Reviewerly/exHarmony huggingface dataset:
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts

Many large-scale knowledge bases simultaneously represent two views of knowledge graphs (KGs): an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instantiated from ontological concepts. Existing KG embedding models, however, merely focus on representing one of the two views alone. In this paper, we propose a novel two-view KG embedding model, JOIE, with the goal to produce better knowledge embedding and enable new applications that rely on multi-view knowledge. JOIE employs both cross-view and intra-view modeling that learn on multiple facets of the knowledge base. The cross-view association model is learned to bridge the embeddings of ontological concepts and their corresponding instance-view entities. The intra-view models are trained to capture the structured knowledge of instance and ontology views in separate embedding spaces, with a hierarchy-aware encoding technique enabled for ontologies with hierarchies. We explore multiple representation techniques for the two model components and investigate with nine variants of JOIE. Our model is trained on large-scale knowledge bases that consist of massive instances and their corresponding ontological concepts connected via a (small) set of cross-view links. Experimental results on public datasets show that the best variant of JOIE significantly outperforms previous models on instance-view triple prediction task as well as ontology population on ontology-view KG. In addition, our model successfully extends the use of KG embeddings to entity typing with promising performance.
Recommendation system exploiting aspect-based opinion mining with deep learning method

With the developments of e-commerce websites, user textual review has become an important source of information for improving the performance of recommendation systems, as they contain fine-grained users’ opinions that generally reflect their preference towards products. However, most of the classical recommender systems (RSs) often ignore such user opinions and therefore fail to precisely capture users’ specific sentiments on products. Although a few of the approaches have attempted to utilize fine-grained users’ opinions for enhancing the accuracy of recommendation systems to some extent, most of these methods basically rely on handcrafted and rule-based approaches that are generally known to be time-consuming and labour-intensive. As such, their application is limited in practice. Thus, to overcome the above problems, this paper proposes a recommendation system that utilizes aspect-based opinion mining (ABOM) based on the deep learning technique to improve the accuracy of the recommendation process. The proposed model consists of two parts: ABOM and rating prediction. In the first part, we use a multichannel deep convolutional neural network (MCNN) to better extract aspects and generate aspect-specific ratings by computing users’ sentiment polarities on various aspects. In the second part, we integrate the aspect-specific ratings into a tensor factorization (TF) machine for the overall rating prediction. Experimental results using various datasets show that our proposed model achieves significant improvements compared with the baseline methods.
Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius

This work reports on the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized. The phenomenon can be beneficial to the design of optimal reservoir computing, representing a step forward in understanding these machine-learning systems.
TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection

TASED-Net is a 3D fully-convolutional network architecture for video saliency detection. It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information. As a result, a single prediction map is produced from an input clip of multiple frames. Frame-wise saliency maps can be predicted by applying TASED-Net in a sliding-window fashion to a video. The proposed approach assumes that the saliency map of any frame can be predicted by considering a limited number of past frames. The results of our extensive experiments on video saliency detection validate this assumption and demonstrate that our fully-convolutional model with temporal aggregation method is effective. TASED-Net significantly outperforms previous state-of-the-art approaches on all three major large-scale datasets of video saliency detection: DHF1K, Hollywood2, and UCFSports. After analyzing the results qualitatively, we observe that our model is especially better at attending to salient moving objects.
Incrementally Solving Nonlinear Regression Tasks Using IBHM Algorithm

This paper considers the black-box approximation problem where the goal is to create a regression model using only empirical data without incorporating knowledge about the character of nonlinearity of the approximated function. This paper reports on ongoing work on a nonlinear regression methodology called IBHM which builds a model being a combination of weighted nonlinear components. The construction process is iterative and is based on correlation analysis. Due to its iterative nature, the methodology does not require a priori assumptions about the final model structure which greatly simplifies its usage. Correlation based learning becomes ineffective when the dynamics of the approximated function is too high. In this paper we introduce weighted correlation coefficients into the learning process. These coefficients work as a kind of a local filter and help overcome the problem. Proof of concept experiments are discussed to show
INFLUENCE OF DIFFUSIVE SURFACE POSITION AND EXTENSION ON THE ACOUSTICS OF A SMALL PERFORMANCE SPACE

Diffusive surfaces have been used in different environments and several studies have been carried out to better understand their positive and negative effects through in-field 1,2 and physical-scale 3-6 measurements, simulations and perceptual tests 3,7,8. However, very little research has been carried out in real concert halls due to many practical problems, that is technical and economical.In this study, it was possible to investigate the effects of diffusive surfaces in a real shoebox concert hall with variable acoustics.In-situ measurements conducted by Jeon et al.1 demonstrated that early decay time (EDT) and interaural cross correlation (IACC) are the most affected parameters by the use of diffusive surfaces.Furthermore, Kim et al.2 found that the effects of scattered sound on acoustic parameters depend on the receiver position.Several studies have been conducted to determine the effects of diffusive surfaces by using scale models.Ryu and Jeon 3 and Kim et al. 4 found that hemispherical diffusers, applied to scale model surfaces, decrease reverberation time (RT) and early decay time (EDT).Different results were found for clarity (C 80 ), which increased at the front seats and decreased at the rear seats 3
Integrating Learner Help Requests Using a POMDP in an Adaptive Training System

&#x0D; &#x0D; &#x0D; This paper describes the development and empirical testing of an intelligent tutoring system (ITS) with two emerging methodologies: (1) a partially observable Markov decision process (POMDP) for representing the learner model and (2) inquiry modeling, which informs the learner model with questions learners ask during instruction. POMDPs have been successfully applied to non-ITS domains but, until recently, have seemed intractable for large-scale intelligent tutoring challenges. New, ITS-specific representations leverage common regularities in intelligent tutoring to make a POMDP practical as a learner model. Inquiry modeling is a novel paradigm for informing learner models by observing rich features of learners’ help requests such as categorical content, context, and timing. The experiment described in this paper demonstrates that inquiry modeling and planning with POMDPs can yield significant and substantive learning improvements in a realistic, scenario-based training task.&#x0D; &#x0D; &#x0D;
Pluralism and Hybridity in instant poems: Taxonomizing Indonesian students' degree of nationalism through their creative process

Poetry can be a device that is rich in meaning in responding to any issues, including pluralism issue. The aim of this research is to elaborate students’ attitudes expressed in their poem on pluralism issues in Indonesia. This research is also conducted to identify and group the responses into three attitudes: positive, negative, and neutral, and into five hybrid categories proposed by Steven G. Yao: Cross-fertilization, Mimicry, Grafting, Transplantation, and Mutation. The research is conducted in five private universities in Jakarta and Tangerang: Bina Nusantara University, Pamulang University, Nasional University, Indonesian Christian University, and Indonesian Al-Azhar University. The result of the research shows that out of 153 poems, most students (65%) have positive attitude toward pluralism. While 16 % of them have negative attitude and only 19% of the respondents have neutral or ambiguous attitude. Interestingly, 36 students (23.52%) use the word “tunngal/one” as a metaphoric medium to express their response, while 33 (21.56%) students use colors to represent their thoughts, 28 students (18.30%) use “bhinneka / diversity” to show their responses, and there are 10 students (6.53%) use “rainbow” to signify pluralism issues. There are strong metaphors found effective in reflecting the students’ ideas such as garden, music, jar, batik, Garuda, salad, and sandals. The strong metaphors, studied in Yao’s hybridization, show all the five degrees of hybrid interaction: two cross-fertilizations, one mimicry, three graftings, three transplantations, and three mutations. It is concluded that the students have positively owned a high degree sense of nationalism in responding to Indonesian plural realities.
The Role of Cross – Training in Reducing the Level of Labor Turnover Applied Study on Some Hotels in Tripoli-Libya (2022-2023)

Cross-Training is an effective training technology which results in employee motivation and problemsolving. A good cross-training plan can encourage employees to learn more about all sides of the business and is able to jump in to help anytime, anywhere when other departments need to be supported. The tourism industry is a labor-intensive service industry dependent for survival and competitive advantage on the availability of good quality personnel to deliver, operate and manage the tourist product. Cross-training is presented as a learning opportunity whose main purpose is to build employee motivation, reduce turnover and increase productivity for industries. The interaction between tourist and tourism industry personnel is an integral part of the total tourist experience. Generally speaking, hospitality industry has one of the highest levels of skill shortages and Libya is no exception. This paper focuses on skills relating to cross training in hotels industry. The paper considers the key arguments from Libya's perspective in the context of hotels industry regarding cross training and its effect on labor turnover .Three hotels have been selected for the study by distributing and collecting questionnaire on 91 employees. The Statistical Package for the Social Sciences (SPSS) was used to analyze the data. Both hypothesis of the study were tested. The first one indicated that there is a negative relationship between the level of per capita income and labor turnover and the other one tested if cross training has an effect on labor turnover rates. The effect of employee's income on labor turnover has been tested, in order to find out if employees'income is the only factor effects labor turnover, or cross training is another factor which has its effect on labor turnover too. Labor turnover average in selected hotels illustrated. Justifications of labor turnover will be tested and solutions for reducing labor turn over will be presented.
Exploring the power of supervised learning methods for company name disambiguation in microblog posts

Twitter is an online social networking website where people can post short messages on any subject, and these messages become visible to other users. Users intentionally express their opinions about companies or products via microblogging texts. Analyzing such messages might help explore what customers think about company products, or what the broad feelings of customers are. Identifying tweets referring to products and companies is becoming an important tool recently. However, company names are often vague. Hence, the first step is to locate the messages that are relevant to a company. In this paper, we present a number of supervised learning techniques to decide whether a given tweet is about a company, e.g., whether a message containing the term \textquoteleft amazon\textquoteright is related to the company Amazon Inc. or not. Solving this task is challenging in comparison to the classical classification process. The main difficulty with this problem is that tweets and company names include limited information. To make this task tractable, external resources are used to get richer data about a company. More specifically, we generate several profiles for each organization, which contain richer information. Then we perform feature extraction to obtain both numerical and categorical features and we do feature selection to identify the most relevant attributes with our task. Finally, we train several supervised classifiers. Our constructed classifiers and carefully selected features provide high accuracy on the WePS-3 dataset. Our results show considerable improvement of accuracy by 11 % over baseline approaches.
Computers as Assistants—Introduction and Overview

“The major metaphor shift in computing will be toward programs that act like assistants rather than tools: they will show more initiative, assume responsibility for larger sub tasks, and take appropriate risks.” Research on computers as assistants means looking for new ways of dividing the labor between humans and computers. Future systems should take on more tasks than existing systems do, especially those that seem tedious or difficult for humans. An important requirement for assisting systems is the ability to adapt to users’ individual styles and tasks. Two forms of adaptation should be distinguished here: one that occurs on the user’s initiative, and the other initiated by the system itself. System adaptivity is especially desirable in the offering of help when the user is having difficulties. Help facilities should be adjusted according to the current dialogue situation and the individual user. Supplying systems with domain knowledge is the goal of conventional expert systems.
ActiveTracker: Uncovering the Trajectory of App Activities over Encrypted Internet Traffic Streams

Despite the increasing popularity of mobile applications and the widespread adoption of encryption techniques, mobile devices are still susceptible to security and privacy risks. In this paper, we propose ActiveTracker, a new type of sniffing attack that can reveal the fine-grained trajectory of user’s mobile app usage from a sniffed encrypted Internet traffic stream. It firstly adopts a sliding window based approach to divide the encrypted traffic stream into a sequence of segments corresponding to different app activities. Then each traffic segment is represented by a normalized temporal-spacial traffic matrix and a traffic spectrum vector. Based on the normalized representation, a deep neural network (DNN) classification algorithm is developed to recognize the crucial activities conducted with different apps by the user. We show by extensive experiments on real-world app usage traffic collected from volunteers that the proposed approach achieves up to 78.5% accuracy in recognizing app trajectory over encrypted traffic streams.
Crime Data Analysis in Python using K - Means Clustering

Crime is one of the biggest hurdle in today's world and its increasing at the fire pace which is a major cause for concern. There is a need to monitor and keep track of all the crimes so that it can be used by police department to investigate the cases easily and quickly. In this experiment, a machine learning K-means algorithm is used to predict and analyze the crime in the town of Chicago. The dataset of crimes in Chicago is available at the kaggle website, that is used as the dataset to make prediction and to visualize the patterns and trends of different crimes. The other purpose of this project is to evaluate how much k -means algorithm is feasible to determine and solve the current problem.
Impact of using Agile Methods in Software Engineering Education: A Case Study

Agile methods have proved their applicability and efficiency in software development. Advantages of using agile methods include short development life-cycle times; multidisciplinary development teams; continuous evolution, immediacy, and reduced cost of change. Although some universities started including these methods in their curriculum, courses on practical practices for Agile methods at the undergraduate and graduate levels are still few. In this paper, an investigation is conducted to evaluate the impact of teaching practical agile methods in web engineering course on the learning outcomes of the software engineering students. This case study started by conducting a real-life business simulation inside a computer lab. Then, a questionnaire was prepared and disseminated in order to obtain the feedback on the students' perception on this teaching method. Afterward, the questionnaire results were analyzed and assessments for students were done to find out the impact of this educational technique on students' performance.
Implementation of Certainty Factor Method for Diagnoses of Photocopy Machine Damage

Abstract Photocopying machines are machines that make paper copies of documents and other visual images quickly and cheaply. PT. TRITANU has a copy machine product, such as the Fotocpy Sharp machine Type B201D. The rapid development of photocopying technology at this time can be seen that adult photocopying machines are now not only operating copier but can scan, print, and even fax. The rapid development of photocopying technology does not necessarily provide complete convenience to users. This can be seen from the high cost of consultation in the event of damage to the photocopy machine, requiring time to check the damaged machine, and not to mention other procedures in the repair that exist at PT. TRANANU. So from to provide solutions to these problems, the researchers made an Expert System for Handling Print Results Problems on Sharp Photocopying Machines Type B201D Using the Certainty Factor Method. System testing shows that the system is able to diagnose damage to the B201D copy machine based on the symptoms in the printout, even though these symptoms contain uncertainty. The results of the diagnosis are accompanied by Certainty Factor values that indicate the level of truth, accuracy of possible damage to the B201D copy machine.
Prediction of Tourist Behaviour: Tourist Visiting Places by Adapting Convolutional Long Short-Term Deep Learning

The trend of industry tourism GDP is increasing in every year that speculates from statistics of the World Travel & Tourism Council (2018). Moreover, travel industry not only considered as the most dynamic sector but also the most importance generator of income and jobs in the country. Thus, the prototype for tourism plans are needed for strategic planning. Currently, social web is a great tool for providing useful insights about tourist behaviors especially with the text data that comes from travelers' opinions. In this work, we use sequential patterns of users' behavior which are ordered by time from tourist including opinions, reviews as our input data. Then, we use Convolutional Long Short-Term Deep Learning (CLSTDL) which is a deep learning technique that combines Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) to predict the expected location. During the process, the output of CNN will be fed into LSTM to learn the sequence pattern behavior of traveler. The model output is then used to predict the next location that particular travelers are likely to go. The experimental results have shown that CLSTDL outperforms other models when evaluating with the accuracy and loss metrics.
Java Stream Fusion

In this paper, we show how stream fusion, a program transformation technique used in functional programming, can be adapted for an Object-Oriented setting. This makes it possible to have more Stream operators than the ones currently provided by the Java Stream API. The addition of more operators allows for a greater deal of expressiveness. To this extent, we show how these operators are incorporated in the stream setting.
Product Recommendations Using Textual Similarity Based Learning Models

Recommendation systems are achieving great success in e-Commerce applications, during a live interaction with a customer; recommendation system may apply different techniques to solve the problem of making a correct and relevant product recommendation. The main objective of this research is to perform product recommendation using textual similarity based Learning model. In this research data acquired through Amazon product advertising API after Data cleaning and text preprocessing the content based product recommendation have been performed using Bag of Words(BOW) and Term Frequency-Inverse Document Frequency(TF-IDF) based text vectorization techniques. Textual Description of the product converted into n-dimensional vector, and later the Euclidean similarity can be measured between the ndimensional vector of the queried product and other products. Text-based product similarity through text vectorization technique is very useful in performing content-based product recommendation and recommending the similar item to the user it can be used in various E-Commerce applications since these applications are heavily populated with the textual description of the product. Bag of words and TF-IDF generates an n-dimensional vector of a different textual description of the product which in turn leads to a better recommendation of the product. Experimental results and analysis section clearly describes how the proposed model for text-based product find the similarity of products with queried product and display as output the best-recommended product.
Semantic Unsupervised Learning for Word Sense Disambiguation

The identification of the particular meaning for a word based on the context of its usage is commonly referred to as Word Sense Disambiguation (or WSD). Although considered a complex task, WSD is an important component of language processing and information analysis systems in several fields. Current methods for WSD rely on human input and are usually limited to a finite set of words. Complicating matters further, language is dynamic with (current) word usage changes and the introduction of new words. Static definitions created by previously defined analyses become outdated or are inadequate to deal with current usage. Fully automated methods for WSD are needed both for sense discovery and for distinguishing the sense being used for a word in context. Latent Semantic Analysis (LSA) is a candidate automated unsupervised learning system that has not been widely applied in this area. In this chapter, advanced LSA techniques are deployed as an unsupervised learning approach to the WSD tasks of sense discovery and distinguishing senses in use.