character that is told.
Digital platforms and the rise of global regulation of hate speech

The EU Code of Conduct on hate speech requires online platforms to set standards to regulate the blocking or removal of undesirable content. The standards chosen can be analysed for four variables: the scope of protection, the form of speech, the nature of harm, and the likelihood of harm. Comparing the platforms' terms of use against existing legal standards for hate speech reveals that the scope of speech that may be removed increases significantly under the Code's mechanism. Therefore, it is legitimate to consider the platforms as substantive regulators of speech. However, the Code is only the latest example in a global trend of platforms' activities affecting both the substantive regulation of speech and its governance. Meanwhile, States' authority to set standards of acceptable speech wanes.
JPEG Image Compression Using Discrete Cosine Transform

In this paper, Baseline JPEG standard using has been implemented along with encoding and decoding of gray scale images in JPEG.The first step in encoding starts by dividing the image in 8*8 blocks into sub-images on which DCT is performed which is followed by dividing the resulted matrices by a Quantization matrix.The algorithm ends by making the data one-dimensional which is done by Zigzag Coding and composed by Arithmetic coding or Huffman Coding.Reversing the process of encoding results in decoding process.At first, the received bit stream is converted back into two-dimensional matrices and multiplied by Quantization matrix and Inverse DCT is performed and the sub-images are connected to restore the image.Effect of coefficients on the image restored and difference between compression ratios has been presented in the paper.
Librarian role in research library services in Indonesian Institute of Sciences

Introduction.In a research library, researchers are the main players, while . librarians should be the partners in conducting research even though this combination rarely happens. In general, librarians are only involved in a part of research activities as their research assistants to carry out some administrative stuffs, such as distributing questionnaires and sending invitation letters, instead of designing a research plan and/or somequestionnaires or doing data analyses.Data Collection Method. We interviewed librarians in the Center for Political Studies, Research Center for Economic, Center for Science and Technology Development, and Center for Scientific Documentation and Information of Indonesian Institute of Sciences, Jakarta.Data Analyses. Data were analyzed using qualitative analysis methods, through the direct interview, where data categorized by using open coding.Results and Discussions.The results stated that 86.7% of respondents had ever been involved in research activities carried out by the research center in the same or different units. From that result, 53.3% respondents ever engaged research activities up to 5 times in a year. In the other hands, librarians who answer conduct independent research and are often involved in the research are 13% respondents. Some recommendations figured out from this research are (1) a librarian could be a plagiarism examiner for a research proposal, (2) a librarian must be literated in information technologies, (3) a librarian should be involved in examining a literature review of the proposal and writing the manuscripts.Conclusions.The role of a librarian is not just as a research assistant or a data collector but as a research buddy who could help addressing some documentative problems.
Postscript. Fur, feather, teeth and skin: How do technologies and ontologies meet in time and space?

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Combinatorial space of watershed hierarchies for image characterization

We propose a framework for image characterization using hierarchies of segmentations. For this purpose, we structure the space of hierarchies using the Gromov–Hausdorff distance. We propose different ways of combining hierarchies and study their properties thanks to the GH distance. We then expose how to leverage the combinatorial space of hierarchies to derive efficient image representations. This framework opens a path for a controlled exploration and use of the combinatorial space of hierarchies.
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models

Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data.Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated.We perform extensive experiments on 86 combinations of languages and tasks.Our results are that, on average, transductive auxiliary task self-training improves absolute accuracy by up to 9.56% over the pure multitask model for dependency relation tagging and by up to 13.03% for semantic tagging.N Main MTL Aux-ST ceiling Aux-ST Extra Aux Extra Main 10k 86.39 *3.79% *1.97% -0.30% 0.19% 1k 79.19 *7.42% *5.39% *1.74% *7.01%0.5k 75.77 *8.69% *6.85% *2.64% *10.17% 0.1k 66.31 *11.32% *9.56% *4.97% *18.15% 1k 67.82 n/a *1.74% 0.05% 0.64% 0.5k 63.32 n/a *4.60% 0.83% *2.31% 0.1k 50.44 n/a *13.03% *4.93% *11.58%
Accelerating Position-Aware Top-k ListNet for Ranking Under Custom Precision Regimes

Document ranking is used to order query results by relevance with ranking models. ListNet is a well-know ranking approach for constructing and training learning to rank models. Compared with traditional learning approaches, ListNet delivers better accuracy, but is computationally too expensive to learn models with large datasets due to the large number of permutations involved in computing the gradients. This paper introduces a position-aware sampling approach, which takes the importance of ranking positions into account and shows better accuracy than previous sampling methods. We also propose an effective quantisation method based on FPGA devices for the ListNet algorithm, which organises the gradient values to several batches, and associates each batch with a different fractional precision. We implemented our approach on a Xilinx Ultrascale+ board and applied it to the MQ 2008 benchmark dataset for ranking. The experiment results show a 4.42x speedup over an Nvidia GTX 1080T GPU implementation with 2% accuracy loss.