Reviewerly/exHarmony huggingface dataset:
gStore-C: A Transactional RDF Store with Light-Weight Optimistic Lock

RDF systems are widely applied in many areas such as knowledge base, semantic web, social network. Traditional RDF systems focus on speed up SPARQL queries on large RDF data but disregarding the performance of updates and transaction processing. In this demonstration, we propose a new transactional RDF system based on multi-version and MVCC. We introduce a lightweight optimistic lock upon atomic variables and operations that provides fine-grained locking and avoids scalability issues. The methods are fully implemented in an open-source RDF system gStore. And it outperforms other state-of-art RDF systems solutions on transactional workloads.
PCCR Based Wheelchair Control System [Society News]

Quadriplegia is an extremely serious disease. Due to the impaired function of patients' limbs, it is difficult for them to move freely by a wheelchair. This dilemma has become a huge obstacle to the rehabilitation of the disabled, and meanwhile added extra burden to society. To improve the quality of their lives, our team designs a vision-controlled automatic wheelchair based on the pupil center and cornea reflection (PCCR) technique. By constructing a multi-sensor real-time intelligent control system, the wheelchair realizes eye-controlled movements and other auxiliary functions such as emergency communication and rollover alarm. Through continuous optimization of multiple versions, a more intelligent, highly reliable, and low-cost wheelchair is manufactured.
Ontology Engineering for Gastric Dystemperament in Persian Medicine

Developing an ontology can help collecting and sharing information in traditional medicine including Persian medicine in a well-defined format. The present study aimed to develop an ontology for gastric dystemperament in the Persian medicine.This was a mixed-methods study conducted in 2019. The first stage was related to providing an ontology requirements specification document. In the second stage, important terms, concepts, and their relationships were identified via literature review and expert panels. Then, the results derived from the second stage were refined and validated using the Delphi method in three rounds. Finally, in the fourth stage, the ontology was evaluated in terms of consistency and coherence.In this study, 241 concepts related to different types of gastric dystemperament, diagnostic criteria, and treatments in the Persian medicine were identified through literature review and expert panels, and 12 new concepts were suggested during the Delphi study. In total, after performing three rounds of the Delphi study, 233 concepts were identified. Finally, an ontology was developed with 71 classes, and the results of the evaluation study revealed that the ontology was consistent and coherent.In this study, an ontology was created for gastric dystemperament in the Persian medicine. This ontology can be used for designing future systems, such as case-based reasoning and expert systems. Moreover, the use of other evaluation methods is suggested to construct a more complete and precise ontology.
The Role of Adaptation Patterns to Improve Information Literacy Skills of Students from Affirmation Program

Efforts to advance access and opportunities to continue secondary education in better schools is one of the strategies carried out by the Government of Indonesia for students in eastern Indonesia, such as Papua and West Papua through affirmation programs. The movement from the original location to the destination location for the affirmation causes students to be able to adapt with new conditions, therefore this study aims to determine the adaptability of affirmation students to improve their information literacy skills. This study used a correlational design and was carried out by distributing questionnaires to 93 affirmation students. This study used regression analysis to determine the relationship between adaptability and information literacy capabilities. The results showed that the significance value between adaptability and information literacy was 0,000, which means that there is a very significant relationship between the two variables. Students who took part in the affirmation program felt an improvement in their information literacy skills. This was because the affirmation destination schools had better technology access than their original schools. The affirmation program is very suitable to be carried out to advance the equal distribution of the quality of human resources in Indonesia, bearing in mind that Indonesia is an archipelago.
Electromyogram Based Prediction of Spoken Syllable Duration

There is a method of recognizing speech behavior based on information other than sound (silent speech recognition) as a method for speech communication in environments where speech cannot be used. Although there have been studies on the prediction of spoken words using electromyogram (EMG), information on emotional expressions such as syllable duration is lost. This study proposed a method for prediction the syllable duration of spoken words from EMG. The results of classification accuracy and average error show that the proposed method can predict the syllable duration.
QDetect: An Intelligent Tool for Detecting Quranic Verses in any Text

The past decade has witnessed an explosion in the generation of Arabic textual data, written in both modern standard Arabic (MSA) and in dialectal formats. Observing existing data reveals that verses from the Holy Quran are often quoted whether someone is giving a speech, conveying condolences, going through a hardship, and when expressing hope or joy among other emotions. Many applications can benefit from the automatic detection of those verses yet detecting Quranic Verses in text is not as easy as it may initially seem. This paper lists the challenges posed by this task, and proceeds to present an intelligent matching algorithm that addresses those. The paper also briefly describes a tool that has implemented the proposed algorithm, and which is currently publicly available as an open-source tool.
A Guide to Help Governments Prevent Ransomware Attacks

As ransomware attacks become more of an established part of the threat landscape for public sector organizations, government agencies can forge a safer path towards mission success by focusing on f...
Estimation and Correction of Multiple Skews Arabic Handwritten Document Images

Skew detection and correction have become important in the preprocessing stage as the first step in Arabic handwriting document recognition and analysis. In this paper, the skewness is detected and corrected through the help of morphological operation and connected components to identify the line in Arabic documents. For such purpose, the bounding box is drawn for each line and segmented separately which could be replaced by global bounding box in some cases. A statistical method is applied for detecting a skew angle and correcting the skewness with different orientations. The proposed method is implemented on more than 700 Arabic handwritten documents. The accuracy ratio is 98% based on line segmentation and it takes 2.5 s in color images and 1.5 s on binary or grayscale images.
Blockchain-based Service Performance Evaluation Method Using Native Cloud Environment

This study presents a performance evaluation system that helps Blockchain-based service planners make decisions. This system is offered as Docker and Kubernetes for portability and flexibility.
Design of a Reconfigurable Information Collection and Identification System for Packages Storage and Checkout

Abstract With the continuous and rapid development of e-commerce, the requirements of logistics informatization are also increasing. At present, the barcode collection and identification device used in the operation of packages storage and checkout is generally based on ASIC chip, which is not fast enough. Therefore, this paper proposes a reconfigurable information acquisition and identification system, which is applied to the operation of package storage and delivery. This system uses FPGA + ARM architecture to realize the functions of identifying, storing and uploading the barcode information on the outer surface of the package to the host computer through UDP protocol. Because the parallel operation structure of FPGA is used to design the barcode image processing accelerator circuit, the data processing capacity of the soft core processor is reduced, and the recognition speed of the system is improved as a whole. The test shows that this system is faster and more accurate than the package barcode information recognition device based on ASIC technology.
Studying the Role of Data Quality on Statistical and Neural Machine Translation

Statistical and Neural Machine translation techniques are based on the parallel data used for training models. The general belief is that more training data would result in better models. We studied the available corpora and ambient noises present in them. It revealed that the available data is highly noisy. The paper describes various types of noises present in there and how these are identified. Different types of noise filters are developed and normalization processes have been applied on the corpora. Statistical and neural machine translation models are trained to study the impact of cleaning of noisy data. We performed experiments with noisy data and with cleaned data after discarding noisy data from the training corpus. Standard test set WMT-14 has been used for performing evaluation. The quality of machine translation has been measured through BLEU scores. It was observed that even after discarding a significant volume of noisy data, the models without noisy data performed better than the corpus containing noises. It proves that quality of data has significant impact and mere having huge piles of uncleaned data in not a good choice. The test case presented here is for English-Hindi language pair. It also shows a path that for low resource language pairs, paying attention to the quality of data would bring returns in form of better translation performance. As the noises discussed in paper are general in nature, the findings should be true for any other Indian language pair also, due to inherent similarity among Indian languages.
Influence of an efficient Hierarchical Clustering Algorithm in analyzing Cancer affected DNA Dataset

Abstract This research work presents an influence of hierarchical clustering approach in anlyzing cancer affected DNA data set. The primary objective of this research work is to identify the best clustering algorithms to group cancer-affected DNA datasets. Data analysis shows an important role in bioinformatics. Data analysis technique used for grouping the data objects is based on unsupervised learning. Clustering is an unsupervised learning technique in data mining. It groups a set of clusters from the entire dataset. In this research work, 700 cancer-affected DNA datasets are considered for analysis. This research work compares three types of Clustering techniques, K-Means (KM), K-Medoids (KMS), and Hierarchical Clustering (HC), to group cancer-affected DNA. Each algorithm has some strengths and weaknesses. These clustering algorithms are compared in detail based on various parameters. Results prove that the hierarchical clustering algorithms show lesser execution time and increased accuracy than other KM and KMS algorithms.
Significance of Emphasized Features for Good Representation on Deep Metric Learning

Deep metric learning (DML) learns the mapping, which maps into embedding space in which similar data is near and dissimilar data is far. Most DML frameworks apply L2 normalization to feature vectors, and these feature vectors are non-sparse [6], [10], [30]. In this paper, we propose to apply L1 regularization loss to feature vectors. Proposed regularization emphasizes important features and restraints unimportant features on L2 normalized features. L1 regularization can combine with general DML losses because L1 regularization only regularizes feature vectors. In this paper, we finally propose SparseSoft-Triple loss, which is a combination of SoftTriple loss and L1 regularization. We demonstrate the effectiveness of the proposed SparseSoftTriple loss on some data sets for image retrieval tasks and fine-grained images.
A Novel Capsule Aggregation Framework for Natural Language Inference

Recent advances have advocated the use of complex attention mechanism to capture interactive information between premise and hypothesis in Natural Language Inference (NLI). However, few studies have focused on the further processing of matching feature, i.e. information aggregation. In this paper, we first investigate a novel capsule network for NLI, referred as Gcap. Gcap utilizes a gated enhanced fusion operation to obtain richer features between massive soft alignment information. Then the capsule aggregates those features through routing algorithms. Benefit from the routing mechanism of the capsule network, Gcap can dynamically generate feature vectors for subsequent classifier. Evaluation results demonstrate that our model achieves accuracy of 89.1%, 88.2% and 79.6% (79.3%) on SNLI, SciTail and MultiNLI datasets respectively, which outperforms the strong baseline with gains of 0.2%, 1.4% and 0.3% (0.6%). In particular, we compare the runtime inference efficiency of BERT and our model. Our model can attain up to 33.3 $$\times $$ speedup in online inference time. Thanks to dynamic aggregation, Gcap shows a strong ability to distinguish those cases that are easily confused.
Voxel-Based Three-Dimensional Neural Style Transfer

Neural Style Transfer has been successfully applied in the creative process for generating novel artistic 2D images by transferring the style of a painting to an existing content image. These techniques which rely on deep neural networks have been extended to further computational creativity tasks like video, motion and animation stylization. However, only few research has been conducted to utilize Neural Style Transfer in the spatially three-dimensional space. Existing 2D/3D hybrid approaches avoid the extra dimension during the stylization process and add postprocessing or differentiable rendering to transform the results to 3D. In this paper, we propose for the first time a complete three-dimensional Neural Style Transfer pipeline based on a high-resolution voxel representation. Following our previous research, our architecture includes the standardized gram matrix style loss for noise reduction and visual improvement, the bipolar exponential activation function for symmetric feature distributions and best practices for the underlying classification network. In addition, we propose regularization terms for voxel-based 3D Neural Style Transfer optimization and demonstrate their capability to significantly reduce noise and undesired artefacts. We apply our 3D Neural Style Transfer pipeline on a set of style targets. The style transfer results are evaluated using 3D shape descriptors which confirm the subjective visual improvements.
Simple interfaces for interactive and generative art installations

This article describes an ongoing research by the authors in the field of art within which a set of interactive audio-visual installations and sculptures have been produced over the last ten years. Those works have been exhibited in various public venues ranging from art spaces and galleries to public squares and streets. We introduce some of those works reflecting critically on the nature of interactivity used in them based on the experience gained by the use of the systems by spectators. Each of the works described explores a different mode of interactivity that tries to bridge the gap between the viewer and the system. Simplicity, both technical and in terms of design, has been a key issue when developing the interaction methods. The aim has been to produce non-intrusive, non-intimidating, direct and easy to use input methods using simple, common and well known objects and tools as interfaces. Finally, we also describe the way in which generative software processes and sound feedback techniques have been used to create ongoing processes that can be interrupted or altered by the viewer's interaction.
Learning-based cell detection in digital pathology

In blood testing, knowing the ratio and throughput of blood cells is crucial to help doctors make a clinical diagnosis. Here we propose a deep transfer learning strategy for accurate cell detection for digital pathology.
Application of Channel Attention for Speaker Recognition in the Wild

research-article Application of Channel Attention for Speaker Recognition in the Wild Share on Authors: Zhi Chen Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, ChinaView Profile , Lei Wang Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 128Pages 1–5https://doi.org/10.1145/3469213.3470331Online:18 August 2021Publication History 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months12Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Monolithic vs. hybrid controller for multi-objective Sim-to-Real learning

Simulation to real (Sim-to-Real) is an attractive approach to construct controllers for robotic tasks that are easier to simulate than to analytically solve. Working Sim-to-Real solutions have been demonstrated for tasks with a clear single objective such as "reach the target". Real world applications, however, often consist of multiple simultaneous objectives such as "reach the target" but "avoid obstacles". A straightforward solution in the context of reinforcement learning (RL) is to combine multiple objectives into a multi-term reward function and train a single monolithic controller. Recently, a hybrid solution based on pre-trained single objective controllers and a switching rule between them was proposed. In this work, we compare these two approaches in the multi-objective setting of a robot manipulator to reach a target while avoiding an obstacle. Our findings show that the training of a hybrid controller is easier and obtains a better success-failure trade-off than a monolithic controller. The controllers trained in simulator were verified by a real set-up.
Distillation for text classification task based on BERT

In recent years, with the rapid development of the Internet and the surge in the number of web texts, the demand for text classification technology has become increasingly significant. However, there are also the following problems: 1. The maximum input length of the model is 512, and some information will be lost if the longer text is directly truncated; 2. The model is large and the reasoning time is long, which is not convenient for mobile terminal deployment requirements. Aiming at problem 1: Firstly, the text with a length of more than 512 is intercepted. Considering that the end of the text usually contains more emotional information, the intercepting strategy is 170th and 340th. At the same time, another kind of text feature surface of the model is selected: The mean and maximum values are calculated respectively along the dimension of sequence length, which are spliced into column vectors as the input features of the model. Aiming at problem 2, the knowledge distillation of the model for classification tasks is carried out to achieve the reduction parameters to improve the inference efficiency to facilitate the actual deployment and application. Three groups of control experiments show that the overall classification accuracy of the improved BERT model is 97%, and the overall performance is more balanced, and the overall performance is more robust, which is slightly better than the BERT text classification model. The effect of the distilled BERT model is only 1.5% lower than that of the BERT model, but the number of parameters of the model is 92.6% less than that of the original model, and the reasoning time is nearly 4 times faster than the original model, which also shows the effectiveness of improving input features and model compression.
Teaching Practice of Data Structure Based On WeChat Platform

Abstract With the development of the mobile communication network, mobile learning is becoming a hot spot in the world. According to the idea of system engineering, this paper describes the teaching design process based on WeChat, which is divided into three parts, i.e. teaching preparation, teaching implementation and teaching evaluation. It also elaborates on the specific activities of each stage. The result shows that teaching activities based on the WeChat platform can promote students’ learning and have certain practical significance.
Mining and analysis of hot topics in Social Media

In social media, useful data information is mined to identify hot topics and predict the development direction of hot topics. The research method is to use web crawler to obtain relevant data, find new hot topics in media, get the original data, use Jieba to segment words, generate word segmentation results, obtain eigenvalues through Term Frequency - Inverse Document Frequency (TF-IDF) word frequency statistics, and use k-means algorithm to complete the clustering of eigenvalues, obtain the cluster results, and judge the hot topics in the cluster. In the experiment of advanced optimization hot topic, Linear Discriminant Analysis (LDA) method is used to classify the topic model, Gibbs sampling is used to sample and train the model, and K-means algorithm is used to achieve the purpose of clustering. The recall rate, accuracy rate and F value are calculated, and the experimental results of hot topics are summarized and analyzed to obtain the LDA model results of current hot topics in network media.
Nepali Word-Sense Disambiguation Using Variants of Simplified Lesk Measure

This paper evaluates simplified Lesk algorithm for Nepali word-sense disambiguation (WSD). Disambiguation is performed by computing similarity between sense definitions and context of ambiguous word. We compute the similarity using three variants of simplified Lesk algorithm: direct overlap, frequency-based scoring, and frequency-based scoring after dropping target word. We further evaluate the effect of stop word elimination, number of senses and context window size on Nepali WSD. The evaluation was carried out on a sense annotated corpus comprising of 20 polysemous Nepali nouns. We observed overall average precision and recall of 38.87% and 26.23% using frequency-based scoring for baseline. We observed overall average precision and recall of 32.23% and 21.78% using frequency-based scoring after dropping target word for baseline. We observed overall average precision and recall of 30.04% and 20.30% using direct overlap for baseline.
Network data acquisition method based on crop pest control knowledge

In this article, through a multithreaded focused web crawler type crawl, the knowledge related websites to establish database of crop diseases and pests control of crop diseases and pests control to create professional knowledge search engine, and by using natural language processing, knowledge map technology for processing and storage of data, such as, lower demand for the user to enter the search term, optimization of the user's search experience. This study constructed a complete search engine and provided a set of methods for obtaining and gathering data related to crop disease and pest control knowledge, which can also be applied to information collection and processing in other fields.