Reviewerly/exHarmony huggingface dataset:
Sistem Retensi dan Alih Image Rekam Medis Inaktif RS Khusus Kanker MRCCC Siloam Semanggi

Along with the development of technology, the speed of data processing is needed in order to compete with competitors. A company must have an advantage over other companies if it does not want to lose in the competition. MRCCC Siloam Semanggi is a company that provides health services for cancer patients. One of the transaction processes within the hospital is sensing data in the form of images of patient data. Image data processing activities at this hospital are not yet structured and require a database in order to assist in fast data processing. This study aims to create an image transfer system to transfer physical documents into digital documents. This system is useful for hospital employees to be able to find documents easily for certain purposes, the system is made web-based using XAMPP, using PHP language with MySQL database. The results of the analysis of research that has been done, there are problems that arise related to the retention system in hospital patient data. Retention data collection activities are usually carried out by sorting out patient medical record documents from those not recorded on a computer.
Unbalanced mallows models for optimizing expensive black-box permutation problems

Expensive black-box combinatorial optimization problems arise in practice when the objective function is evaluated by means of a simulator or a real-world experiment. Since each fitness evaluation is expensive in terms of time or resources, the number of possible evaluations is typically several orders of magnitude smaller than in non-expensive problems. Classical optimization methods are not useful in this scenario. In this paper, we propose and analyze UMM, an estimation-of-distribution (EDA) algorithm based on a Mallows probabilistic model and unbalanced rank aggregation (uBorda). Experimental results on black-box versions of LOP and PFSP show that UMM outperforms the solutions obtained by CEGO, a Bayesian optimization algorithm for combinatorial optimization. Nevertheless, a slight modification to CEGO, based on the different interpretations for rankings and orderings, significantly improves its performance, thus producing solutions that are slightly better than those of UMM and dramatically better than the original version. Another benefit of UMM is that its computational complexity increases linearly with both the number of function evaluations and the permutation size, which results in computation times an order of magnitude shorter than CEGO, making it specially useful when both computation time and number of evaluations are limited.
Modeling the Unigram Distribution

The unigram distribution is the non-contextual probability of finding a specific word form in a corpus.While of central importance to the study of language, it is commonly approximated by each word's sample frequency in the corpus.This approach, being highly dependent on sample size, assigns zero probability to any out-of-vocabulary (oov) word form.As a result, it produces negatively biased probabilities for any oov word form, while positively biased probabilities to in-corpus words.In this work, we argue in favor of properly modeling the unigram distribution-claiming it should be a central task in natural language processing.With this in mind, we present a novel model for estimating it in a language (a neuralization of Goldwater et al.'s (2011) model) and show it produces much better estimates across a diverse set of 7 languages than the naïve use of neural character-level language models.
The Exploration of Metallic Deposits Using Satellite Image Processing in the Parwan-Panjsher Area, Afghanistan

Unique tectonic features have resulted in diverse metallic and non-metallic mineralization in Afghanistan. Hence, this paper is focused on the development of exploration and mineral resource data in Afghanistan. The study area is located in the western Hindu Kush tract and on the northern verge of the Hari Rud–Panjsher fault, Afghanistan, which mainly associates with the western Hindu Kush and Badakhshan plutonic belts. The rock units include crystalline limestones and diabase formed during the Paleozoic era and Triassic period. The aim of this study was to employ Remote Sensing (RS) methods by using the Landsat-8 satellite and ASTER sensor to spot iron and copper mineralization zones in the Parwan-Panjsher area. Therefore, Band Composition, Principal Component Analysis (PCA), and Band Ratio were applied to identify the iron oxide minerals. The detected area provided by satellite images had very good conformability with the results of field studies. Furthermore, mineralization of carbonate host-rock iron and Fe–Cu–(Au) skarn were observed during the field surveys. Hematite–Magnetite, Chalcopyrite, and pyrite mineralization have resulted from the injection of various diabase subvolcanic into carbonate units. Additionally, high heat flow has caused widespread marble formation in the area. The results were supported by microscopic and geochemical studies.
T.U.E.S.D.A.Y (Translation Using machine learning from English Speech to Devanagari Automated for You)

In today's globalized world, one thing that acts as a barrier to healthy information exchange is language.On top of that, with the onset of technologies such as YouTube and Facebook making it easy to share knowledge with people all around the world, language can impede that flow of information.If someone in India wants to access a piece of content in written form from another country, they can make use of services such as Google translate.However, the same cannot be extended for any piece of content which is rendered in an audio-visual medium since no such apparatus has been developed which can help people comprehend the content of that specific type.Specifically, students have experienced this first-hand when they try to access content from other universities but the medium of language is something that they are not wellversed in.With these issues in mind, this group is trying to build an automatic voice dubbing system: a speech-to-speech translation pipeline which can help users easily understand other users without the worry of language barrier.The model is called T.U.E.S.D.A.Y. (Translation Using machine learning for English Speech to Devanagari Automated for You) and is divided into three conversion modules: English speech to English text, English text to Devanagari text, and finally Devanagari text to Hindi speech.These modules work together in tandem to ensure an integrated model as a whole.
Use Learnable Knowledge Graph in Dialogue System for Visually Impaired Macro Navigation

Dialogue in natural language is the most important communication method for the visually impaired. Therefore, the dialogue system is the main subsystem in the visually impaired navigation system. The purpose of the dialogue system is to understand the user’s intention, gradually establish context through multiple conversations, and finally provide an accurate destination for the navigation system. We use the knowledge graph as the basis of reasoning in the dialogue system, and then update the knowledge graph so that the system gradually conforms to the user’s background. Based on the experience of using the knowledge graph in the navigation system of the visually impaired, we expect that the same framework can be applied to more fields in order to improve the practicality of natural language dialogue in human–computer interaction.
DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions

Short textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question answering.However, generating entity descriptions, especially for new and long-tail entities, can be challenging since relevant information is often scattered across multiple sources with varied content and style.We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description.DESC-GEN consists of 37K entity descriptions from Wikipedia and Fandom, each paired with nine evidence documents on average.The documents were collected using a combination of entity linking and hyperlinks to the Wikipedia and Fandom entity pages, which together provide high quality distant supervision.The resulting summaries are more abstractive than those found in existing datasets, and provide a better proxy for the challenge of describing new and emerging entities.We also propose a two-stage extract-then-generate baseline and show that there exists a large gap (19.9% in ROUGE-L) between state-of-the-art models and human performance, suggesting that the data will support significant future work. 1
Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training

Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing (NLP), which is generally trained on the human-annotated corpus.However, data annotation is costly and timeconsuming, which restricts its scale and further leads to the performance bottleneck of NER models.In reality, we can conveniently collect large-scale entity dictionaries and distantly supervised data.However, the collected dictionaries are lack of semantic context and the distantly supervised training instances contain large noise, which will bring uncertain effects to NER models when directly incorporated into the high-quality training set.To address the above issue, we propose a BERT-based decoupled NER model with two-stage training to appropriately take advantage of the heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances.Our decoupled model consists of a Mention-BERT and a Context-BERT to respectively learn from the context-deficient dictionaries and noised distantly supervised instances at the pre-training stage.At the unifiedtraining stage, the two BERTs are trained together on human-annotated data to predict the correct labels for candidate regions.Empirical studies on three Chinese NER datasets demonstrate that our method achieves significant improvements against several baselines, establishing the new state-of-the-art performance.
Rancang Bangun Aplikasi Naskah Dinas Elektronik Berbasis Web Menggunakan WDLC

In the modern era of technological development, the application of electronic official script management is a solution to the administrative and communication challenges of agencies during the pandemic, such as today, where administrative work needs to be done from home (work from home). With the condition of a high level of need, the management of official manuscripts in the XYZ organization still uses conventional methods. So, this raises obstructed letter distribution and loss documents problem, and waste of paper use. We propose the Electronic Service Manuscript Management Application to replace the official script management process that previously applied conventional methods to using electronic methods to solve this problem. Besides, we also implement security aspects in the SHA-512 algorithm to fulfill authentication, AES-128 to meet confidentiality, and RSA2048 to meet integrity and non-repudiation. Apart from the security aspect, functionally, the system is built based on the Guidelines for Electronic Service Manuscripts, which are tailored to the organization's needs. The system development uses the Web Development Life Cycle (WDLC) methodology using the Yii2 framework. From the research results, it is concluded that the Electronic Service Manuscript Management Application has been built according to the needs of the organization and can be a security alternative in the official script management process.&#x0D; &#x0D; Index Terms—digital signature; encryption; official script management; WDLC
A Hybrid Shuffled Frog-Simulated Annealing Algorithm for Clustering

Data clustering is a collection of data objects similar to one another within the same cluster and dissimilar to the objects in other clusters.The shuffled frog-leaping algorithm is a nature-inspired algorithm that mimics the natural biological evolution process of frogs.This algorithm also consists of elements like local search and exchanging information globally.This algorithm faces the problem of converging in local optima due to the limitations of the local search method used to explore search space.In this paper, a hybrid shuffled frog-leaping algorithm is introduced for clustering.The proposed algorithm uses a simulated annealing search method instead of a simple local search to improve the search behavior for selecting fitter solutions required in each iteration.Six benchmark datasets are used to validate the performance of the proposed algorithm.Quality measures used are purity, entropy, completeness score (CS), homogeneity score (HS), and FMeasure (FM).Fitness functions used to optimize are total within-cluster variance (TW) and the Silhouette coefficient (SC).Results obtained are compared with the results of twelve other state of the art algorithms.Results stored in the tables clearly shows that our proposed algorithm outperforms other algorithms in terms of quality.Results also prove that the proposed algorithm converges in the significantly less amount of time and eliminates local optima problem also.
A Laser Spot Tracking Algorithm for Underwater Wireless Optical Communication Based on Image Processing

The APT for a laser spot underwater is one of the key technologies for underwater wireless laser communication. In this paper, an algorithm is proposed for fast and accurate tracking of underwater moving target spot based on laser spot image captured by CCD camera, i.e., MASMS algorithm, which combines the classical Kalman filtering algorithm with ASMS algorithm to improve the loss of the target when the target moves too fast. The performance of MASMS is tested, including center position error of a laser spot, frame rate, precision plot, and success plot, and compared with the classical algorithms such as Mean-Shift, ASMS, STRUCK, and KCF, which are available in literature. It is shown by our simulating experiment that the performance of MASMS algorithm is better than that of mentioned above four existing algorithms except for the only frame rate, that is slightly lower than KCF and ASMS. Therefore, the MASMS algorithm can be applied to track underwater wireless laser communication signals in the future in order to enhance the effectiveness and reliability of communication.
Entropy-Guided Control Improvisation

High level declarative constraints provide a powerful (and popular) way to define and construct control policies; however, most synthesis algorithms do not support specifying the degree of randomness (unpredictability) of the resulting controller.In many contexts, e.g., patrolling, testing, behavior prediction, and planning on idealized models, predictable or biased controllers are undesirable.To address these concerns, we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm which supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur, (ii) a soft constraint specifying what typically occurs, and (iii) a randomization constraint specifying the unpredictability and variety of the controller, as quantified using causal entropy.This framework, which extends the state of the art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment.ERCI enables a flexible modeling formalism which we argue, theoretically and empirically, remains tractable.
The English Google translation of Indonesian lecturer’s academic writing: A preliminary study

The work of translation seems to be much easier with the assistance of web-based Machine Translation such as Google Translate. Does it work well? This study aims at finding how Google translates academic texts from Indonesian into English. It is also to find the extent to which Google Translate accurately and naturally transfers such texts. The data are Indonesian academic texts written by undergraduate lecturers from three different majors: Management, Japanese Literature, and Mathematics. The discussion section in each article is translated into English using Google Translate web. The data is analyzed in terms of two perspectives: forms and meaning. With regard to form or syntactic analysis, the data are investigated based on the structure of the Source Language and the Target Language. It is also to observe whether such forms are natural to generate meaning in the Target Language. With regard to the meaning or semantic analysis, the data are evaluated based on the original message, whether or not the message conveyed in the Target Language is accurate. The findings indicate that most of the translations of Google Translate are built in similar forms to those of the Source Language. In terms of meaning, the messages conveyed seem to be fairly accurate even though inaccuracy is still found.
Evaluating the Efficacy of Summarization Evaluation across Languages

While automatic summarization evaluation methods developed for English are routinely applied to other languages, this is the first attempt to systematically quantify their panlinguistic efficacy.We take a summarization corpus for eight different languages, and manually annotate generated summaries for focus (precision) and coverage (recall).Based on this, we evaluate 19 summarization evaluation metrics, and find that using multilingual BERT within BERTScore performs well across all languages, at a level above that for English.
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition

We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale. Our platform is built upon the Machine Learning Platform for AI of Alibaba Cloud. Its main functionality is to support efficient learning and inference for end-to-end ASR models on distributed GPU clusters. It allows users to learn ASR models with either pre-defined or user-customized network architectures via simple user interface. On EasyASR, we have produced state-of-the-art results over several public datasets for Mandarin speech recognition.
Stratigraphic and Microfacies Study of Kometan Formation (Upper Turonian-Lower Campanian), in the Dokan area, Northern Iraq

The Kometan Formation is widely distributed in the northern (Kurdistan region) and central Iraq. The studied area is located near the Dokan Dam, about 58 km., to the Northwest of the Sulaymaniyah city, Northeastern Iraq. The Kometan Formation is exposed on the southwest flank of the Sarah anticline. The formation consists of limestone and dolomitic limestone, which have cherts nodules throughout the formation. The Gulneri Formation is recorded below the Kometan Formation with unconformable contact, while at the top is bounded by the Shiranish Formation unconformably too. Three microfacies are identified, these are lime mudstone, planktic foraminiferal lime wackestone-packstone, keeld planktonic foraminiferal lime wackestone-packstone microfacies. All the sedimentary and fossil evidence refer that the sedimentary environment of the formation is the outer shelf to upper bathyal at the lower and upper parts of formation and its extension to the middle bathyal in the middle part of the formation. Based on the stratigraphic ranges of the recorded Calcareous nannofossils biozones, the age of the Kometan Formation at Dokan area is Late Turonian-Early Campanian.
A Coalitional Markov Decision Process Model for Dynamic Coalition Formation among Agents

In multi-agent field, most studies on coalition formation problems assume static environments, but in real-world scenarios coalition formation problems can occur in dynamic environments. This creates the dynamic coalition formation problem where the coalition structure might change with time; our response is to propose a coalitional Markov decision process (CMDP). In CMDP, the dynamic process is modeled as a MDP where the agents observe the current state to formulate some coalitions and each coalition represents a unit that can take action to impact the environment. The environment probabilistically transfers to the next state to repeat the process. However, changing the coalition structure in the midst of MDP transitions incurs a cost that hinders the use of the classical algorithms invoked to solve MDP (e.g. Q-learning) to solve CMDP. Thus, we propose a novel algorithm coalitional Q-learning to solve CMDP and prove it can guarantee the convergence on optimal policies in CMDP; Furthermore, we apply the proposed algorithm to a dynamic formation problem in edge computing to guide edge servers to cooperatively perform tasks and thus verify the algorithm's effectiveness.
Performance Analysis of an Energy Trading Platform Using the Ethereum Blockchain

We evaluate and analyze the performance of a local electricity market for energy trading that we implemented on the Ethereum platform. The energy trading is based on a double auction with multiple sellers and multiple buyers; the matched price and volume are determined by a trade reduction mechanism. We benchmark the performance of Ethereum using a systematic blockchain performance evaluation method. Based on this, we propose and analyze an efficient market operation method. In particular, we relate the limits on the scalability and real-time performance of the market to the throughput and latency of the Ethereum platform. We also identify the minimum resources necessary to operate an Ethereum client.
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision

In structured prediction problems, crosslingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages.However, not all source models are created equal and some may hurt performance on the target language.Previous work has explored the similarity between source and target sentences as an approximate measure of strength for different source models.In this paper, we propose a multi-view framework, by leveraging a small number of labeled target sentences, to effectively combine multiple source models into an aggregated source view at different granularity levels (language, sentence, or sub-structure), and transfer it to a target view based on a task-specific model.By encouraging the two views to interact with each other, our framework can dynamically adjust the confidence level of each source model and improve the performance of both views during training.Experiments for three structured prediction tasks on sixteen data sets show that our framework achieves significant improvement over all existing approaches, including these with access to additional source language data.