Reviewerly/exHarmony huggingface dataset:
An ant colony optimisation-based approach to solve time interval dependent travelling salesman problem under fuzziness

In this study, we have explained a constrained travelling salesman problem (TSP) where total travelling cost must maintain a maximum level. The objective of this proposed TSP is to minimise the tot...
Rancang Bangun Sistem Informasi Tracer Study bagi Alumni Fakultas Teknik Universitas Mataram berbasis Web

Tracer study or commonly known as track record study or alumni survey, is the study of graduates of higher education institutions. The Faculty of Engineering is one of the faculties at the University of Mataram. The Faculty of Engineering, University of Mataram has four departments, including the Department of Civil Engineering, the Department of Mechanical Engineering, the Department of Electrical Engineering, and the Informatics Engineering study program. Each department will graduate students in each judicial period each year. At this time, the Faculty of Engineering has conducted a tracer study for its alumni. The tracer study is usually carried out using social media such as Facebook or using Google forms. This method is considered inappropriate because the files used are not stored in one place. The design process for this information system uses Waterfal. Waterfall is a reliable method in planning which is carried out with non-repetitive iterations. Based on the results of the questionnaire testing conducted, all respondents gave a score of agree and strongly agree. With an average percentage of 48% who answered strongly agree and a percentage of 52% for those who answered agree, this information system has helped the Faculty of Engineering in carrying out tracer studies.
Real Time Distributed Solar Irradiance sensing: a step closer to smart photovoltaic grids

We propose the first fiber-based distributed sensor able of monitoring absolute solar irradiance in real time, overcoming some of the greatest limitations imposed by the existing remote sensing technologies for dynamic solar energy systems.
The extent of use of social media networks by the female entrepreneurs in Jordan: empirical study

This study aims to determine the extent to which female entrepreneurs in Jordan use social media networking platforms to grow their businesses, and the extent to which social media drives success. ...
A Deep Convolutional Network for Saliency Object Detection with Balanced Accuracy and High Efficiency

It is difficult for current salient object detection algorithms to reach a good balance performance between accuracy and efficiency. To solve this problem, a deep convolutional network for saliency object detection with balanced accuracy and high efficiency is produced. First, through replacing the traditional convolution with the decomposed convolution, the computational complexity is greatly reduced and the detection efficiency of the model is improved. Second, in order to make better use of the characteristics of different scales, sparse cross-layer connection structure and multi-scale fusion structure are adopted to improve the detection precision. A wide range of evaluations show that compared with the existing methods, the proposed algorithm achieves the leading performance in efficiency and accuracy.
Target similarity matching algorithm of big data in remote sensing image based on Henon mapping

In order to overcome the problem of low matching accuracy in traditional big data target similarity matching algorithm of remote sensing image, this paper proposes a new target similarity matching ...
Enhanced bitcoin with two-factor authentication

Bitcoin transactions rely on digital signatures to prove the ownership of bitcoin. The private signing key of the bitcoin owner is the key component to enable a bitcoin transaction. If the signing ...
Film Trailer Generation via Task Decomposition.

Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie. These diverse functions make automatic trailer generation a challenging endeavor. We decompose it into two subtasks: narrative structure identification and sentiment prediction. We model movies as graphs, where nodes are shots and edges denote semantic relations between them. We learn these relations using joint contrastive training which leverages privileged textual information (e.g., characters, actions, situations) from screenplays. An unsupervised algorithm then traverses the graph and generates trailers that human judges prefer to ones generated by competitive supervised approaches.
On age prediction from facial images in presence of facial expressions

Predicting age and gender from facial images is a fundamental research problem that has many applications in major research areas. The state-of-the-art online APIs can predict age and gender but th...
One privacy-preserving multi-keyword ranked search scheme revisited

Searchable encryption is a useful tool which allows a user to securely search over encrypted data through keywords and retrieve documents of interest. It plays a key role in big data and outsourcin...
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by  Finding Flat Minima

This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our study shows that existing methods severely suffer from catastrophic forgetting, a well-known problem in incremental learning, which is aggravated due to data scarcity and imbalance in the few-shot setting. Our analysis further suggests that to prevent catastrophic forgetting, actions need to be taken in the primitive stage -- the training of base classes instead of later few-shot learning sessions. Therefore, we propose to search for flat local minima of the base training objective function and then fine-tune the model parameters within the flat region on new tasks. In this way, the model can efficiently learn new classes while preserving the old ones. Comprehensive experimental results demonstrate that our approach outperforms all prior state-of-the-art methods and is very close to the approximate upper bound. The source code is available at https://github.com/moukamisama/F2M.
Urdu character recognition: a systematic literature review

The cursive nature of the Urdu text, where a particular character concatenated in different words forms different shapes, makes it a complicated language for automatic character recognition. This p...
BiOBr/HPW/Au的制备及其光催化性能的研究

In this study, a novel BiOBr/HPW/Au photocatalyst was prepared via hydrothermal method followed by photo-reduction method. The characterization results indicated the successful introduction of HPW and Au in BiOBr. BiOBr/HPW/Au exhibited excellent photocatalytic activity in RhB degradation. The degradation rate constant of BiOBr/HPW/Au was 3.55 times higher than that of BiOBr. Radical scavenger experiments showed that ·O2- was the dominant reactive radical species. A possible mechanism of the enhanced photocatalytic activity was attributed to the synergistic effect of BiOBr, HPW and Au, which resulted in enhanced quantum efficiency and high light harvesting efficiency.
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural  Machine Translation Training

Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in real world. One common practice is to adjust the share of each corpus in the training, so that the learning process is balanced and low-resource cases can benefit from the high resource ones. However, automatic balancing methods usually depend on the intra- and inter-dataset characteristics, which is usually agnostic or requires human priors. In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation. We experiments with two classes of uncertainty measures on multilingual (16 languages with 4 settings) and multi-domain settings (4 for in-domain and 2 for out-of-domain on English-German translation) and demonstrate our approach MultiUAT substantially outperforms its baselines, including both static and dynamic strategies. We analyze the cross-domain transfer and show the deficiency of static and similarity based methods.
Does Thermal data make the detection systems more reliable

Deep learning-based detection networks have made remarkable progress in autonomous driving systems (ADS). ADS should have reliable performance across a variety of ambient lighting and adverse weather conditions. However, luminance degradation and visual obstructions (such as glare, fog) result in poor quality images by the visual camera which leads to performance decline. To overcome these challenges, we explore the idea of leveraging a different data modality that is disparate yet complementary to the visual data. We propose a comprehensive detection system based on a multimodal-collaborative framework that learns from both RGB (from visual cameras) and thermal (from Infrared cameras) data. This framework trains two networks collaboratively and provides flexibility in learning optimal features of its own modality while also incorporating the complementary knowledge of the other. Our extensive empirical results show that while the improvement in accuracy is nominal, the value lies in challenging and extremely difficult edge cases which is crucial in safety-critical applications such as AD. We provide a holistic view of both merits and limitations of using a thermal imaging system in detection.
Dominion algorithm - a novel metaheuristic optimisation method

In this paper, a novel bio-inspired and nature-inspired algorithm, named dominion algorithm, is proposed for solving optimisation tasks. The fundamental concepts and ideas which underlie the propos...
Smart Band For Elderly Fall Detection Using Machine Learning

Fall is a common issue for senior citizens, as they are weak and need assistance. A fall could result in serious injury or perhaps death. So giving proper attention as soon as possible will reduce the impact of the fall. The main goal of this research is to perceive the appropriate machine learning algorithm for the scenario and illustrate the deployment of the fall detection system. So if the person is under fall, the system detects it immediately and intimate the concerned person by sending a message through SMS along with the person's current location as a google map link. So that they can arrive at that spot and help them before the situation gets out of hand. It is a wearable device that should be worn in order to detect a person's fall. The hardware and software are combined in the design and implementation to detect and report the elderly's collapse in a seamless manner.
Articulatory and acoustic analysis of voice acting

Voice actors are an under-utilized population for linguistic study. Looking at voice actors' novel productions of typical speech sounds can tell us more about over which muscles we have volitional control. This can help test models of speech production, inform speech therapy, and create better pedagogy for teaching vocal performance. While there have been several studies analyzing the acoustics produced by voice actors (Teshigawara, 2001; 2003; 2004; 2009; 2011; Teshigawara et al., 2007; Uchida, 2007) there are fewer studies that have investigated the articulation of voice acting (Teshigawara &amp; Murano, 2004). This study used acoustic analysis, 3D ultrasound, and electroglottography to investigate the speech produced by six professional and amateur voice actors (2 female, 4 male). Measures of average fundamental frequency (F0) and F0 range were collected from each actor speaking in their regular adult voice and a simulated child voice. 3D ultrasound was used to look at tongue placement and horizontal kymograms of the ultrasound images were also used to infer hyoid bone movement. Additionally, acoustic estimates of vocal tract length were compared to electroglottography measurements of laryngeal raising for two of the subjects. This presentation will describe the within-subject variation across each actor’s adult and simulated child voices.
Computational trust evaluation algorithm for cloud models using fuzzy logic approach

Cloud computing offers several advantages over traditional computing models in industries, so applications which are developed for smart cities requires large amount of storage. Hence, smart cities...
Seismic Inversion by Hybrid Machine Learning

This research was fully funded by the Deep Earth Imaging Future Science Platform, CSIRO. The authors thank Dr. Mehdi Tork Qashqai and Dr. Caroline Johnson for reviewing an earlier version of the manuscript. They would like to thank Dr. Ben Harwood and Dr. Muming Zhao from Data 61, CSIRO for their guidance and insights on convolutional autoencoder. They would like to thank the Center for Subsurface Imaging and Fluid Modeling (CSIM), KAUST for the release of the Aqaba data. They also appreciate the time and efforts of the editor - Prof. Yehuda Ben-Zion, associate editor - Prof. Andrew Curtis, reviewer - Prof. Tariq Alkhalifah and one anonymous reviewer in reviewing this manuscript.
Optimisation of K-means algorithm based on sample density canopy

Since the random selection of the initial centroid and the artificial definition of the number of clusters affect the experimental results of K-means, therefore, this article uses sample density an...
Translation of code mixed language to monolingual languages using rule-based approach

The demand of bilingual code switching has significantly increased due to cross-lingual communication and information exchange at a global scale and specifically in India. The core component of MT ...
Factors Influencing the Quality of Academic Performance in Higher and Middle Secondary Schools of Trashi Yangtse Dzongkhag

The purpose of this article includes is to identify the factors that will determine the quality of academic performance of students and to assess teachers’ perceptions on factors that influence the quality of academic performance. In light of the deteriorating education quality and poor academic performance in high schools, it became important more than ever before to study the factors influencing the performance. The research focused mainly on educational article, thus, it was convenient to adopt descriptive research and follow the mixed method as a research design. The quantitative data was collected from 100 school teachers through e-mail and four principals for in-depth study. The interviews were recorded using the mobile device and then transcribed to analyze and develop themes. The research found that there is insufficient resources in school that has deteriorated the quality of academic performance, lack of professional development training. It was also evident that school culture contributes to improve the quality of academic performance. Therefore, the study recommends the concerned agencies need to allocate enough resources, improve social ambience, conduct frequent in-service workshops for the teachers and as well act as change agent for further improvement of conducive environment in all the levels of schools across the country.
An Adaptive Linear Filter model of procedural category learning

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The current model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data spans over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF’s advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve longstanding challenges to similar models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.
Product Copy Generation for Fashion Footwear Data

Natural Language Generation is a specialized field of Artificial Intelligence (AI) that deals with generation of text, given a set of inputs, like text, image or both. It is also deals with training of a given algorithm or machine to learn about a particular information and build relevant set of information about it in the form of natural language or text, given an input, while validating the same. This technology has stirred up a storm in the field of AI and has a great impact on automation science.
Model Explainability for Computer Vision

Computer vision tasks such as image classification and object detection are gradually becoming better day by day due to continuous research in improving the accuracy of models, the evolution of new frameworks, and the growing open source community. The results from recent state-of-the-art machine learning models are more promising than half a decade ago, which gives us confidence to expand the horizon of problems by undertaking more complex problems. If we look at the use cases around computer vision, the most prominent are in retail, agriculture, public health, the automobile industry, and the gaming industry. Some of the industries from this list have a legal mandate to use AI responsibly. The European Union is also trying to regulate the application and usage of AI. If the predictions or classifications generated by image classification models go wrong, users will not trust the model and its predictions. It is necessary to understand why the AI model has generated a prediction or classification one way vs. another way. Model explainability shows the end user which particular parts of an image made the model predict class A vs. other classes.
Generic Model for Adaptive Systems Realization

The permanent acquisition of the technical environment state and the ability to react to changes in this environment as well as to adapt to it are nowadays crucial for any information system. In this article, the authors present a well-defined model to guarantee in a simple way the design and the realization of adaptive information systems. This model is based on the Unified Modeling Language (UML) which is a widely known modeling standard. Its coverage is limited to bringing out the graded parties in the design of adaptive information systems. A future definition of a metamodel less related to UML language is therefore possible. The authors also present a code generator based on a model transformation technique. This generator allows you to partially produce domain-specific code as needed. A more complete code generator will come to ensure automatic generation of the code.
Einblick- A Sentimental Analysis And Opinion Mining System For Mobile Networks

Sentimental analysis and popular legal opinion mining are one among the foremost agile research areas in natural language processing and is additionally widely studied in data processing, web mining and text mining. The growing grandness of sentimental analysis coincide with the rise of social medium like reviews, forum discussion, blog, micro blog, Twitter and social electronic networks. A system to implement this technology in mobile network sectors can be very much helpful for any mobile network for quicker interaction with their subscribers as well as enriching their reach of advertisement. Thus ensuring effective and optimized use of their resources to yield desired public response within a proposed time limit.Sentimental analysis helps to decide whether a statement is positive ,neutral or negative. It also helps data analyst to collect public opinion and do research based on that. In sentimental analysis we breakdown text into small parts and then we will identify each statement bearing phrase or components and will assign a score to each component.
LANGaware: Introducing the right solution for the early detection of neurodegenerative and psychiatric diseases

Abstract Background There are multiple neurodegenerative diseases that directly affect speech [1]. However, its utilization as a robust indicator for cognitive impairment is under‐investigated. In many cases, mild cognitive decline progresses to a neurodegenerative disease and its detection is of utmost importance, since it is at this stage that treatment is most effective. One of our core goals is developing techniques for differentiating between patients with cognitive decline and healthy cohorts, by utilizing only speech samples [2]. Such samples are obtained from verbal elicitation tasks designed for cognitive assessment, e.g. picture descriptions and narration of everyday activities. Method Audio recordings from cognitive assessment tasks are fed through our platform to a Natural Language Processing and Machine Learning pipeline, employing an automatic discovery procedure of predictive salient biomarkers, to train an advanced classification system. The biomarker collection includes features that characterize voice, speech, language structure, composition and usage, and are engineered to highlight symptoms of neurodegenerative disorders. Biomarkers undergo multiple stages of filtering, processing and transformation to train and fine‐tune the final model. Diagnostic performance of the output classifier is obtained on an unseen test set, to ensure a robust generalization of the platform. Result Our platform utilizes the set of automatically selected, cross‐linguistic digital biomarkers to obtain sensitivity and specificity scores of 81% and 84% respectively, compared against medical expert diagnosis. The most salient biomarkers with respect to identifying our pathological cohort, relate to feature categories of Syntactic Complexity, Content Word usage, Lexical Repetition, Syntactic Errors and Function Words, with weight contributions of ∼ 16%, 14%, 13%, 12% and 11% respectively. Our platform provides additional, detailed population and patient‐based descriptive analytics to enhance transparency and explainability of the results. Conclusion Early detection of cognitive decline facilitates early intervention, treatment and proactive care, delaying disease progression and reducing symptom severity. We believe that our platform provides an effective solution for risk factor estimation and our findings incentivize further research into speech analysis techniques for the prediction of cognitive decline. [1] Boschi, Veronica, et al.,Frontiers in psychology 8 (2017): 269. [2] Vassiliki Rentoumi et al., Alzheimer's &amp; Dementia, Wiley, volume 16, 2020.
EDI Overview (EDI: It's Not So Scary After All This Time)

The EDI (Electronic Data Interchange) Preconference was presented in six parts. It opened with a comprehensive overview of EDI, including its use within the library serials industry. The Pre-conference 6was well-attended, attracting 170 people, including librarians, vendors, automation system representatives, and publishers. Tina Feick delivered the first, keynote presentation and laid the groundwork for the five talks to follow. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: etinfo@haworth.com]