Miracle-dz/UniFAR-FTUs huggingface dataset:
ENHANCED PREDICTION OF HEART DISEASE WITH FEATURE SUBSET SELECTION USING GENETIC ALGORITHM

Clinical diagnosis is done mostly by doctor’s expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis. In many cases, not all the tests contribute towards effective diagnosis of a disease. The objective of our work is to predict more accurately the presence of heart disease with reduced number of attributes. Originally, thirteen attributes were involved in predicting the heart disease. In our work, Genetic algorithm is used to determine the attributes which contribute more towards the diagnosis of heart ailments which indirectly reduces the number of tests which are needed to be taken by a patient. Thirteen attributes are reduced to 6 attributes using genetic search. Subsequently, three classifiers like Naive Bayes, Classification by clustering and Decision Tree are used to predict the diagnosis of patients with the same accuracy as obtained before the reduction of number of attributes. Also, the observations exhibit that the Decision Tree data mining technique outperforms other two data mining techniques after incorporating feature subset selection with relatively high model construction time. Naive Bayes performs consistently before and after reduction of attributes with the same model construction time. Classification via clustering performs poor compared to other two methods.
Measuring the Value of Linguistics: A Case Study from St. Lawrence Island Yupik

The adaptation of neural approaches to NLP is a landmark achievement that has called into question the utility of linguistics in the development of computational systems. This research proposal consequently explores this question in the context of a neural morphological analyzer for a polysynthetic language, St. Lawrence Island Yupik. It asks whether incorporating elements of Yupik linguistics into the implementation of the analyzer can improve performance, both in low-resource settings and in high-resource settings, where rich quantities of data are readily available.
IP Flow Mobility Trigger Mechanism in Heterogeneous Wireless Networks

With the Multi Access Packet Data Network Connectivity and IP flow mobility introduced by 3GPP, mobile devices with multiple wireless interfaces can connect to multiple access networks simultaneously, meanwhile allowing dynamic allocation of different IP flows to different access systems corresponding to their requirements. In order to enable IP flow mobility, an effective IP flow mobility trigger mechanism is indispensible. However, among the existing technical standard and current literatures in IP flow mobility, almost no research involves the trigger mechanism and decision algorithm. This paper proposes a mechanism for network-based IP flow mobility, which includes a trigger process and a network selection decision algorithm based on each IP flow. Simulation results validate the flexibility and efficiency of our proposed mechanism, which can achieve a better network load balancing as well as a higher user satisfaction.
Joint Network Admission Control, Mode Assignment, and Power Allocation in Energy Harvesting Aided D2D Communication

Green communication with sustainable energy is being considered for 5G cellular network and Internet of Things (IoT) mainly with focus on energy harvesting (EH) to prolong network lifetime. Moreover, device-to-device (D2D) communication on shared channels is also considered as a promising technology to achieve high data rates, ultralow latency communication, and high spectral efficiency. In this paper, we investigated resource allocation in EH-aided D2D communication underlying 5G cellular along with enabling IoT services. The objective is to maximize throughput of the network subject to the joint constraints on user performance, number of admitted users for equity and fair usage, mode assignment (cellular or D2D) as per available energy, and transmit power allocation along with EH techniques, which results in a mixed integer nonlinear programming problem. We have proposed a low complexity and efficient algorithm, adaptive resource allocation and energy sentient network (ARA-ESN) using branch–cut, branch–bound, and mesh adaptive direct search (MADs) solutions, where cellular or D2D communication is based on available energy and user performance criteria along with EH through ambient energy and radio frequency (RF) energy transfer techniques. We have applied the outer approximation based linearization technique that guarantees the convergence to the optimal solution. The results show that ARA-ESN branch–cut outperforms ARA-ESN branch–bound and ARA-ESN MADs. Moreover, we have also observed that ambient harvesting increases performance of network due to better acquisition of energy as compared to RF energy transfer.
Feedback Shaping: A Modeling Approach to Nurture Content Creation

Social media platforms bring together content creators and content consumers through recommender systems like newsfeed. The focus of such recommender systems has thus far been primarily on modeling the content consumer preferences and optimizing for their experience. However, it is equally critical to nurture content creation by prioritizing the creators' interests, as quality content forms the seed for sustainable engagement and conversations, bringing in new consumers while retaining existing ones. In this work, we propose a modeling approach to predict how feedback from content consumers incentivizes creators. We then leverage this model to optimize the newsfeed experience for content creators by reshaping the feedback distribution, leading to a more active content ecosystem. Practically, we discuss how we balance the user experience for both consumers and creators, and how we carry out online A/B tests with strong network effects. We present a deployed use case on the LinkedIn newsfeed, where we used this approach to improve content creation significantly without compromising the consumers' experience.
Developing the resilient solar energy management system: a hybrid qualitative-quantitative approach

Nowadays, managers in the solar energy sector need to develop a resilient and sustainable management system to deal with challenges as well as improving the quality of their organisation’s performance. Therefore, the purpose of this study is to present an integrated framework as a tool for developing a resilient solar energy management system (RSEMS). In order to obtain valid results, intuitionistic fuzzy DEMATEL-DEA, a qualitative-quantitative approach is implemented. Twenty-four enabling factors are identified to be used in the intuitionistic fuzzy DEMATEL methodology. Then, 30 enabling strategies are identified to be employed in the DEA methodology based on the inputs and outputs determined in previous steps. An intuitionistic fuzzy method is an effective tool for the analysis process. The results reveal that among enabling factors and strategies, some have more considerable effects on developing the RSEMS than the others.
Comments on "A carry-free 54 b×54 b multiplier using equivalent bit conversion algorithm"

For original paper see ibid., vol. 36, no. 10, p. 1538-1545 (Oct. 2001). In the aforementioned paper by Kim et al., a multiplier is presented which produces the result in radix-2 signed-digit representation. It is claimed that this representation can be converted into conventional magnitude representation by an algorithm which has no carry propagation. To the commenters this algorithm seems incorrect. The critical situation is a string which consists of a sequence of zeros followed by a -1; in such a case a carry is needed and the algorithm proposed is deemed incorrect. Consequently, it is pointed out that the proposed algorithm produces a correct multiplication result in conventional magnitude representation only if the signed-digit string does not have a sequence of 0's followed by a -1. The commenters show a multiplication example using the proposed conversion algorithm in which this situation occurs.
Mechanics of ELM control coil induced fast particle transport in ITER

Using the orbit-following code ASCOT, we have modelled fast ion transport in ITER under the influence of ELM control coils (ECCs), toroidal field ripple, and test blanket modules, with emphasis on how the plasma response (PR) modifies the transport mechanisms and fast ion loads on the divertor. We found that while PR shields the plasma by healing broken flux surfaces at the plasma periphery, it also opens a new loss channel for marginally trapped particles: PR causes strong toroidal variation of the poloidal field near the X-point which leads to de-localisation of banana tips and collisionless transport. The reduction in passing particle losses and the increase in marginally trapped particle losses shift divertor loads from targets to the dome and under-the-dome structures. The plasma response was calculated by both MARS-F and JOREK codes. The new transport mechanism was stronger for PR calculated by JOREK which, unlike MARS-F, explicitly includes the X-point.
Improving Webpage Access Predictions Based on Sequence Prediction and PageRank Algorithm

Aim/Purpose In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% Improving Webpage access Predictions 28 and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendations for Researchers The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.
Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment

When a speaker, Mary, asks “Do you know that Florence is packed with visitors? ”, we take her to believe that Florence is packed with visitors, but not if she asks “Do you think that Florence is packed with visitors? ”. Inferring speaker commitment (aka event factuality) is crucial for information extraction and question answering. Here, we explore the hypothesis that linguistic deficits drive the error patterns of existing speaker commitment models by analyzing the linguistic correlates of model error on a challenging naturalistic dataset. We evaluate two state-of-the-art speaker commitment models on the CommitmentBank, an English dataset of naturally occurring discourses. The CommitmentBank is annotated with speaker commitment towards the content of the complement (“Florence is packed with visitors” in our example) of clause-embedding verbs (“know”, “think”) under four entailment-canceling environments (negation, modal, question, conditional). A breakdown of items by linguistic features reveals asymmetrical error patterns: while the models achieve good performance on some classes (e.g., negation), they fail to generalize to the diverse linguistic constructions (e.g., conditionals) in natural language, highlighting directions for improvement.
Leveraging Meta Information in Short Text Aggregation

Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.
Development of a skin temperature measuring system for non-contact stress evaluation

Skin temperature is an effective indicator for objectively evaluating human sensations, because it is controlled by sympathetic nerve activity which reflects the course of information processing in the brain. In this paper, the authors show a method to evaluate stress from skin temperature and an equipment which continuously measures skin temperature of an subject working in front of a computer terminal. An experiment is performed to investigate a relationship between stressful task and the skin temperature. The experiment shows that there is a high correlation among stress, skin temperatures on nose and forehead. From this experiment, a regression equation is derived which computes the intensity of stress from skin temperatures on nose and forehead. A non-contact skin temperature measuring system is developed based on knowledge obtained in the experiment. The system comprised of an infrared camera, color camera, image processing unit and workstation. The features are the abilities to track nose and forehead positions of an subject doing computer operation automatically and to evaluate the stress continuously.
Irish dependency treebanking and parsing

Despite enjoying the status of an official EU language, Irish is considered a minority language. As with most minority languages, it is a `low-density' language, which means it lacks important linguistic and Natural Language Processing (NLP) resources. Relative to better-resourced languages such as English or French, for example, little research has been carried out on computational analysis or processing of Irish. Parsing is the method of analysing the linguistic structure of text, and it is an invaluable processing step that is required for many different types of language technology applications. As a verb-initial language, Irish has several features that are uncharacteristic of many languages previously studied in parsing research. Our work broadens the application of NLP methods to less studied language structures and provides a basis on which future work in Irish NLP is possible. We report on the development of a dependency treebank that serves as training data for the first full Irish dependency parser. We discuss the linguistic structures of Irish, and the motivation behind the design of our annotation scheme. Our work also examines various methods of employing semi-automated approaches to treebank development. We overcome the relatively small pool of linguistic and technological resources available for the Irish language with these approaches, and show that even in early stages of development, parsing results for Irish are promising. What counts as a sufficient number of trees for training a parser varies according to languages. Through empirical methods, we explore the impact our treebank's size and content has on parsing accuracy for Irish. We also discuss our work in crosslingual studies through converting our treebank to a universal annotation scheme. Finally we extend our Irish NLP work to the unstructured user-generated text of Irish tweets. We report on the creation of a POS-tagged corpus of Irish tweets and the training of statistical POS-tagging models. We show how existing resources can be leveraged for this domain-adapted resource development.
Twitter Homophily: Network Based Prediction of User’s Occupation

In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user’s occupational class. We show that the content information of a user’s tweets, the profile descriptions of a user’s follower/following community, and the user’s social network provide useful information for classifying a user’s occupational group. In our study, we extend an existing data set for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work. In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this data set with just a small fraction of the training data.
Learning to Abstract for Memory-augmented Conversational Response Generation

Neural generative models for open-domain chit-chat conversations have become an active area of research in recent years. A critical issue with most existing generative models is that the generated responses lack informativeness and diversity. A few researchers attempt to leverage the results of retrieval models to strengthen the generative models, but these models are limited by the quality of the retrieval results. In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation. Our model clusters query-response samples, extracts characteristics of each cluster, and learns to utilize these characteristics for response generation. Experimental results show that our model outperforms other competitive baselines.
Learning from Omission

Pragmatic reasoning allows humans to go beyond the literal meaning when interpret- ing language in context. Previous work has shown that such reasoning can improve the performance of already-trained language understanding systems. Here, we explore whether pragmatic reasoning during training can improve the quality of learned meanings. Our experiments on reference game data show that end-to-end pragmatic training produces more accurate utterance interpretation models, especially when data is sparse and language is complex.
Learner autonomy versus guided reflection: How different methodologies affect intercultural development in online intercultural exchange

Abstract This paper presents the results of a study exploring the intercultural development of first-year Japanese university students engaged in online intercultural exchange (OIE) using two variations: one implementing guided reflection, and the other relying on the learner autonomy model. Intercultural development was quantitatively measured using the Intercultural Sensitivity Scale (Chen & Starosta, 2000) and qualitatively investigated through participants’ written reflections. Results of the OIE using guided reflection showed significant gains in respect for the target culture, whereas the OIE that followed the autonomous learning model yielded significant gains in self-efficacy in relation to intercultural contact. Qualitative analysis of student reflections confirmed these findings and provided insight into the processes involved in achieving these results.
Automatic Number Plate Recognition Using Smart Phones

This work develops a smart phone based system featuring license plate recognition. This system features the detection and recognition of license plate using smart phones. Using smart phone the image of a license plate is taken and the user gets to know whether the license plate is original, it also provides the details of the vehicle and the details of the owner of a vehicle. This system has been combined in three self developed novel methods, those are license plate extraction and segmentation using vertical and horizontal histogram, Ad-Hoc based remote motion control system, character recognition using artificial neural network. After implementation we can show the license plate detection rate and recognition rate of the Smart Phone based System are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about few seconds per frame on the Smart Phone-based System.
Modeling Dwell Time Engagement on Visual Multimedia

Visual multimedia is one of the most prevalent sources of modern online content and engagement. However, despite its prevalence, little is known about user engagement with such content. For instance, how can we model engagement for a specific content or viewer sample, and across multiple samples? Can we model and discover patterns in these interactions, and detect outlying behaviors corresponding to abnormal engagement? In this paper, we study these questions in depth. Understanding these questions has implications in user modeling and understanding, ranking, trust and safety and more. For analysis, we consider content and viewer dwell time (engagement duration) behaviors with images and videos on Snapchat Stories, one of the largest multimedia-driven social sharing services. To our knowledge, we are the first to model and analyze dwell time behaviors on such media. Specifically, our contributions include (a) individual modeling: we propose and evaluate the ŁFmodel, ŁTmodel and \Vmodel parametric models to describe dwell times of unlooped/looped media and viewers which outperform alternatives, (b) aggregate modeling: we show how to flexibly summarize the respective joint distributions of multivariate parametrized fits across many samples using Vine Copulas in the analog \ALFmodel, \ALTmodel and \AVmodel models, which enable inferences regarding aggregate behavioral patterns, and offer the ability to simulate real-looking engagement data (c) anomaly detection: we demonstrate our aggregate models can robustly detect anomalies present during training ($0.9+$ AUROC across most attack models), and also enable discovery of real dwell time anomalies.