Reviewerly/exHarmony huggingface dataset:
Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation

The huge amount of information stored in audio and video repositories makes search on speech (SoS) a priority area nowadays. Within SoS, Query-by-Example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given a spoken query. Research on this area is continuously fostered with the organization of QbE STD evaluations. This paper presents a multi-domain internationally open evaluation for QbE STD in Spanish. The evaluation aims at retrieving the speech files that contain the queries, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: MAVIR database, which comprises a set of talks from workshops; RTVE database, which includes broadcast television (TV) shows; and COREMAH database, which contains 2-people spontaneous speech conversations about different topics. The evaluation has been designed carefully so that several analyses of the main results can be carried out. We present the evaluation itself, the three databases, the evaluation metrics, the systems submitted to the evaluation, the results, and the detailed post-evaluation analyses based on some query properties (within-vocabulary/out-of-vocabulary queries, single-word/multi-word queries, and native/foreign queries). Fusion results of the primary systems submitted to the evaluation are also presented. Three different teams took part in the evaluation, and ten different systems were submitted. The results suggest that the QbE STD task is still in progress, and the performance of these systems is highly sensitive to changes in the data domain. Nevertheless, QbE STD strategies are able to outperform text-based STD in unseen data domains.
FMQO: A Federated RDF System Supporting Multi-query Optimization

This demo designs and implements a system called FMQO that can support multiple query optimization in federated RDF systems. Given a set of queries posed simultaneously, we propose a heuristic query rewriting-based approach to share the common computation during evaluation of multiple queries. Furthermore, we propose an efficient method to use the interconnection topology between SPARQL endpoints to filter out irrelevant sources and join intermediate results during multiple query evaluation. The experimental studies over both real federated RDF datasets show that the demo is effective, efficient and scalable.
Topic Modelling with Fuzzy Document Representation

Latent Dirichlet Allocation (LDA) and its variant topic models have been widely used for performing text mining tasks. Topic models sometimes produce incoherent topics having noisy words with high probabilities. The reason is that topic model suffers from binary weighting of terms, sparsity and lack of semantic information. In this work, a fuzzy document representation is used within the framework of topic modeling that resolves these problems. Fuzzy document representation uses the concept of Fuzzy Bag of Word (FBoW) that maps each document to a fixed length fuzzy vector of basis terms. Each basis term in the fuzzy vector belongs to all documents in the dataset with some membership degree. Latent Dirichlet Allocation is tailored to use fuzzy document representation and results are compared with regular LDA and term weighted LDA over short and long text documents on document clustering and topic quality tasks. LDA with fuzzy representation generates more coherent topics than other two methods. It also outperforms the other methods on document clustering for short documents and produces comparable results with term weighted LDA for long documents.
Ensemble Methods to Distinguish Mainland and Taiwan

This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese. We first build several base classifiers, including a Naive Bayes classifier with word n-gram as features, SVMs with both character and syntactic features, and neural networks with pre-trained character/word embeddings. Then we adopt ensemble methods to combine output from base classifiers to make final predictions. Our ensemble models achieve the highest F1 score (0.893) in simplified Chinese track and the second highest (0.901) in traditional Chinese track. Our results demonstrate the effectiveness and robustness of the ensemble methods.
PERAMALAN CURAH HUJAN EKSTRIM DI PROVINSI BANTEN DENGAN MODEL EKSTRIM SPASIAL

Extreme rainfall can cause negative impacts such as floods, landslides, and crop failures. Extreme rainfall modeling using spatial extreme models can provide location information of the event. Spatial extreme models combine the extreme value theory, the max-stable process, and the geostatistical correlation function of F-madogram. The estimation of the return value on the spatial extreme models is performed using the copula approach. This research used monthly rainfall data from January 1998 until December 2014 at 19 rain stations in Banten Province. The results showed that there was a high spatial dependence on extreme rainfall data in Banten Province. The forecast in range 1.5 years showed the best result compared to other ranges (1 year, 3 years, and 5 years) with MAPE 20%. The pattern of extreme rainfall forecasting was similar to its actual value with a correlation of 0.7 to 0.8. The predicted location that has the highest extreme rainfall was the Pandeglang Regency. Extreme rainfall forecasting at 19 rain stations in Banten Province using spatial extreme models produced a good forecasting.
A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults' root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario.
Querying heterogeneous linked building datasets with context-expanded GraphQL queries

Linked Data in the construction industry is a topic gaining serious interest over the last years. However, this interest remains largely academic and has not sparked much adoption of web technologies in the field. To nourish adoption of Linked Data in practice, access to the data has to be made more easy. SPARQL, the recommended RDF query language, has proven very powerful for retrieving and updating RDF datasets. However, due to its verbosity and complexity, it is often considered a threshold for developers to implement in their tools. In this paper, we compare SPARQL with Linked Data querying languages that extend the GraphQL syntax, in context of querying building datasets: HyperGraphQL and GraphQL-LD. Since it went open source in 2015, GraphQL has been adopted by a large community of developers, partly due to its elegance and conciseness. As a use case, the queries are performed on an RDF-based multi-model that relies on the recent ISO standard ICDD (ISO 21597). ICDD interlinks information on a sub-document identifier level, based on OWL. It is aimed at the industry in that it adds a layer of Linked Data to documentation formats that are still widely used in practice: IFC, spreadsheets, imagery etc. Therefore, it is considered a tuned use case for comparing these different RDF query languages.
ENHANCEMENT OF CYBERSECURITY AWARENESS PROGRAM ON PERSONAL DATA PROTECTION AMONG YOUNGSTERS IN MALAYSIA: AN ASSESSMENT

Cybersecurity awareness program has been used as a medium to educate and make awareness among youngsters on personal data protection. However, it is still unclear the extent to which the effectiveness of this cybersecurity awareness program among youngsters require an assessment. This to ensure the current content of cybersecurity awareness program is consistently updated and aligned with current Internet usage among youngsters. Our assessment was systematically conducted using Kirkpatrick's Four Learning Evaluation Model as conceptual framework; which consists of four phases of sequential assessment; Phase 1 –Reaction, Phase 2- Learning, Phase 3 – Behavior and Phase 4-Result. This study used mixed method research methodology in conducting the assessment and the instruments used in Phases 1 until 4 respectively were survey, pre-test and post-test survey, observation of web recording and focus group interview. The findings from this study revealed that youngsters do have positive reaction towards the program content, reported to have changes in terms of their knowledge and skills and practice of desired behavior on personal data protection. However, the undesired behavior also revealed practiced among youngsters and this suggest improvement required to the current module used to educate youngsters. The improvements include i) Decision making process in using personal data, ii) Management of online application iii)Management of online contents iv)Management of password and username. The result of this assessment is significant to the stakeholders in terms of providing insight into the effectiveness level of current module used to educate youngsters and assist them to decide better module.
Facial expression recognition using geometric features and modified hidden Markov model

This work proposes a geometric feature-based descriptor for efficient Facial Expression Recognition (FER) that can be used for better human-computer interaction. Although lots of research has been focused on descriptor-based FER still different problems have to be solved regarding noise, recognition rate, time and error rates. The Japanese Female Facial Expression (JAFFE) data sets help to make FER more reliable and efficient as pixels are distributed uniformly. The proposed system introduces novel geometric features to extract important features from the images and layered Hidden Markov Model (HMM) as a classifier. The layered HMM is used to recognised seven facial expressions i.e., anger, disgust, fear, joy, sadness, surprise and neutral. The proposed framework is compared with existing systems where the proposed framework proves its superiority with the recognition rate of 84.7% with the others 85%. Our proposed framework is also tested in terms of recognition rates, processing time and error rates and found its best accuracy with the other existing systems.
Co-training for Policy Learning

We study the problem of learning sequential decision-making policies in settings with multiple state-action representations. Such settings naturally arise in many domains, such as planning (e.g., multiple integer programming formulations) and various combinatorial optimization problems (e.g., those with both integer programming and graph-based formulations). Inspired by the classical co-training framework for classification, we study the problem of co-training for policy learning. We present sufficient conditions under which learning from two views can improve upon learning from a single view alone. Motivated by these theoretical insights, we present a meta-algorithm for co-training for sequential decision making. Our framework is compatible with both reinforcement learning and imitation learning. We validate the effectiveness of our approach across a wide range of tasks, including discrete/continuous control and combinatorial optimization.
A Convolutional Neural Network for Automatic Identification and Classification of Fall Army Worm Moth

To combat the problem caused by the Fall Army Worm in the country there is a need to come up with robust early warning and monitoring systems as the current manual system is labor intensive and time consuming.The automation of the identification and classification of the insect is one of the novel methods that can be undertaken.Therefore this paper presents the results of training a Convolutional Neural Network model using Google's Tensorflow Deep Learning Framework for the identification and classification of the Fall Army worm moth.Due to lack of enough training dataset and good computing power, we used transfer learning, which is the process of reusing a model trained on one task as a starting point for a model on a second task.Googles pre-trained InceptionV3 model was used as the underlying model.Data was collected from four sources namely the field, Lab setup, by crawling the internet and using Data Augmentation.We Present results of the best three trials in terms of training accuracy after several attempts to get the best metrics in terms of learning rate and training steps.The best model gave a prediction average accuracy of 82% and a 32% average prediction accuracy on false positives.The results shows that it is possible to automate the identification and classification of the Fall Army worm Moth using Convolutional Neural Networks.
Sniff‐Phish: A novel framework for resource intensive computation in cloud to detect email scam

Abstract Today, the growing significance of digitization across the globe has made the cybersecurity as inevitable. There are many threats in existence to induce harm and to make illegal activities in a device or a network. Email scam or phishing is a technique that most of the hackers use as baits to infiltrate into a system. After a systematic analysis of the infiltration process, a Chrome extension application named Sniff‐Phish is developed, where the resource intensive computational tasks are performed in cloud with which we could be able to detect various categories of real‐time email scams or phishing attacks like zero‐day and spear phishing. This extension application is based upon analyzing some basic criterion of the malicious URL and duly presenting the legitimacy level of the link in the form of a report. The accuracy obtains for Sniff‐Phish is 98% and covers a wide range of phishing websites, resulting in the less false positive rate of 1.2%. Considering the zero‐day and spear phishing detection rate of Sniff‐Phish, the overall experimental results prove that the proposed system outperforms the conventional phishing detection methods.
The Marginal Cost of Mining, Metcalfe's Law and Cryptocurrency Value Formation: Causal Inferences from the Instrumental Variable Approach

This paper is the first to rigorously test commonly cited simplistic theories of cryptocurrency pricing, namely, cost-based model and Metcalfe’s law, using causal inferences from the instrumental variables approach on block-level data for six proof-of-work coins. Positive effects of hashrate and transaction count implied by cost-based pricing and Metcalfe’s law, respectively, are non-existent for any of the coins investigated. Negative and insignificant estimators cannot be explained by weak instruments, suggesting previously reported strong positive relationships are spurious due to autocorrelation and endogeneity. The study reinforces the need for a more sophisticated cryptocurrency valuation framework.
Entropy Based Independent Learning in Anonymous Multi-Agent Settings

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of other similar ”learning” individuals and only local aggregated observation of other agents states (e.g., only number of other taxis in same zone as current agent).
Linking Educational Resources on Data Science

The availability of massive datasets in genetics, neuroimaging, mobile health, and other subfields of biology and medicine promises new insights but also poses significant challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, funding several centers of excellence in biomedical data analysis and a Training Coordinating Center (TCC) tasked with facilitating online and inperson training of biomedical researchers in data science. A major initiative of the BD2K TCC is to automatically identify, describe, and organize data science training resources available on the Web and provide personalized training paths for users. In this paper, we describe the construction of ERuDIte, the Educational Resource Discovery Index for Data Science, and its release as linked data. ERuDIte contains over 11,000 training resources including courses, video tutorials, conference talks, and other materials. The metadata for these resources is described uniformly using Schema.org. We use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology, which we developed to further describe resource content. Finally, we map references to people and organizations in learning resources to entities in DBpedia, DBLP, and ORCID, embedding our collection in the web of linked data. We hope that ERuDIte will provide a framework to foster open linked educational resources on the Web.
A recommendation system for emergency mobile applications using context attributes: REMAC

The extensive use of mobile devices had led to tremendous growth in not only the usage of different apps but also their capability to help people in moments of crisis. There are different emergency mobile apps published in the app markets; these apps can be of enormous assistance to victims as they can provide valuable information and guidance at the opportune moments. However, app store reviews, ratings, and relevant studies have revealed that users are often averse to using these apps or their different features. This draws our attention to the need for recognizing essential features and including them in the emergency apps to increase their usability. Our proposed recommendation system called REMAC combines different machine learning techniques to analyze the context characteristics of different organizations and suggest unique features that can be included in their emergency apps. REMAC is built by analyzing 24 potential context attributes of 1909 universities spread across North America. This research also includes a systematic attribute selection process that enables us to reach a local optimum for the given dataset. This tool carefully dissects the context attributes of each university and suggests top features that should be included in its emergency app. It leverages the data (other apps and features) provided by the app markets to suggest essential features.
A Concise Review of Named Entity Recognition System: Methods and Features

Abstract Named Entity Recognition (NER) is an elementary tool for all application areas in Natural Language Processing (NLP) such as Automatic Summarization, Information Extraction, Information Retrieval, Text Mining, Machine Translation, Question Answering, and Genetics. NER is a task to discover and categorises the named entities (‘atomic elements’) in the text into predefined classes such as the names of persons, organizations, locations, terminologies of time, quantity and etc. Different languages may have different morphologies and thus involve dissimilar NER procedures. For example, an Arabic NER system cannot be practically used in processing Malay texts due to the different morphological features. The morphological features of every language are rich and complex and donates to the difficulties of implementing an actual method to develop the accurate NER system. In this paper, we review on three main techniques that commonly used to develop an NER system well-known as Rule-Based, Machine Learning, and Hybrid approach. This paper also highlights the features of each technique.
A Novel Deep Learning Approach for Semantic Interoperability Between Heteregeneous Multi-Agent Systems

This article focuses on the issue of semantic interoperability in heterogeneous distributed multi-agent systems. Existing middleware technologies offer programming models that strongly combine agents’ learning models and communication models, which can lead to performance weaknesses when the number of agents is very important. Moreover, existing methods in the field of semantic interoperability solve the problem of understanding messages exchanged between distributed agents with heterogeneous ontologies, using several techniques to combine these ontologies. The first category of these methods relies on the fusion principle, others use alignment, and finally, there are those founded on Semantic Web technique. All these methods are limited to abstract concepts and do not deal with concrete concepts such as those represented by images. We propose in this paper a new approach that addresses the problem of semantic interoperability between heterogeneous distributed agents based on two principles: At first, the communication aspect of the agent from the learning aspect is separated. Then, we propose extending semantic interoperability to concrete concepts by combining two techniques: Semantic Web technology, which allows terms representing abstract concepts to be interpreted and deep learning technology, which is introduced as a new method to ensure semantic interoperability in the case of concrete concepts such as images. A detailed description of the proposed approach is provided, showing that it is very useful in solving the disadvantages of existing multi-agent platforms.
Omnidirectional field of view structured light calibration method for catadioptric vision system

Abstract There have been improvements to structured light-based catadioptric vision systems (SLCV) to assist with robot navigation and reduce computation time. However, structured-light calibration does not satisfy this assumption. Motivated by this problem, this paper proposes a new approach for estimating the position of the structured lights of an SLCV system. Structured light points projected by lasers are reflected into the image plane through a conic mirror in order to view an omnidirectional image containing multiple structured light points. Efficient calibration of the laser location is possible based on the 2D information of the laser points, subject to the camera parameters and the mirror position. The performance of this method is evaluated experimentally using simulated and real data, and demonstrates that the proposed method is more robust and provides a higher measurement accuracy with a lower number of images, thus verifying the method’s feasibility.