Reviewerly/exHarmony huggingface dataset:
Anomaly Detection With Multivariate K-sigma Score Using Monte Carlo

We propose a new unsupervised anomaly detection technique based on Monte Carlo sampling. We draw samples from a Gaussian probability distribution that fits to the given data and use these samples to form a Monte Carlo estimate of an anomaly score. Many of the current anomaly detection algorithms detect anomalies without producing any anomaly scores. This may lead to confusion over the scaling of the anomaly situation as well as identifying the underlying cause for anomalies in explainable conditions. The proposed algorithm is a multi-variate unsupervised technique, which also provides anomaly scores that allows measuring the strength of outlierness of a multi-variate data point. Our experiments show that the proposed algorithm achieves better results compared to the state-of-the-art anomaly detection algorithms with clustering-based approaches and isolation forest.
F0 Modeling for Isarn Speech Synthesis using Deep Neural Networks and Syllable-level Feature Representation

The generation of the fundamental frequency (F0) plays an important role in speech synthesis, which directly influences the naturalness of synthetic speech. In conventional parametric speech synthesis, F0 is predicted frame-by-frame. This method is insufficient to represent F0 contours in larger units, especially tone contours of syllables in tonal languages that deviate as a result of long-term context dependency. This work proposes a syllable-level F0 model that represents F0 contours within syllables, using syllable-level F0 parameters that comprise the sampling F0 points and dynamic features. A Deep Neural Network (DNN) was used to represent the relationships between syllable-level contextual features and syllable-level F0 parameters. The proposed model was examined using an Isarn speech synthesis system with both large and small training sets. For all training sets, the results of objective and subjective tests indicate that the proposed approach outperforms the baseline systems based on hidden Markov models and DNNS that predict F0 values at the frame level
Blockchain brings confidence to facilitate the flow of data in the agricultural field

In our digital world, farms are connected and produce more and more data. This source of data can be a precious input to address challenges of multi-performance agriculture. Beyond the services provided, access to farmers’ data depends on a clear understanding of their use, which must be done in a transparent way. Several codes of conduct at a national or international level push for a voluntary commitment to respect some good practices in the use of agricultural data. To provide a tool and answer farmer’s questions on the control of their data and the transparency of the data processing, we have designed an interoperable ecosystem of farmer consents management based on Blockchain, protecting farmers from no consented uses of their data.
Current Trend and Policy Implication of Social Fisheries in Japan: Focusing on the Case of Ehime Prefecture

Currently, Fisheries Industry of Korea is facing problems with a lack of successors and an aging society. Failure to address this is highly likely to cause a future decline in the industry. In Japan, there are efforts to mitigate these problems through social fisheries. Therefore, the purpose of this research is to define the trends in and policy implications of social fisheries in Japan and to make better policies to improve the industry in Korea. To this end, the researcher examined existing literature, and as a result of the survey, selected the case of Ehime Prefecture and conducted an on-site survey. One nonprofit organization (NPO) in Ehime Prefecture acquired a trout farming company and currently uses government funding to employ disabled person. Determining that it is dangerous for those with disabilities to work at sea, the organization requires these employees to perform their duties on fish farms in inland waters. Meanwhile, there is also support from administrative agencies and local universities. For example, the local government and Ehime University established a university–industry–government linkage project for the social fisheries industry. The survey found that people with disabilities work on trout farms, which can help these workers overcome depression and have hope for a future career in fishing.
Development of Motor Skills

The conditioned reflex theory of motor skill development and its mistakes. The definition of motor skill. Two stages of skill development. Setting the leading level. Definition of the motor composition. Revealing sensory corrections. The automation phase. Backgrounds and automatisms. Skill transfer. Decreasing the threshold for receptors. The phase of correction recruitment. Standardization. Three stages of skill development with synergic backgrounds. Dynamically stable movements. The discrete nature and generality of dynamically stable forms. The stabilization phase. Factors confounding automation. Improvement of switching, effector transfer, and technique transfer. Skill generalization. Preliminary corrections. The structure of the writing skill. Re-automation and warming-up.
Overbounding Sequential Estimation Errors Due to Non-Gaussian Correlated Noise

He is currently a lead signal processing engineer at The MITRE
Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption

Spoken dialog systems have seen applications in many domains, including medical for automatic conversational diagnosis. State-of-the-art dialog managers are usually driven by deep reinforcement learning models, such as deep Q networks (DQNs), which learn by interacting with a simulator to explore the entire action space since real conversations are limited. However, the DQN-based automatic diagnosis models do not achieve satisfying performances when adapted to new, unseen diseases with only a few training samples. In this work, we propose the Prototypical Q Networks (ProtoQN) as the dialog manager for the automatic diagnosis systems. The model calculates prototype embeddings with real conversations between doctors and patients, learning from them and simulator-augmented dialogs more efficiently. We create both supervised and few-shot learning tasks with the Muzhi corpus. Experiments showed that the ProtoQN significantly outperformed the baseline DQN model in both supervised and few-shot learning scenarios, and achieves state-of-the-art few-shot learning performances.
Estimating a Driver's Gaze Point by a Remote Spherical Camera

In this paper we propose a novel method of detecting a driver's gaze point by a single remote spherical camera. A spherical camera is fixed at the upside of the windshield of a vehicle so that it can observes outside scenes and an inside driver simultaneously. We propose a simplified model of a driver's gaze point detection under this setting-up by approximating the gaze direction vector as that starting from the viewpoint of the spherical camera by omitting the distance of the driver from the spherical camera. Furthermore, two algorithms are proposed and evaluated: one is gaze point estimation based on the geometric model using OpenFace [1], and the other is to use a neural network, which uses the gaze vector and head pose parameters obtained from OpenFace as input of the neural network, to estimate the gaze point in the spherical image. As shown in the experimental results, using a neural network to compensate the measuring errors of OpenFace can achieves a much better result.
Roman to Gurmukhi Social Media Text Normalization

Purpose Normalization is an important step in all the natural language processing applications that are handling social media text. The text from social media poses a different kind of problems that are not present in regular text. Recently, a considerable amount of work has been done in this direction, but mostly in the English language. People who do not speak English code mixed the text with their native language and posted text on social media using the Roman script. This kind of text further aggravates the problem of normalizing. This paper aims to discuss the concept of normalization with respect to code-mixed social media text, and a model has been proposed to normalize such text. Design/methodology/approach The system is divided into two phases – candidate generation and most probable sentence selection. Candidate generation task is treated as machine translation task where the Roman text is treated as source language and Gurmukhi text is treated as the target language. Character-based translation system has been proposed to generate candidate tokens. Once candidates are generated, the second phase uses the beam search method for selecting the most probable sentence based on hidden Markov model. Findings Character error rate (CER) and bilingual evaluation understudy (BLEU) score are reported. The proposed system has been compared with Akhar software and RB\_R2G system, which are also capable of transliterating Roman text to Gurmukhi. The performance of the system outperforms Akhar software. The CER and BLEU scores are 0.268121 and 0.6807939, respectively, for ill-formed text. Research limitations/implications It was observed that the system produces dialectical variations of a word or the word with minor errors like diacritic missing. Spell checker can improve the output of the system by correcting these minor errors. Extensive experimentation is needed for optimizing language identifier, which will further help in improving the output. The language model also seeks further exploration. Inclusion of wider context, particularly from social media text, is an important area that deserves further investigation. Practical implications The practical implications of this study are: (1) development of parallel dataset containing Roman and Gurmukhi text; (2) development of dataset annotated with language tag; (3) development of the normalizing system, which is first of its kind and proposes translation based solution for normalizing noisy social media text from Roman to Gurmukhi. It can be extended for any pair of scripts. (4) The proposed system can be used for better analysis of social media text. Theoretically, our study helps in better understanding of text normalization in social media context and opens the doors for further research in multilingual social media text normalization. Originality/value Existing research work focus on normalizing monolingual text. This study contributes towards the development of a normalization system for multilingual text.
Modeling and Analysis of Boundary Objects and Methodological Islands in Large-Scale Systems Development

Large-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. In previous studies, we found the importance of analyzing methodological islands (i.e., groups using different development methods than the surrounding organization) and boundary objects between them. In this paper, we propose a metamodel to better capture and analyze coordination and knowledge management in practice. Such a metamodel can allow practitioners to describe current practices, analyze issues, and design better-suited coordination mechanisms. We evaluated the conceptual model together with four large-scale companies developing complex systems. In particular, we derived an initial list of bad smells that can be leveraged to detect issues and devise suitable improvement strategies for inter-team coordination in large-scale development. We present the model, smells, and our evaluation results.
ENHANCING FAKE PRODUCT DETECTION USING DEEP LEARNING OBJECT DETECTION MODELS

ResearchAndMarkets wrote in their report on May 15, 2018, that up to 1.2 Trillion USD in 2017 of products are counterfeited goods. The report estimated this damage globally at 1.82 Trillion USD in 2020. This paper does not consider copyright infringement, digital piracy, counterfeiting or fraudulent documents, but rather examines the prevention of counterfeiting on a technological basis. The presence of counterfeit products on the European and US markets increase, the intervention of inspection bodies and authorities alone is obviously not sufficient, but consumers could make their contribution and improve the situation. In this paper, we research the possibility to reduce counterfeit products using machine learning-based technology. Image and text recognition, and classification based on machine learning have the potential to become the key technology in the fight against counterfeiting. Image recognition and classification of product information empowers the end customer to identify counterfeits accurately and efficiently by comparing them with trained models. The goal of this paper is to create an easy, simple, and elegant application, which empowers the end-users to identify counterfeit products and as such contribute to the fight against product piracy.
RETRACTED: The Construction of the Paperless English Test Answering System in University Based on Computer Technology

Abstract In recent years, due to the diversification of College English education learning mode and the complexity of students, the organization and management of English education examination is very heavy. The traditional paper-based English test not only has a long cycle and complex process, but also is difficult to prevent cheating. Therefore, the construction of the paperless examination and answering system for the college English test is particularly urgent. This paper clarifies the process of the paperless examination of college English and analyses the functional requirements of the paperless English examination answer system. On this basis, this paper constructs the College paperless English examination and answering system based on computer technology, which provides a solid technical support for strengthening the reform of the College English teaching system.
RETRACTED: Research on the Construction of Contemporary Chinese Literature and Art Subject Knowledge System Based on Computer Technology

Abstract Knowledge system is a new theoretical research field in the field of Humanities and social sciences. To study the axiology knowledge and value neutrality in the history of western thought through computer technology, it is helpful to provide reference for the theory of literature and art. This paper first analyses the influence of Western knowledge and knowledge system on China, and then discusses the evolution of knowledge concept in the history of thought, as well as the anxiety consciousness of literature and art research. Finally, the paper gives the strategy of value reconstruction in knowledge production and knowledge construction of literature and art.
Cyber Range Training Programme Specification Through Cyber Threat and Training Preparation Models

In light of the ever-increasing complexity and criticality of applications supported by ICT infrastructures, Cyber Ranges emerge as a promising solution to effectively train people within organisations on cyber-security aspects, thus providing an efficient mechanism to manage the associated risks. Motivated by this, the work presented herein introduces the model-driven approach of the THREAT-ARREST project for Cyber Range training, presenting in detail the Cyber Threat Training and Preparation (CTTP) models. These models, comprising sub-models catering for different aspects of the training, are used for specifying and generating the Training Programmes. As such, the paper also provides details on implementation aspects regarding the use of these models in the context of a usable cyber range training platform and two specific training scenarios.
Tracking System and Its Application in Unmanned Automobile Navigation Based on Sparse Photoelectric Sensor Network

Sensor tracking technology has broad prospects of application in the fields of smart home and environmental protection. The passive motion tracking method of sensor networks can realize the perception of location, temperature and other information without carrying sensor nodes. A sparse network tracking system based on infrared sensor nodes is proposed in this study, which can control the running automobiles with unmanned navigation. On the basis of the theory of diffraction, the way of spreading for wireless received signal strength (RSS) can be divided into "scattered waves" and "diffracted waves," which can be regarded as two components of infrared sensing wireless signals so as to further propose the RSS indicators of "long-term testing value" and "short-term test value." Based on these indicators, a measurement model based on diffraction effects and scattering effects is proposed, and an improved particle filter algorithm is used to update the motion tracking. The hardware design of each module in an unmanned vehicle includes the main controller, tracking circuit, serial port circuit, motor control circuit and infrared sensor control circuit of the car. In the experiment, the measurement accuracy of the tracking system based on the sparse infrared photoelectric sensor was first tested. In the simulation experiment, the long-term test value, the short-term test value and the actual measurement value were compared respectively. The test results show that the theoretical RSS value and the actual test result can be matched. Moreover, the infrared photoelectric tracking system is used to design the navigation control system of unmanned cars, helping the car to drive automatically through obstacle avoidance test and tracking obstacle avoidance test.
ONT-SLD: A DOMAIN ONTOLOGY FOR LEARNING DISABILITY

Knowledge-based technology with complete access to useful information is vital to support decision-making and solve complex problems in many areas such as management, medical, business, education and others.However, the domain of specific learning disabilities still suffers from the lack of explicit representation of knowledge and data modelling format through explicit format and standards such as RDF and OWL, which limits the effective sharing between experts and general users.Therefore, this article presents a novel domain ontology, called ONT-SLD, to formally conceptualize essential elements and rules, covering the characteristics of specific learning disabilities student and educational activities.During the ontology building, applicable standards and regulations have been extensively adopted.The proposed ontology model is evaluated in a real-world case to early identify a student with learning disabilities and recommend appropriate educational activities.Taxonomy evaluation is conducted using SPARQL to show the ontological model is able to detect any semantic conflicts.The future work includes the extension of ONT-SLD to other types of special education so that it can contribute to a broader range of applications in the special education environment.
Mining Fuzzy Common Sequential Rules with Fuzzy Time-Interval in Quantitative Sequence Databases

There are two kinds of sequential rules. They are classical sequential rules and common sequential rules. The common sequential rules present the relationship between unordered itemsets in which all the items in the antecedent part have to appear before the ones in the consequent part. All existing algorithms for mining common sequential rules can not apply to quantitative sequence databases. Furthermore, the common sequential rules found so far did not yet reveal the time gap about the apperance of itemsets in its antecedent and consequent parts. The purpose of this article is to overcome the two disadvantages mentioned above. Specifically, the article proposes an algorithm called IFERMiner to discover common sequential rules in quantitative sequence databases, where the time gap about appearance of two attribute sets in its antecedent and consequent parts is taken account. This algorithm was developed from the ERMiner algorithm that is the most efficient algorithm to discover common sequential rules in transactional sequence databases now. The computational complexity of the IFERMiner algorithm is also shown in the article and it is polynomial. The FCSI rules found out by the IFERMiner algorithm are useful for marketing domain and market analysis.
Mask R-CNN for Moving Shadow Detection and Segmentation

One of the primary tasks of completing and developing many computer vision applications is to identify and remove shadow regions. Most existing moving shadow detection methods depend on extracting hand-crafted features of object and shadow regions manually (for example the chromaticity, physical, or geometric properties). Shadow detection using handcrafted features is a challenging task due to different environmental conditions of the shadow such as camouflage and illumination irregularity problems that make these features inefficient to handle such problems. The proposed method uses Convolution Neural Networks (CNN) to automatically learn different distinctive features to model shadow under different environmental conditions. In this paper, the Mask Region Convolution Neural Network (Mask R-CNN) framework is evaluated and tested to automatically perform semantic segmentation in order to detect and classify shadow pixels from the entire video frame. To adapt Mask R-CNN for segmenting and detecting shadow regions, the most significant features are extracted from video frames in a supervised way using deep Residual Network (ResNet-101) architecture. Then, the Region proposal network (RPN) predicts regions of interest (ROI) and their classes that contain foreground objects. Finally, Fully Convolutional Network (FCN) generates a binary segmentation mask for each detected class in ROI. The proposed framework evaluated on common shadow detection datasets that have different environmental issues. Experimental results achieved high performance rates compared to several state-of-the-art methods in terms of average detection rate (96.81%), average discrimination rate (99.42%), and overall accuracy (98.09 %).
Towards More Effective App-Assisted Language Learning: The Essential Content and Design Features of Educational Applications

While the use of apps specifically designed for language learning has witnessed a surge over the past decade, research on the productivity of these apps for language learning purposes along with their essential design and content features from students’ lens remains scant. The present paper illustrates an attempt to explore the way using language learning apps throughout an academic semester develops university level students’ semi-technical English language knowledge and the features and qualities they find essential in these educational apps. Drawing on empirical data obtained from pre- and post-tests and App Feature Interview form, it was observed that students who used language learning apps had a significantly better performance in a semi-technical English language post-test compared to the students who did not use language learning apps of their choice. Following content analysis, participants’ responses to an interview question were explored to determine the qualities they found crucial for language learning apps. Students highlighted 18 qualities, which were categorized under three main themes: smartphone, pedagogical content, and design features, as required for effective language learning apps. The findings of this study provide insights for educational app designers and educators on what works better in these apps from users’ perspective.