Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for Dark Particles and Dark Sector at Belle

We search for dark photon $A^\prime$ and the dark Higgs boson $h^\prime$ particles that are suggested in number of recently proposed dark sector models. We present our search results in the production of $e^+e^- \rightarrow A^\prime h^\prime$ with the decay mod of $h^\prime \rightarrow A^\prime A^\prime$. The search was carried out in both inclusive and exclusive modes. We also discuss a search for new dark vector gauge boson that couples to light quark predominantly.
Spam comments prediction using stacking with ensemble learning

Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.
Combined approach for VLSI placement problem

The work discusses a stage of electronic computing equipment automated design VLSI fragments placement. This task belongs to the NP-complex class of problems. The paper presents the statement of the VLSI placement problem. It is proposed an "evolution" "search" strategy. A new search architecture based on the proposed strategy is constructed. The principal difference of this approach is the division of the search process into two stages and the application of different methods on each of them. At the first search stage it is used a genetic algorithm. At the second search stage an annealing simulation algorithm is proposed, which allows to improve the result found by the genetic algorithm. Based on this approach, a combined algorithm has been developed. A computational experiment was carried out on test examples (benchmarks). The quality of the obtained placement is on average 3.5% higher than the placement results obtained by using the well-known Dragon 2.23 algorithm. Series of tests and experiments were carried out and showed the promise of this approach. Time complexity of the developed algorithm is represented as O (n). Keyword— VLSI placement, annealing simulation, genetic algorithm, combined approach.
A 1D Spectral Image Validation/Verification Metric for Fingerprints

Image validation and verification are important functions in the acquisition of fingerprint images from live-scan devices and for assessing and maintaining the fidelity of fingerprint image databases. Such databases are used by law enforcement agencies, for which data integrity is paramount, and many hours must be devoted to visual inspection of images. In addition, such databases are used by the National Institute of Standards and Technology (NIST) and others to test automated fingerprint identification system (AFIS) algorithms and to aide the advance of this technology. We propose a comparatively simple computational mechanism by which to screen fingerprint image databases for specimens improperly scanned from fingerprint cards, guide the auto-capture process and flag auto-capture failures, identify non-fingerprint images that may have been included in a database, and recognize aberrant sampling of fingerprint images. The scheme reduces an input image to a 1-dimensional power spectrum that makes explicit the characteristic ridge structure of the fingerprint that on a global basis differentiates it from most other images. The magnitude of the distinctive spectral feature, related directly to the distinctness of the level 1 ridge flow, provides a primary diagnostic indicator of the presence of a fingerprint image. The frequency of the spectral feature provides a secondary classification metric and, on a coarse level, indicates the scan sample rate of the fingerprint image. Test results are reported in which the Spectral Image Validation and Verification (SIVV) utility is applied to a variety of databases composed of fingerprint and non-fingerprint images. Using only the peak height and frequency limit as simple classification criteria, the SIVV utility achieves an equal error rate (EER) for false positive and false negative classifications of 10 % for fingerprints mixed with a variety of non-fingerprint images, including many chosen to exhibit periodic structure similar to that of a fingerprint. An EER of around 7 % is found with a dataset containing fingerprints mixed with other biometric samples, i.e. face and iris images.
Argo as a platform for integrating distinct biodiversity analytics tools into workflows for building graph databases

Together with the increasingly growing amount of available data on biodiversity comes the proliferation of various informatics tools aimed at the collection, management and analysis of biodiversity-relevant knowledge. Consequently, we have seen how several data formats and programming languages or environments have come into use, giving rise to a problem in interoperability should anyone wish to combine the outputs of distinct tools, or to integrate them into one solution. Argo (Rak et al. 2012), an online text mining workbench based on the Unstructured Information Management Architecture (UIMA) interoperability standard, offers a means for seamlessly unifying various tools and resources into customisable text processing workflows. Among many other features, Argo provides: (1) a library of diverse tools, i.e., UIMA components, each of which is dedicated to a specific task such as loading datasets or gazetteers of interest (e.g., the Biodiversity Term Inventory), recognition of species names and their semantically related terms (Nguyen et al. 2017); (2) a graphical interface for designing workflows using components as building blocks; (3) an environment for executing and monitoring the progress of workflows; and (4) a user-interactive annotation editor for manually revising or validating results of automated processing. ‡ ‡ ‡ § ‡ © Batista-Navarro R et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Recently, Argo has been extended to provide support for incorporating into workflows external web services conforming with the Representational State Transfer (REST) protocol. Taking advantage of these features, we demonstrate how we combine inhouse tools and resources for named entity recognition (Batista-Navarro et al. 2017) with externally developed ones, e.g., EXTRACT (Pafilis et al. 2016), in order to build text mining workflows for populating neo4j graph databases with biodiversity-relevant knowledge. To provide a few exemplars, we focus on use cases that seek to leverage various sources of literature to capture fine-grained information on the habitat and reproductive conditions of: (1) a subset of plants catalogued in World Flora Online (Jackson and Miller 2015), and (2) tropical trees belonging to the Dipterocarpaceae family.
Multi-channel feature dictionaries for RGB-D object recognition

Hierarchical matching pursuit (HMP) is a popular feature learning method for RGB-D object recognition. However, the feature representation with only one dictionary for RGB channels in HMP does not capture sufficient visual information. In this paper, we propose multi-channel feature dictionaries based feature learning method for RGB-D object recognition. The process of feature extraction in the proposed method consists of two layers. The K-SVD algorithm is used to learn dictionaries in sparse coding of these two layers. In the first-layer, we obtain features by performing max pooling on sparse codes of pixels in a cell. And the obtained features of cells in a patch are concatenated to generate patch jointly features. Then, patch jointly features in the first-layer are used to learn the dictionary and sparse codes in the second-layer. Finally, spatial pyramid pooling can be applied to the patch jointly features of any layer to generate the final object features in our method. Experimental results show that our method with first or second-layer features can obtain a comparable or better performance than some published state-of-the-art methods.
Dynamic programming for valuing American options under a variance‐gamma process

Levy processes provide a solution to overcome the shortcomings of the lognormal hypothesis. A growing literature proposes the use of pure‐jump Levy processes, such as the variance‐gamma (VG) model. In this setting, explicit solutions for derivative prices are unavailable, for instance, for the valuation of American options. We propose a dynamic programming approach coupled with finite elements for valuing American‐style options under an extended VG model. Our numerical experiments confirm the convergence and show the efficiency of the proposed methodology. We also conduct a numerical investigation that focuses on American options on S&P 500 futures contracts.
Developing and Implementing A System for Shipping Companies Comparison

Information-intensive Web services such as shipping comparison sites have recently been gaining popularity. However, most users including novice shoppers have difficulty in browsing such sites because of the massive amount of information gathered and the uncertainty surrounding Web environments. The aim of this research is to design a system which is called Shohnati to perform all procedures related to the order of shipment, and to store and process all information relating to customers or shipping companies in a database. Through this research, the customers will be able to order the shipment more easily by providing a complete comparison between the shipping companies, request the shipment from the preferred company's site, follow the shipment, and follow the latest offers of companies on our site. The proposed system was developed using the Unified Modeling Language (UML) and Visual Studio-ASP.NET programming language.
Factors constraining Grade 12 learners’ achievement in Sesotho Home Language

This study was undertaken upon the realisation that Grade 12 learners at a Secondary School in the Xhariep District throughout the years continued to promote Sesotho Home Language, however, their academic performance in the subject is disquieting. Given that learners have been proficient in Sesotho and have been using the language for purposes such as elicitation of knowledge and communication for almost two decades, the expectation is that learner’s cognitive knowledge of the language is sufficient to have learners achieve more that it is apparent. This study set out to find out both linguistic and extra-linguistic factors constraining meritorious learner’ achievement in Sesotho Home Language in Grade 12. The study employed a qualitative research approach, using three varying methods for data elicitation: questionnaires, interviews and observation. The data for this study was gathered from learners, parents and a single teacher. The study found out that factors impeding meritorious achievement in SHL include amongst others the inability of parents to voluntarily and constantly help learners with school work, learners attitude towards SHL, the unavailability of reference sources for learners, and linguistic factors such as learners tendency of using multi-codes in their speech has affected their ability to maintain monolingual speech especially in SHL lessons, thereby limiting learners’ Sesotho vocabulary. It has also been established that using methods such as code switching and code mixing have negative impact on learner’s education for the language under study. Recommendations aimed at addressing identified factors have also been proposed.
Reducing the Cost of Heuristic Generation with Machine Learning

The space of compile-time transformations and or run-time options which can improve the performance of a given code is usually so large as to be virtually impossible to search in any practical time-frame. Thus, heuristics are leveraged which can suggest good but not necessarily best configurations. Unfortunately, since such heuristics are tightly coupled to processor architecture performance is not portable; heuristics must be tuned, traditionally manually, for each device in turn. This is extremely laborious and the result is often outdated heuristics and less effective optimisation. Ideally, to keep up with changes in hardware and run-time environments a fast and automated method to generate heuristics is needed. Recent works have shown that machine learning can be used to produce mathematical models or rules in their place, which is automated but not necessarily fast. This thesis proposes the use of active machine learning, sequential analysis, and active feature acquisition to accelerate the training process in an automatic way, thereby tackling this timely and substantive issue. First, a demonstration of the efficiency of active learning over the previously standard supervised machine learning technique is presented in the form of an ensemble algorithm. This algorithm learns a model capable of predicting the best processing device in a heterogeneous system to use per workload size, per kernel. Active machine learning is a methodology which is sensitive to the cost of training; specifically, it is able to reduce the time taken to construct a model by predicting how much is expected to be learnt from each new training instance and then only choosing to learn from those most profitable examples. The exemplar heuristic is constructed on average 4x faster than a baseline approach, whilst maintaining comparable quality. Next, a combination of active learning and sequential analysis is presented which reduces both the number of samples per training example as well as the number of training examples overall. This allows for the creation of models based on noisy information, sacrificing accuracy per training instance for speed, without having a significant affect on the quality of the final product. In particular, the runtime of highperformance compute kernels is predicted from code transformations one may want to apply using a heuristic which was generated up to 26x faster than with active learning alone. Finally, preliminary work demonstrates that an automated system can be created which optimises both the number of training examples as well as which features to select during training to further substantially accelerate learning, in cases where each feature value that is revealed comes at some cost.
Content-based image retrieval base on relevance feedback

In order to reduce the “semantic gap” between the underlying features and the high-level semantics in the image retrieval, the feature re-weighted correlation feedback mechanism is applied. The basic idea is to reduce the weight of the irrelevant feature dimension in the feature space and increase the weight of the relevant feature dimension. The improved method of feature weight adjustment proposed in this paper is compared with another method. The accuracy of the algorithm is proved by the experiment as the criterion of the evaluation effect.
Modeling and Operation of a Micro Grid using Fuzzy based Incremental Conductance for a PV System

: This article presents the application of renewable energy sources like wind and solar energy for modeling and operation of a micro grid. A current-source-interface multiple-input dc-dc converter is used to combine the wind and solar energy to the main dc bus. In this article, PMSG is used with a variable speed control method whose approach is to capture the maximum wind energy below the rated wind speed. This analysis provides the variations in load power with the change in solar irradiance wind energy. Fuzzy-based incremental conductance has been implemented for solar PV system. As a case study a 30-KW wind/solar hybrid power system model is examined. The observed dynamics demonstrates that the anticipated power system is a possible option for a sustainable micro grid application
Fast Implementation of Privacy Amplification in Continuous-Variable Quantum Key Distribution

By partition matrix method, we improve the speed of privacy amplification, the key phase of the post-processing in continuous-variable quantum key distribution system. The computational complexity of privacy amplification could be decreased by the partition matrix method. The optimal speed of the privacy amplification has been improved from 1.000 Mbit/s to 6.939 Mbit/s in our experimental tests.
Grammatical Difficulties with English Morphemes Encountered by Undergraduate Students in the Context of Myanmar

This study investigates grammatical difficulties encountered by learners in the context of Myanmar, based on the theory of grammatical difficulty with reference to the concept of explicit knowledge (Ellis, 2008). This study used the modified questionnaire including the grammatical judgment tasks designed by Shiu (2011) targeting 17 grammatical items. 50 first-year students from Yadanabon University completed the questionnaire targeting four grammatical items: plural -s, third-person singular -s, past tense -ed and progressive aspect -ing. The results indicate that the participants find most difficulty in identifying the plural -s errors though they find it harder to correct the progressive aspect -ing errors and they find least difficulty in identifying and correcting the past tense -ed errors. The learners find difficulty with plural -s and this may be because plural suffixes are not used when the noun is preceded by a number or a quantifier in Myanmar. They find difficulty with third-person singular -s because there is no grammatical system of third-person singular -s in Myanmar. They find difficulty with progressive aspect -ing because they seem to confuse progressive aspect and simple present tense. They find least difficulty with past tense ed and this may be due to the positive first language transfer because there is a past tense morpheme in Myanmar, just as in English. Although morpheme errors do not lead to communication problems, it is recommended that the teachers should help their students overcome grammatical difficulties by providing them reinforcement activities and letting them use in their own context.
(Early) practice makes perfect? Improving the quality of language input in early English programmes to promote L2 production skills

Over the last twenty years teaching English to young learners has become a global trend with about five hundred million children worldwide learning English in the early stages of their compulsory school education, while more and more children are being introduced to English in pre-school programmes from age 3 onwards. Our study focused specifically on the Dutch context of primary education and on those educational settings where English is taught in minimal input set-ups (up to 60 min per week). Such a minimal input situation, which is by far the most common one, has been shown to result in the development of very basic receptive skills and virtually absent productive skills (Aarts & Ronde, 2006). In our study we wanted to see if children’s receptive and, more importantly, productive skills in grade 1 (age 4 and 5) improve when taught only 60 min per week, but with the help of very specific methods of storytelling and fingerplay. The time span in which this study was carried out was two weeks. Our results show that even under minimal input conditions there was a radical improvement in both receptive and productive skills of children and, moreover, a complete retention and even a slight improvement of these skills four weeks after the end of the intervention programme. Our findings suggest that it might be that even with 60 min per week children still can develop their language skills as long as language learning takes place in an engaging, meaningful, and scaffolded ways. One of the ways to provide this kind of learning is through the methods of storytelling and fingerplay.
Providing a Structured Method for Integrating Non-Speech Audio into Human-Computer Interfaces

This thesis provides a framework for integrating non-speech sound into human-computer interfaces. Previously there was no structured way of doing this, it was done in an ad hoc manner by individual designers. This led to ineffective uses of sound. In order to add sounds to improve usability two questions must be answered: What sounds should be used and where is it best to use them? With these answers a structured method for adding sound can be created. An investigation of earcons as a means of presenting information in sound was undertaken. A series of detailed experiments showed that earcons were effective, especially if musical timbres were used. Parallel earcons were also investigated (where two earcons are played simultaneously) and an experiment showed that they could increase sound presentation rates. From these results guidelines were drawn up for designers to use when creating usable earcons. These formed the first half of the structured method for integrating sound into interfaces. An informal analysis technique was designed to investigate interactions to identify situations where hidden information existed and where non-speech sound could be used to overcome the associated problems. Interactions were considered in terms of events, status and modes to find hidden information. This information was then categorised in terms of the feedback needed to present it. Several examples of the use of the technique were presented. This technique formed the second half of the structured method. The structured method was evaluated by testing sonically-enhanced scrollbars, buttons and windows. Experimental results showed that sound could improve usability by increasing performance, reducing time to recover from errors and reducing workload. There was also no increased annoyance due to the sound. Thus the structured method for integrating sound into interfaces was shown to be effective when applied to existing interface widgets.
Sleepy: Wireless Channel Data Driven Sleep Monitoring via Commodity WiFi Devices

Sleep is a major event of our daily lives. Its quality constitutes a critical indicator of people's health conditions, both mentally and physically. Existing sensor-based or vision-based sleep monitoring systems either are obstructive to use or fail to provide adequate coverage. With the fast expansion of wireless infrastructures nowadays, channel data, which is pervasive and transparent, emerges as another alternative. To this end, we propose Sleepy, a wireless channel data driven sleep monitoring system leveraging commercial WiFi devices. The key idea of Sleepy is that the energy feature of the wireless channel follows a Gaussian Mixture Model (GMM) derived from the accumulated channel data over a long period. Therefore, a GMM based foreground extraction method has been designed to adaptively distinguish motions like rollovers (foreground) from background (stationary postures), leading to certain major merits, e.g., no calibrations or target-dependent training needed. We prototype Sleepy and evaluate it in two real environments. In the short-term controlled experiments, Sleepy achieves 95.65 percent detection accuracy (DA) and 2.16 percent false negative rate (FNR) on average. In the 60-minute real sleep studies, Sleepy demonstrates strong stability, i.e., 0 percent FNR and 98.22 percent DA. Considering that Sleepy is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for sleep monitoring.