Reviewerly/exHarmony huggingface dataset:
Data Warehouse and Asset Management Intelligence Architecture for Condition Assessment of Major Equipment in Power Plant

This paper proposes a development on data warehouse and asset management intelligence architecture using MySQL with PHP and JAVA programing language for condition assessment of major assets in generating power plant. The assets consists of power transformer, high voltage air-insulated substation, gas-insulated switchgear, medium voltage switchgear, and underground cable system. The database setup for technical specification, factory test as well as routine inspection and maintenance such as visual inspection, electrical tests, and other special tests for each equipment are mentioned to evaluate the condition of the assets. The health index of each asset is calculated by applying weighting and scoring technique. Finally, the proper maintenance tasks and resource allocation are effectively and efficiently managed to optimize the overall cost.
IoT security: could careless talk cost livelihoods?

The rise of the Internet of Things (IoT) promises exciting capabilities for business but could it usher in risks that are difficult to assess, let alone deal with? In a world where companies can use Alexa to help set up new office IT, could unsecured IoT systems be the equivalent of careless talk giving away company secrets – and endangering livelihoods? The rise of the Internet of Things (IoT) promises exciting capabilities for business but could it usher in risks that are difficult to assess, let alone deal with? Unsecured IoT systems could be the equivalent of careless talk giving away company secrets and endangering livelihoods. IoT-enabled devices behind cutting-edge consumer and business products must talk securely to the company's core IT and business systems. Without this secure conversation, IoT's learning capabilities could simply enable hackers to carry out wider-scale attacks, explains Marc Sollars of Teneo.
Face Image Manipulation Detection

Abstract This paper proposes a CNN-based (Convolutional Neural Network based) network to detect altered face picture, which can cover the most common face swap methods. The network uses an autoencoder which is pre-trained on the original images to reconstruct the input images. The reconstructed one and the input image are then processed by the SRM filter which can extract the noise distribution of images. We then feed the minus result of two processed results into a CNN architecture to predict whether the input image is original or tampered. The model was trained and evaluated in FaceForensics dataset and state-of-art face swap method. Experimental results demonstrate the effectiveness of our network.
Extracting Core Elements of TFM Functional Characteristics from Stanford CoreNLP Application Outcomes

Stanford CoreNLP is the Natural Language Processing (NLP) pipeline that allow analysing text at paragraph, sentence and word levels. Its outcomes can be used for extracting core elements of functional characteristics of the Topological Functioning Model (TFM). The TFM elements form the core of the knowledge model kept in the knowledge base. The knowledge model ought to be the core source for further model transformations up to source code. This paper presents research on main steps of processing Stanford CoreNLP application results to extract actions, objects, results and executors of the functional characteristics. The obtained results illustrate that such processing can be useful, however, requires text with rigour, and even uniform, structure of sentences as well as attention to the possible parsing errors.
Design of a Computerized Educational System about Risks of Social Networking Services for Children

In this paper, we design a chat system aiming at implementing collaborative and interactive lessons in information moral education and propose the design of necessary functions to reduce the workload of a teacher. Also, when using this system in which teachers and students communicate bi-directionally in a lesson, it is necessary to support the operation method of information equipment in parallel while proceeding the lesson. For this reason, it is necessary to estimate beforehand how much time will be required for teaching as well as a large workload on the teacher. Therefore, we propose a method for teachers to know in proceed the time necessary for support to students from the viewpoint of usability while a teacher proceeds a lesson in a chat system.
Modeling Traceability in Software Development: A Metamodel and a Reference Model for Traceability

Many traceability models lack well-defined traceability link types, provide incomplete coverage of situations, do not provide mechanisms to ensure consistency of traceability, and consider only requirements traceability ignoring other activities of development. We propose a set of basic concepts for traceability, a reference model, and a comprehensive metamodel for traceability created using this reference model. The reference model defines: basic elements for traceability, basic actions to be done on artifacts, basic properties that sets of link types and artifact types should have, basic categories that should be realized regarding these sets, and basic set of processes for traceability. The metamodel is composed of a visual model defining how its elements interact, the definition and semantic description of link types and artifact types which realize the categories of the reference model, and a set of detailed processes describing the steps to maintain traceability and system consistency. Our proposal aims to reduce the problems identified; the reference model provides directions to help the creation, or evaluation, of a traceability model; the metamodel provides semantically described traceability link types, coverage of the most common situations, mechanisms to ensure consistency of traceability, and covers the most common activities in software development.
Adversarial Domain Adaptation Being Aware of Class Relationships

Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain adaptation (DA) tasks based on deep neural networks. Until very recently, existing adversarial domain adaptation (ADA) methods ignore the useful information from the label space, which is an important factor accountable for the complicated data distributions associated with different semantic classes. Especially, the inter-class semantic relationships have been rarely considered and discussed in the current work of transfer learning. In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain. Specifically, we impose a regularization term to penalize the structure discrepancy between the inter-class dependencies respectively estimated from domain discriminator and label predictor. Through this alignment, our proposed method makes the adversarial domain adaptation aware of the class relationships. Empirical studies show that the incorporation of class relationships significantly improves the performance on benchmark datasets.
Of Pipes and Patches: Listening to augmented pipe organs

Pipe organs are complex timbral synthesisers in an early acousmatic setting, which have always accompanied the evolution of music and technology. The most recent development is digital augmentation: the organ sound is captured, transformed and then played back in real time. The present augmented organ project relies on three main aesthetic principles: microphony, fusion and instrumentality. Microphony means that sounds are captured inside the organ case, close to the pipes. Real-time audio effects are then applied to the internal sounds before they are played back over loudspeakers; the transformed sounds interact with the original sounds of the pipe organ. The fusion principle exploits the blending effect of the acoustic space surrounding the instrument; the room response transforms the sounds of many single-sound sources into a consistent and organ-typical soundscape at the listener’s position. The instrumentality principle restricts electroacoustic processing to organ sounds only, excluding non-organ sound sources or samples. This article proposes a taxonomy of musical effects. It discusses aesthetic questions concerning the perceptual fusion of acoustic and electronic sources. Both extended playing techniques and digital audio can create musical gestures that conjoin the heterogeneous sonic worlds of pipe organs and electronics. This results in a paradoxical listening experience of unity in the diversity: the music is at the same time electroacoustic and instrumental.
An Approach of Extracting God Class Exploiting Both Structural and Semantic Similarity

Code smell is a sign of design and development flaws in a software system which reduces the reusability and maintainability of the system. Refactoring is a continuous practice of eliminating code smells from the source code. A God Class is one of the most common code smells where too many responsibilities are defined in a single class. God Classes reduce the quality of a system by increasing coupling and decreasing cohesion. In this paper, we propose an approach for extracting a God Class into new classes by increasing class cohesion. For this, both structural and semantic relationship between methods in a class are analyzed, and strongly related methods are clustered and suggested to be in the same class. We assessed the proposed approach on fifteen real God Classes from two well-known open source systems and it is shown that the cohesion among the classes is increased after refactoring. A comparative result of our approach with a similar existing approach is presented and it is found that our approach provides better results for almost all the experimented God Classes.
Cybersecurity by Design for Smart Home Environments

The Internet of Things (IoT) is being increasingly adopted by businesses to improve operations, processes, and products. While it opens endless opportunities, it also presents tremendous challenges especially in the area of cyber risks and related security controls. With billions of interconnected devices worldwide, how do we ensure that they are sufficiently secure and resilient? As a reasonable solution, ‘Cybersecurity by Design’ seems a promising approach. In this research, ‘Smart Homes’ - as IoT containing products – are selected as unit of analysis because they are exposed to numerous cyber threats with corresponding adverse consequences for the life, safety and health of residents. By aiming to secure Smart Home Environments (SHEs) from cyber threats, we adopted ‘design science’ as methodology and developed a holistic approach, highlighting ‘good practices’, which can be applied in every phase of the SHEs product lifecycle. In addition to these good practices, a ‘Cyber Security Maturity Assessment’ tool for SHEs has been developed. Both artefacts have already been validated and incrementally improved, and are now awaiting their future application and further enhancements.
High-accuracy Three-dimensional Measurement by Improving the Asymmetry of Dithered Patterns

Abstract The previously proposed dithering defocusing technology performs well for threedimensional (3D) measurement when stripes are relatively wide, yet suffers if stripes are narrow. This paper finds two asymmetries in dithered patterns generated by the Sierra Lite dithering algorithm and verifies the longitudinal fringes are more advantageous for phase-shifting technique over transverse fringes. Furthermore, this paper proposes an algorithm with a meandering scan. In each pattern, the pixels of odd lines are scanned from left to right while the even lines are scanned from right to left. The proposed method avoids the quantization errors propagating in a specific direction and greatly improves the symmetry of longitudinal fringes. Both simulation and experimental results have shown this method can effectively improve accuracy of 3D measurement especially for narrow stripes.
Gait Recognition and Robust Autonomous Location Method of Exoskeleton Robot Based on Machine Learning

Focusing on the autonomous location method of lower extremity exoskeleton robot in complex environment, this paper presents a gait type recognition method based on support vector machine (SVM)C and an autonomous location method based on inertial information mapping model and system reconstruction. In this paper, support vector machine is used to effectively recognize various kinds of conventional gait types of exoskeleton robots. The inertial information mapping models among different parts of the lower limbs of the robots under different gaits are established respectively. Aiming at the problem of the failure of the inertial measurement unit at the end of the limb in the impact or high overload motion, a robust autonomous location method based on system reconstruction is studied. The experiment results show that, using different neural network model parameters under different gaits can be used to reduce the complexity of the network model. While the inertial measurement unit at the end of the limb fails, the location performance of the exoskeleton robot navigation system based on this method, is equal to that of the inertial navigation system at the end of the limb with same sensor precision.
A New Era of Serverless Computing

Modern-day software engineering practices have evolved due to the fundamental improvements of automation techniques, computational speed, and operational strategies. The latest technologies and cloud platforms empowered software organizations and professionals to quickly adapt to new engineering practices without major technical and operational disruptions. The modernization of core software engineering practices like Agile development, customer focused testing, the Continuous Integration-Validation-Deployment cycle, predictive analytics, and more, have not only changed the process of building software but have also served as a foundation of a new software paradigm, serverless computing.
A Key Sentences Based Convolution Neural Network for Text Sentiment Classification

Abstract Existing research treated all sentences in the text on an equal basis during the training process and did not consider that key sentences tend to have a stronger influence. We propose a Convolutional Neural Network text sentiment classification model based on the key sentences enhancement. The proposed model can identify key sentences in the text and generate text representation based on these key sentences to reduce noise and improve the accuracy of the model sentiment classification. The experiment results show that the proposed model improves the accuracy of the text sentiment classification compared with other classic text sentiment classification models.
Measurement and Analysis of the Stepwise Curved Surface of Diffractive Optical Elements by a Constant Speed Confocal Probe

Diffractive optical elements (DOEs) play an important role in improving imaging quality and reducing system volume. The measurement of DOE surface topography is of great significance to the evaluation of DOE quality and the optimization of its manufacturing process. However, there are still some difficulties in measuring the large curvature and the large sag of curve-based DOEs. In this paper, considering the geometrical feature information and measurement problems of curve-based DOEs, a measurement method based on a confocal measurement principle for DOE measurement is proposed. The proposed measurement data processing method is verified by a high-precision motion axis system and the stability of an ultra-precision lathe. The results show that the proposed measurement method can reconstruct and accurately evaluate the three-dimensional surface topography of DOEs.
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version).

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality. We propose an alternative to these approaches by directly regularizing a black-box model for interpretability at training time. Our approach explicitly connects three key aspects of interpretable machine learning: (i) the model's innate explainability, (ii) the explanation system used at test time, and (iii) the metrics that measure explanation quality. Our regularization results in substantial improvement in terms of the explanation fidelity and stability metrics across a range of datasets and black-box explanation systems while slightly improving accuracy. Further, if the resulting model is still not sufficiently interpretable, the weight of the regularization term can be adjusted to achieve the desired trade-off between accuracy and interpretability. Finally, we justify theoretically that the benefits of explanation-based regularization generalize to unseen points.
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation

We present a domain adaptation based generative framework for zero-shot learning. Our framework addresses the problem of domain shift between the seen and unseen class distributions in zero-shot learning and minimizes the shift by developing a generative model trained via adversarial domain adaptation. Our approach is based on end-to-end learning of the class distributions of seen classes and unseen classes. To enable the model to learn the class distributions of unseen classes, we parameterize these class distributions in terms of the class attribute information (which is available for both seen and unseen classes). This provides a very simple way to learn the class distribution of any unseen class, given only its class attribute information, and no labeled training data. Training this model with adversarial domain adaptation further provides robustness against the distribution mismatch between the data from seen and unseen classes. Our approach also provides a novel way for training neural net based classifiers to overcome the hubness problem in zero-shot learning. Through a comprehensive set of experiments, we show that our model yields superior accuracies as compared to various state-of-the-art zero shot learning models, on a variety of benchmark datasets. Code for the experiments is available at this http URL
Remote access (Off-Campus access) to e-resources via EZproxy and RemoteXs facilities: A case study of Allama Iqbal Library system, University of Kashmir

Remote access (Off-Campus access) to e-resources via EZproxy and RemoteXs facilities: A case study of Allama Iqbal Library system, University of Kashmir - IJLSIT- Print ISSN No: - 2582-1555 Online ISSN No:- 2456-9623 Article DOI No:- 10.18231/j.ijlsit.2019.008, IP Indian Journal of Library Science and Information Techn
Interactive Cross-Lingual Ontology Matching

Recently, with the growing number of ontologies defined in different languages, to bridge the semantic gaps between them, it is necessary to identify the correspondences between their heterogeneous entities, so-called cross-lingual ontology matching. Due to the complexity and the intricacy of the cross-lingual ontology matching, it is essential to get an expert involved in the matching process to guarantee the alignment’s quality. In this paper, we propose an interactive cross-lingual ontology matching technique that makes the user and automatic matcher work together to create high-quality alignments in a reasonable amount of time. In particular, we present a cross-lingual similarity metric to calculate the similarity value of two cross-lingual entities, construct an optimal model for the cross-lingual ontology matching problem, and propose an interactive compact differential evolution (ICDE) algorithm to effectively match the cross-lingual ontologies. The experiment exploits the ontology alignment evaluation initiative (OAEI) multifarm track to test our proposal’s performance. The experimental results show that the ICDE significantly outperforms other EA-based matchers and OAEI’s participants, and the interacting mechanism can significantly improve the alignment’s quality.
Remote Monitoring of Temperature and Humidity—A Reliable and Inexpensive Device Development Applied in Neonatal Incubators

Neonatal incubator is a medical technology responsible for providing thermoneutral environments to newborn, especially those with low weight. This population requires specific levels of temperature and air humidity to decrease metabolic intake and avoid developmental impairements. Based on that, the present work aims to build up an inexpensive and reliable device that monitors mentioned variables and notify via web or mobile when a critical variation is detected. According to the data obtained, we conclude that the project succeed with a good refresh rate and proper functioning of alarms and notifications. The device, build up with inexpensive components, could be broadly used in any space that requires monitored temperature and humidity, resulting in safer environments.
Optimal Latent Representations: Distilling Mutual Information into Principal Pairs

A popular approach for predicting the future of dynamical systems involves mapping them into a lower-dimensional space where prediction is easier. We show that the information-theoretically optimal approach uses different mappings for present and future, in contrast to state-of-the-art machine-learning approaches where both mappings are the same. We illustrate this dichotomy by predicting the time-evolution of coupled harmonic oscillators with dissipation and thermal noise, showing how the optimal 2-mapping method significantly outperforms principal component analysis and all other approaches that use a single latent representation, and discuss the intuitive reason why two representations are better than one. We conjecture that a single latent representation is optimal only for time-reversible processes, not for e.g. text, speech, music or out-of-equilibrium physical systems.
The Effectiveness of English Club as Free Voluntary Speaking Activity Strategy in Fostering Speaking Skill in Saudi Arabia Context.

This study investigates the significance of English club in fostering speaking skill in Saudi Arabia context. It hypothesizes that Saudi university students are enthusiastic to learn authentic English and English club as free voluntary speaking activity with low anxiety assists Saudi university students practice English joyfully. This study aims at identifying the advantages of activating English club in promoting speaking skill and raising university English teachers’ awareness of the importance of using it. The study method used was the descriptive analytic method. The study concludes that English club assists Saudi students be free from language anxiety and traditional boring routine classroom activities , it helps them practice speaking English joyfully and English club fosters critical thinking , persuasive and argumentative skills among students. It recommends that English club should be scheduled as the essential part of a course in the semester not as academic activity, English club should conduct remedial classes concerning speaking strategies and skills which assist students communicate tactfully and the native speakers should help conduct the English club in order to create realistic language context in terms of phonological aspect.