Reviewerly/exHarmony huggingface dataset:
Port Throughput Forecast Model Based on Adam Optimized GRU Neural Network

The forecast of port throughput can not only provide a basis for the business plan of the enterprise, but also provide a reference for the construction of transportation facilities in the city where the port is located. Gated Recurrent Unit (GRU), as the latest Long Short-Term Memory (LSTM) variant, solves the complementary redundancy of the original input gate and forget gate. In order to predict data more accurately, this paper proposes a port throughput forecast model based on Adam optimized GRU neural network (Adam-GRU). Combining the effective gradient optimization algorithm Adam with GRU can make the forecasting process more efficient. Taking the throughput data of Guangzhou Port Group G Port Affairs Company as an example, this paper compares the Adam-GRU model with other proven traditional forecasting methods, including Back Propagation (BP) Neural Network, Recurrent Neural Network (RNN) and LSTM. The experimental results show that Adam-GRU performs better under the evaluation of various performance indicators, reflecting clear practicality and innovation.
A Parallel Network used to Human Activity Recognition

This paper proposes a novel module AIC for HAR task, which can achieve a good accuracy with few parameters relatively. Instead of blindly improving the model size, we utilize the parallel convolutional network to fully extract features of different dimensions. Extensive experiments on three publicly available HAR datasets are conducted to validate the generalization of our proposed module. The specific results are presented in Experiment Results and Analysis, which shows that the module consumes less time and parameters and is promising to recognize human activity in Mobile devices.
Mining Criminal Dataset Using Gradient Boosting Algorithm

Data mining is a process for evaluating & testing huge remaining databases towards produce novel data that can be necessary for association. Using existing datasets is predicted to remove new information. There have been many approaches to data mining analysis and prediction. But there have been only several attempts in the field of crime science. Very few people have tried to compare all of these approaches which are with the information produced by them. Police stations & further related criminal justice agencies maintain big databases of data that may expect or examine criminal activity & criminal activity into community. We may too predict criminals built proceeding crime statistics. On basis of these subjects, we have used the concept of data mining to predict the criminology and the reasons behind the occurrences of the crime. The proposed algorithm is able to predict more significant features with higher accuracy and efficiency.
Using Paralinguistic Information to Disambiguate User Intentions for Distinguishing Phrase Structure and Sarcasm in Spoken Dialog Systems

This paper aims at utilizing paralinguistic information usually hidden in speech signals, such as pitch, short pause and sarcasm, to disambiguate user intention not easily distinguishable from speech recognition and natural language understanding results provided by a state-of-the-art spoken dialog system (SDS). We propose two methods to address the ambiguities in understanding name entities and sentence structures based on relevant speech cues and nuances. We also propose an approach to capturing sarcasm in speech and generating sarcasm-sensitive responses using an end-to-end neural network. An SDS prototype that directly feeds signal information into the understanding and response generation components has also been developed to support the three proposed applications. We have achieved encouraging experimental results in this initial study, demonstrating the potential of this new research direction.
Cross-Domain Co-Author Recommendation Based on Knowledge Graph Clustering

Nowadays, due to the growing demand for interdisciplinary research and innovation, different scientific communities pay substantial attention to cross-domain collaboration. However, having only information retrieval technologies in hands might be not enough to find prospective collaborators due to the large volume of stored bibliographic records in scholarly databases and unawareness about emerging cross-disciplinary trends. To address this issue, the endorsement of the cross-disciplinary scientific alliances have been introduced as a new tool for scientific research and technological modernization. In this paper, we use a state-of-art knowledge representation technique named Knowledge Graphs (KGs) and demonstrate how clustering of learned KGs embeddings helps to build a cross-disciplinary co-author recommendation system.
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream end-to-end ASR

Performance degradation of an Automatic Speech Recognition (ASR) system is commonly observed when the test acoustic condition is different from training. Hence, it is essential to make ASR systems robust against various environmental distortions, such as background noises and reverberations. In a multi-stream paradigm, improving robustness takes account of handling a variety of unseen single-stream conditions and inter-stream dynamics. Previously, a practical two-stage training strategy was proposed within multi-stream end-to-end ASR, where Stage-2 formulates the multi-stream model with features from Stage-1 Universal Feature Extractor (UFE). In this paper, as an extension, we introduce a two-stage augmentation scheme focusing on mismatch scenarios: Stage-1 Augmentation aims to address single-stream input varieties with data augmentation techniques; Stage-2 Time Masking applies temporal masks on UFE features of randomly selected streams to simulate diverse stream combinations. During inference, we also present adaptive Connectionist Temporal Classification (CTC) fusion with the help of hierarchical attention mechanisms. Experiments have been conducted on two datasets, DIRHA and AMI, as a multi-stream scenario. Compared with the previous training strategy, substantial improvements are reported with relative word error rate reductions of 29.7 - 59.3% across several unseen stream combinations.
Towards Layer-Wise Optimization of Contextual Neural Networks with Constant Field of Aggregation

In this paper contextual neural networks with different numbers of connection groups in different layers of neurons are considered. It is verified if not-uniform patterns of numbers of groups can influence classification properties of contextual neural networks. Simulations are done in dedicated H2O machine learning environment enhanced with Generalized Backpropagation algorithm. Experiments are performed for selected UCI machine learning problems and cancer gene expression microarray data of bone marrow acute lymphatic and myeloid leukemia.
Design of learning applications using the Rapid Application Development method

Abstract Information and Communication Technology (ICT) in the current era of globalization has become a fundamental requirement in supporting the effectiveness and quality of the education process. Educational issues in Indonesia such as the quality and relevance of education, access and equity in education, geographical range, education management, autonomy and accountability, efficiency and productivity, budget and sustainability, cannot be overcome without the help of ICTs. ICT-based education is a means of interaction between management and administration of education, which can be used by both educators and education staff and students in improving the quality, productivity, effectiveness and access to education. The purpose of this study is to find out how to design e-learning applications in informal and non-formal education, as well as efforts to improve the use of e-learning in informal and non-formal education. So that the results of this study can be improved the quality of informal and non-formal education, and can facilitate the field of education in Indonesia by using information technology as an interactive learning medium. The method developed in designing e-learning applications is to use a method that is generally used in the development of information systems using Rapid Application Development. The system that has been built can provide information about distance learning in informal and normal education. In addition, the system that has been built can accelerate the learning process for students / tutors. Therefore, it does not take long in the learning process and to get new material from the teacher. Moreover, the system built can facilitate users in learning and communicating with teachers when there is information needed / needed from the teacher.
Learners'Motivation Types in the Smart Instruction of English for Specific Purposes

The paper introduces results of research in which potential increase in learner's knowledge is considered from the view of four motivation types (Accurators, Coordinators, Directors, Explorers) within the process of smart instruction applied at two topics (Career Development, Healthy Living) of the English for Specific Purposes course. The main research objective is to find out whether learners of all motivation types can succeed in this process. Totally, 119 students, prospective teachers from the Faculty of Education and Faculty of Science, participated in the research. The SAMR (Substitution, Augmentation, Modification, Redefinition) model was applied within the smart instruction using smart devices to approach electronic sources and smart methods towards acquiring the learning content. The smart instruction was conducted for 12 weeks (one semester). Two hypotheses were set, and the quasi-experiment and ex-post-facto method were applied. Data referring to learners' motivation types were collected through the standardized Motivation Type Inventory (MTI) by Plaminek. The increase in learners' knowledge was calculated as the difference between entrance and final didactic tests scores. The results did not show statistically significant difference between single motivation types in the topic of Career Development. However, in Healthy Living, the difference was discovered in the group of Coordinators compared to other three types.
The Existence of Minimal Logarithmic Signatures for Some Finite Simple Unitary Groups

The MLS conjecture states that every finite simple group has a minimal logarithmic signature. The aim of this paper is proving the existence of a minimal logarithmic signature for some simple unitary groups PSUn(q). We report a gap in the proof of the main result of Hong et al. (Des. Codes Cryptogr. 77: 179–191, 2015) and present a new proof in some special cases of this result. As a consequence, the MLS conjecture is still open.
CREATION OF AN AUTHORITATIVE DATABASE OF AGRICULTURAL SCIENTISTS OF THE NATIONAL ACADEMY OF SCIENCES OF BELARUS IN THE LIBRARY AUTOMATION SYSTEM IRBIS64&#x0D; (THE EXPERIENCE OF BelAL)

The article presents the main stages of work on the formation of an authority database «Agricultural Scientists of the National Academy of Sciences of Belarus». A process chart for the creation of an authority record for an individual author in IRBIS64 LAS is proposed. The experience of the Belarus Agricultural Library in organizing Web-IRBIS64-based online access to the authoritative database is presented.
Computerised Sentiment Analysis on Social Networks. Two Case Studies: FIFA World Cup 2018 and Cristiano Ronaldo Joining Juventus

Sentiment analysis on social networks plays a prominent role in many applications. The key techniques here are how to identify opinions, to classify sentiment polarity, and to infer emotions. In this study, we proposed a sentence-level sentiment analysis based on a microblogging platform like Twitter. Comprehensive evaluation results on real-world scenarios such as the FIFA World Cup 2018, and the Cristiano Ronaldo’s transfer to Juventus in the summer of 2018 demonstrate a correlation between the polarity (negative or positive) and fan’s sentiments. In addition, the evaluation of several machine learning techniques; applied to identify the polarity and related emotions, revealed that the SVM outperforms other models such as Naive Bayes, ANN, kNN, and Logistic Regression. Additional studies should be addressed to evaluate the proposed system on different sport events, and scenarios.
Discovering Unusual Periodically Human Activity Patterns Through Fuzzy High Utility Rare Itemsets Mining

Abstract Monitoring the resident activities is a key goal to build a safe and convenient environment in a smart house system. One way to capture the activity is by installing motion sensors around the house. By collecting the triggered sensor state, we attempt to find unusual activity patterns to notify suspicious movements or health issues information as our main goals. This study focuses to express the nature of time interval of each activity more clearly using fuzzy set. Next, this study employs fuzzy high utility rare itemsets mining to obtain some odd activities with different quantity of time intervals. Hence, we can determine how peculiar the activity. In this study, we analyzed the public human activity recognition dataset based on each single resident and their activities. As the results, we successfully catch the uncommon activity compared to the daily ones either fishy movements or sick, the illogical movement order, and the false alarm due to the limitation of sensor transmission.
Increasing Security of WebIDs Through Biometrics

We are creating a streamlined way to adapt WebIDs [1], and biometrics [2] to the cyber world. This involves building a user authentication system that enables quick, fast and secure access. It is understood that compared to traditional username and password user authentication, WebIDs are designed to provide such services. Nevertheless, if an intruder either has direct access to the user's computer or somehow gets the unique certificate of the user, important information can be stolen with solely the use of WebIDs. Since biometric data (e.g. fingerprints, iris scanning, etc.) is unique and not easily duplicated, this possibility can be avoided by including biometrics in the authentication process. We also include an enrollment protocol that checks whether a user has a WebID while trying to access a server. If they do, we allow the user access to the server, and if they do not, by accessing their own server, we register the user for a WebID with their permission. Implementing these features in the WebID protocol will greatly enhance user authentication safety.
Protection Setting Validation Concept by Using Expert System

Setting values (parameter values) of equipment (motor, generator, transformer, bus, and line) and system protection coordinations are not optimal (selective) cause a not expected disturbance on an electrical power system. These problems can be seen on many distribution and transmission systems (20 and 70 kV) disturbances that caused circuit breakers of generator tripped, so power plants could not be operated island or house loads. The parameter value problems because there are not protection experts or can use a protection consultant to optimize the parameter values. However, by using the consultant will spend much money. Therefore, for accomplishing the parameter value problems can use an expert system application that works based on knowledgebases of the protection experts, so do not need to spend much money to pay the consultant. The application has to work automatically, so it can eliminate human errors and cost of the consultant. The parameter values will be changed automatically when there are protected equipment or system topology changes.
Hand Gesture Controlled Robot

Due to the recent advancements in digital technologies, every human being has started to believe more in technology, where internet of things [IoT] assists people to remain more accurate in their work. So, IoT controlled car is a robot, which needs to be controlled by using human gestures. The user must wear a gesture device during which the sensor can be included. The hand movement will be recorded by the sensor for a specific direction, and it can end in deploying robotic motion within the respective directions. The robot and gesture instrument are connected wirelessly with help of radio waves. Wireless communication can help to interact with the robot in a more user-friendly way. In this, it commands the car by using accelerometer sensors that are connected to a hand glove. The sensors are intended to exchange the remote, which is generally utilized to run the car. This will allow the user to regulate actions, i.e., forward, backward, leftward, and rightward movements, while using an equivalent accelerometer sensor to regulate the throttle of the car.
A Two-Layer Architecture for Failure Prediction Based on High-Dimension Monitoring Sequences

In recent years, the distributed architecture has been widely adopted by security companies with the rapid expansion of their business. A distributed system is comprised of many computing nodes of different components which are connected by high-speed communication networks. With the increasing functionality and complexity of the systems, failures of nodes are inevitable which may result in considerable loss. In order to identify anomalies of the possible failures and enable DevOps engineers to operate in advance, this paper proposes a two-layer prediction architecture based on the monitoring sequences of nodes status. Generally speaking, in the first layer, we make use of EXPoSE anomaly detection technique to derive anomaly scores in constant time which are then used as input data for ensemble learning in the second layer. Experiments are conducted on the data provided by one of the largest security companies, and the results demonstrate the predictability of the proposed approach.
Clustering Mixed-Type Data with Correlation-Preserving Embedding

Mixed-type data that contains both categorical and numerical features is prevalent in many real-world applications. Clustering mixed-type data is challenging, especially because of the complex relationship between categorical and numerical features. Unfortunately, widely adopted encoding methods and existing representation learning algorithms fail to capture these complex relationships. In this paper, we propose a new correlation-preserving embedding framework, COPE, to learn the representation of categorical features in mixed-type data while preserving the correlation between numerical and categorical features. Our extensive experiments with real-world datasets show that COPE generates high-quality representations and outperforms the state-of-the-art clustering algorithms by a wide margin.
Quality Assurance of Heterogeneous Applications: The SODALITE Approach

A key focus of the SODALITE project is to assure the quality and performance of the deployments of applications over heterogeneous Cloud and HPC environments. It offers a set of tools to detect and correct errors, smells, and bugs in the deployment models and their provisioning workflows, and a framework to monitor and refactor deployment model instances at runtime. This paper presents objectives, designs, early results of the quality assurance framework and the refactoring framework.
Skeleton-bridged Mesh Saliency and Mesh Simplification

This paper presents a local-to-global framework for mesh saliency, which can measure the visual importance in agreement with human perception. Firstly, based on Voronoi diagram, we give a new descriptor which does not only compute the local saliency, but also facilitates the computation of 3D skeleton and global saliency. Then we calculate the global saliency based on skeleton extraction and hierarchical clustering. In addition, an effective denoising method is exploited to guarantee the smoothness of skeletons and clustering accuracy. At last, we put forward a new solution to integrate local and global saliency to obtain a visually better result. Large amounts of models are tested which demonstrate that our results are consistent with human-marked interest points. We also apply the saliency score on mesh simplification to get visually saliency-aware simplification results.
From Models_For_Programming to Modelling_To_Program and Towards Models_As_A_Program

The history of programming languages can be separated into four or five generations. Most languages are nowadays at the level of the third or fourth generation. The fifth generation programme failed mainly due to the infrastructure that has been available at that time. We are going to revive this fifth generation programming efforts by deployment of models as a source code for compilation to programs. Currently models are used as a blueprint or as some inspiration for programmers. At present, we are able to develop an approach of modelling to program. In future, we might have models as programs. This programme will then result in true fifth generation programming. This Ansatz requires models at a higher quality level. Models at this level will become executable if they are precise and accurate. This paper summarises and discusses the vision that models will become programs in future. Models are used for programming as a communication mediator. Modelling as an art will be an integrative part of program development. Mastering modelling as technology will result in modelling as programming. In future, models will become itself programs. We present some of the main issues for research.
Pb isotopic assessment of Khetri Copper Belt, Rajasthan, India and its use as an exploratory tool for ore body extension

A comprehensive lead isotopic database of the Khetri Copper Belt (KCB), Rajasthan, India has been constructed by analyzing the area's ore, minerals, mine water, plant parts, host and country rocks for the evaluation of the deposits in terms of its age of formation and using the database in a preliminary test case of mineral prospecting in neighbouring Haryana. KCB lies in the northeastern part of the Proterozoic Delhi Fold Belt and has been well researched because it houses India's largest copper repository in Madan Kudan, Kolihan and Chandmari mines in its northern part. The lead isotopic profiling conclusively proves repeated metamorphic occurrences in the study area and that Pb in the ore and other rock-forming minerals evolved in a high μ (238U/204Pb) environment of the upper crust. Whereas the 207Pb/206Pb and 208Pb/206Pb ratios of the sulphides of the Madan Kudan and Kolihan lodes of KCB are similar (0.8302–0.8372 and 2.0627–2.0646, respectively), the corresponding values for the Chandmari lode are 0.7209–0.7682 and 1.9463–2.1134. The evolution curves (207Pb/204Pb and 208Pb/204Pb against 206Pb/204 Pb) of KCB sulphides, country rocks, minerals and Indian galena samples, show two mixing lines and that the sulphides are younger (~800 Ma) to the older minerals like calcite, amphiboles and quartz. The first mixing line is formed from the clustering of Indian galena with the older minerals while sulphides and albitites cluster feebly in the second mixing line. There is no consistent correlation between Pb and U concentrations with the age of the different sulphides, country rocks, minerals. The sulphides of East Khetri Belt (EKCB), Muradpur, Bokri-Malwali and Papurna show exceeding variability in 206Pb/204Pb and 207Pb/204Pb ratios, indicating varying degrees of U enrichment of the source region. The mean 208Pb/204Pb, 207Pb/204Pb and 206Pb/204Pb values of the plant roots (37.489, 15.601, 17.614 respectively) are comparable with the corresponding values of mine water (37.138, 15.562, 17.230) indicating that these are isotopically similar. The 207Pb/204Pb and 208Pb/204Pb values of the leached soils from mineralized Golwa in Haryana cluster linearly with the East and North Khetri Copper Belt sulphides along with the soils of unmineralized Islampur-Sareli around 10 km away, thereby indicating the probability of a metallogenic zone in that area.