Reviewerly/exHarmony huggingface dataset:
MVC-IMASAM: Model-View-Controller inspired modeling approach for system adaptation management

The implementation of self-adaptive systems requires a concise and effective methodology. However, there is no benchmark methodology in this sector. The variety of the application domain of these systems means that the parameters to be considered, to ensure the adaptation, differ frequently. Nevertheless, there are numerous approaches proposed which have considerably reduced this problem but have not succeeded in solving it. We have presented in this document an approach, which is inspired by the foundation of the Model-View-Controller (MVC), allowing to express the process of adaptation for an information system. We briefly give an explanation on aspect that link MVC to our approach. We present how the approach works and how it is structured. We also present the concrete syntax of the approach in order to make it possible to be used.
The connected electronic health record: a semantic-enabled, flexible, and unified electronic health record

One of the major evolutions on e-health is the digitization of the Electronic Health Record (EHR), which can thereby be shared more easily among hospitals via the Internet. During the last century, the Internet of Things for Healthcare (Healthcare-IoT) appeared. It includes health sensor devices as new sources of health information, as well as new technologies and applications for remote diagnostics and the monitoring of patients, equipment, and drugs. The adoption of IoT in health care, anywhere and anytime, will bring future health care services to a new level and great conveniences to both doctors and patients for effective illness monitoring and diagnosis. The emergence of the IoT has led to a myriad of Medical Connected Objects (MCOs). Accordingly, different associated challenges are emerging, including the heterogeneity of the gathered health data from these MCOs within ever changing contexts. Current studies are limited to specific cases and to specific sensors to deliver their services and do not present a standard integration between patients’ MCOs and EHR systems. This disparity limits cooperation between patients and general practitioners, leading to security, transparency, and privacy issues. These challenges are difficult to address due to the absence of a reference model for describing health data and their sources and for linking this data with their contexts. This book chapter addresses this problem and introduces a semantic-based model for flexible and customized EHR for patient monitoring with MCOs. In fact, this approach exploits the HL7 FHIR (Fast Healthcare Interoperability Resources) standard and UMLS (Unified Medical Language System) terminology to handle integration challenges and supports the semantic integration of the EHR system. The main goal is to provide semantic-connected EHR that will facilitate data interoperability, integration, information search and retrieval, and automatic inference and adaptation in real time.
Revisiting Our Quantum World

We now know that quantum mechanics has been a fundamental structure of our world since the universe came into being. However, it has been only a century since the experimental and theoretical discoveries of quantum mechanics were made. We are becoming increasingly aware of its many implications and applications. In particular, there are implications across many disciplines that most likely will affect education, health, and security. Examples are given of the need to start education as early as possible in schools, the use of nano-robots to deliver drugs targeted to specific molecular sites, and to developing new cryptographic systems to safeguard our privacy.
Development of augmented reality as physics learning media on electric concepts

Abstract The 21st Century learning requires technology in the learning process, including physics learning using abstract concepts. Furthermore, many teachers do not ap-ply technology in the classroom because of the teacher’s limitations in making learning media. The purpose of this research was to create an augmented reality-based learning media that can be used in classroom learning. The research method used in this research was Research and Development. The development procedure consists of a preliminary, design, development and implementation. In the preliminary study phase aims to analyse the needs of the world of education so that the direction of design and development to be made clearer and more precise. In the design and development phase, the researcher designed and created an augmented reality application and developed this augmented reality from a marker card to a real object marker. In the development stage augmented reality was judged by physicists and experts in the field of technology. After being judged, the application made was implemented and tested on 10 types of devices with different OS and implemented in classroom learning. Based on the test results, it can be concluded that the augmented reality application that has been made with successfully tested using several different devices and validated by experts and de-cleared eligible or good for use.
Development of Fast Refinement Detectors on AI Edge Platforms

Refinement detector (RefineDet) is a state-of-the-art model in object detection that has been developed and refined based on high-end GPU systems. In this study, we discovered that the speed of models developed in high-end GPU systems is inconsistent with that in embedded systems. In other words, the fastest model that operates on high-end GPU systems may not be the fastest model on embedded boards. To determine the reason for this phenomenon, we performed several experiments on RefineDet using various backbone architectures on three different platforms: NVIDIA Titan XP GPU system, Drive PX2 board, and Jetson Xavier board. Finally, we achieved real-time performances (approximately 20 fps) based on the experiments on AI edge platforms such as NVIDIA Drive PX2 and Jetson Xavier boards. We believe that our current study would serve as a good reference for developers who wish to apply object detection algorithms to AI edge computing hardware. The complete code and models are publicly available on the web (link) .
Lessons from a broad survey of tutoring tools: It's a big world out there

We created a list of more than 140 tools that can be used to create tutoring systems, from complete tutoring systems to low-level tools for preparing instructional materials. Based on this list, we present a preliminary ontology of system dimensions that can serve as a base for a comprehensive review or in building systems. We also note that: (a) such surveys of tutoring systems will always be incorrect because they are incomplete, (b) such reviews are not worth pursing indefinitely (because they will never be complete), and, however, (c) comprehensive reviews, or a “living platform” like a wiki, may be immensely useful for stakeholders. The results provide several insights: (a) the scope of tutoring systems includes a great range of dissimilar tutoring architectures, as well as an even wider range of supplementary tools; (b) systems vary on many dimensions (we provide an initial set); and (c) no tutoring system is likely to gain widespread adoption for all use cases because of the variety of learner and other stakeholder needs. This suggests that a wide variety of tutoring architectures are needed. The ontology and dimensions may be helpful in development, funding, and acquisition decisions.
Utilizing Local Tangent Information for Word Re-embedding

Word embedding models typically learn dense and fixed-length vectors based on local word collocation patterns in a text corpus. Recent studies have discovered that these models often underestimate similarities between similar words and overestimate similarities between distant words. This leads to word similarity results obtained from word embedding models inconsistent with human judgment. A number of manifold learning-based word re-embedding methods are proposed to address this problem by re-embedding word vectors from the original embedding space to a new embedding space. However, these methods perform a weighted locally linear combination of embeddings of words and their neighbors twice. Besides, the reconstruction weights are easily influenced by the selection of word neighbors and the whole combination process is very time-consuming. In this paper, we introduce a novel word re-embedding method based on local tangent information to re-embed word vectors into a refined new space. Unlike previous approaches, our method re-embeds word vectors by aligning original and new embedding spaces based on the tangent information instead of performing weighted locally linear combination twice. To validate the proposed method, experiments were conducted on two standard evaluation tasks. The experimental results show that our method achieves better performance than state-of-the-art methods for word re-embedding.
Insuring against the perils in distributed learning: privacy-preserving empirical risk minimization

<abstract> Multiple organizations would benefit from collaborative learning models trained over aggregated datasets from various human activity recognition applications without privacy leakages. Two of the prevailing privacy-preserving protocols, secure multi-party computation and differential privacy, however, are still confronted with serious privacy leakages: lack of provision for privacy guarantee about individual data and insufficient protection against inference attacks on the resultant models. To mitigate the aforementioned shortfalls, we propose privacy-preserving architecture to explore the potential of secure multi-party computation and differential privacy. We utilize the inherent prospects of output perturbation and gradient perturbation in our differential privacy method, and progress with an innovation for both techniques in the distributed learning domain. Data owners collaboratively aggregate the locally trained models inside a secure multi-party computation domain in the output perturbation algorithm, and later inject appreciable statistical noise before exposing the classifier. We inject noise during every iterative update to collaboratively train a global model in our gradient perturbation algorithm. The utility guarantee of our gradient perturbation method is determined by an expected curvature relative to the minimum curvature. With the application of expected curvature, we theoretically justify the advantage of gradient perturbation in our proposed algorithm, therefore closing existing gap between practice and theory. Validation of our algorithm on real-world human recognition activity datasets establishes that our protocol incurs minimal computational overhead, provides substantial utility gains for typical security and privacy guarantees. </abstract>
Detecting Semantically Equivalent Questions Using Transformer-Based Encodings

Due to the increasing influx of users on various Q and A forums like Quora, Stack Overflow, etc., answers to questions get fragmented across different versions of the same question due to this redundancy of questions. However, if questions which are lexically or semantically identical could be grouped, the search results for a given question could yield the assimilation of answers provided for all versions of a question. In this paper, an ensemble of four separate models is created using four different word-embedding techniques in each. The vectorized data from each of the embedding layers is passed through a custom-made architecture using transformer-based encodings. This architecture is used to determine if a pair of questions have the same semantic meaning or not. Finally, on testing the model on a subset of the provided data, the experiments show that the proposed model achieves an F1 score of 84.63%
DBSCAN Is Semi-Spectral Clustering

Spectral clustering and DBSCAN are two famous clustering methods, the former reduces data dimensionality by spectrum of similarity matrix, and then utilizes kmeans to cluster data in low dimensional space. While DBSCAN performs clustering by finding different density regions that depart from each other, which makes it seems quite different from spectral clustering, and there is little literal discusses the relationship between them. In this paper, we revisit DBSCAN from Similarity Graph and Graph Cut point of views, uncover the underlying relationship between DBSCAN and spectral clustering, which proves that DBSCAN can be explained and rewritten under the framework of spectral clustering. Furthermore, eigenvectors are often approximately resolved, and k-means is usually used in the final stage, often converges in local optimization. Hence, we rewrite spectral clustering by using nearest neighbor query instead of k-means to obtain exact result. Experimental results address that the revised spectral clustering method can obtain the same result as DBSCAN on core point set. Therefore, we come to the conclusion that DBSCAN is semi-spectral clustering. The work of this paper theoretically illustrates that spectral clustering is as good as DBSCAN, and both algorithms can be replaced with each other to avoid their own disadvantages.
Single Source One Shot Reenactment using Weighted Motion from Paired Feature Points

Image reenactment is a task where the target object in the source image imitates the motion represented in the driving image. One of the most common reenactment tasks is face image animation. The major challenge in the current face reenactment approaches is to distinguish between facial motion and identity. For this reason, the previous models struggle to produce high-quality animations if the driving and source identities are different (cross-person reenactment). We propose a new (face) reenactment model that learns shape-independent motion features in a self-supervised setup. The motion is represented using a set of paired feature points extracted from the source and driving images simultaneously. The model is generalised to multiple reenactment tasks including faces and non-face objects using only a single source image. The extensive experiments show that the model faithfully transfers the driving motion to the source while retaining the source identity intact.
Quantifying the Reliable Discharges as an Incipient Analysis of Agricultural Planning and Developing in Ciujung Watershed

Abstract Rainfall-run-off modelling could estimate the surface water discharges according to the rainfall data record. The incremental rainfall derives the reliable rainfall in many probabilities, and then it becomes reliable discharges after rainfall-run-off modelling processes. The standard models to derive rainfall to discharges are F.J. Mock and NRECA. Both models use rainfall, meteorology, and climatology data as the primary information. Location of this research was in the Ciujung watershed, Banten province. The objective of this research was estimating the potential surface water in Ciujung watershed. Rainfall data recording from January 1997 until December 2018 will derive the reliable rainfall of 50%, 70%, 80%, 90% and 99% probability. The meteorology and climatology information were following the days of rainfall, sunshine ratio, wind velocity, air temperature, and relative humidity. The results give information about the potential of reliable discharges in the Ciujung watershed. As the agricultural planning and developing purpose, the 80% reliable discharges Q80 becomes the fundamental consideration. It forecasts maximum discharges in a range of 60 m 3 /s to 80 m 3 /s and the minimum discharges in the range of 0.1 m 3 /s to 0.6 m 3 /s. These values become the threshold in agricultural planning and development. The advance analysis is quantifying the human needs of water, and the remaining value can be as the potential discharges for agricultural purpose. Further research will accommodate these analyses.
Incorporating Discriminative DPGMM Posteriorgrams for Low-Resource ASR

The first step in building an ASR system is to extract proper speech features. The ideal speech features for ASR must also have high discriminabilities between linguistic units and be robust to such non-linguistic factors as gender, age, emotions, or noise. The discriminabilities of various features have been compared in several Zerospeech challenges to discover linguistic units without any transcriptions, in which the posteriorgrams of DPGMM clustering show strong discriminability and get several top results of ABX discrimination scores between phonemes. This paper appends DPGMM posteriorgrams to increase the discriminability of acoustic features to enhance ASR systems. To the best of our knowledge, DPGMM features, which are usually applied to such tasks as spoken term detection and zero resources tasks, have not been applied to large vocabulary continuous speech recognition (LVCSR) before. DPGMM clustering can dynamically change the number of Gaussians until each one fits one segmental pattern of the whole speech corpus with the highest probability such that the linguistic units of different segmental patterns are clearly discriminated. Our experimental results on the WSJ corpora show our proposal stably improves ASR systems and provides even more improvement for smaller datasets with fewer resources.
MurQRI: Encrypted Multi-layer QR Codes for Electronic Identity Management

Quick Response (QR) codes are widely used due to their versatility and low deployment cost. However, the existing QR code standard is ineffective for security-critical applications (e.g., electronic identity management) as the stored information can be easily exposed to unauthorized parties. Moreover, it does not provide sufficient storage capacity to employ robust encryption schemes for complex access control and authentication. In this paper, we present a novel approach of employing encrypted multi-layer QR codes, MurQRI (pronounced “Mercury”), for secure user authentication and fine-grained access control in various domains (e.g., airport and hospital). MurQRI is designed to store up to 45 kilobytes of data and protect the stored information via biometric authentication and encryption. To support fine-grained access control, we employ attribute-based encryption. We also introduce real-world applications where MurQRI can be used effectively and discuss possible methods to enhance security.
Concept Design of Robot-Mechanic for Transport Maintenance Service

The field of robotics has made such a progress in the modern world when robots are conceived to be designed in the vein of their intended vocation. Such a robot could substitute for a human in a certain occupation at his workplace, especially, in difficult, hard, hazardous, or extreme working conditions. Designing a robot-mechanic for transport maintenance service fits into that world trend of the applied robotics. A robot-mechanic would be able to function as a consultant, inspector, maintenance person or in many other roles for industrial or service purposes, in civil or military sectors of the economy. To carry out such tasks, the robot has to feature certain anthropomorphic characteristics, including a strong artificial intelligence (AI). Electronic, in particular, neural network, robot-control systems are successful just at solving problems which require a weak AI. But problems of a strong AI cannot be solved only by mechatronic control systems. Need arises of such systems which could simulate reasoning and deduction of a human. Most simple of those, and therefore more easily applicable at initial stages of realizing a project of robot-mechanic development, are thought to be expert control systems. They require usage of knowledge extraction procedure on the basis of process charts for transport maintenance. This research focuses on the most common conceptual problems in designing a robot-mechanic for transport maintenance service.
Multi-head Attention with Hint Mechanisms for Joint Extraction of Entity and Relation

In this paper, we propose a joint extraction model of entity and relation from raw texts without relying on additional NLP features, parameter threshold tuning, or entity-relation templates as previous studies do. Our joint model combines the language modeling for entity recognition and multi-head attention for relation extraction. Furthermore, we exploit two hint mechanisms for the multi-head attention to boost the convergence speed and the F1 score of relation extraction. Extensive experiment results show that our proposed model significantly outperforms baselines by having higher F1 scores on various datasets. We also provide ablation tests to analyze the effectiveness of components in our model.
Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning

Indirect encoding is a promising area of research in machine learning/evolutionary computation, however, it is rarely able to achieve performance on par with state of the art directly encoded methods. One of the most important properties of indirect encoding is the ability to control exploration during learning by transforming random genotypic variation into an arbitrary distribution of phenotypic variation. This gives indirect encoding a capacity to learn to be adaptable in a way which is not possible for direct encoding. However, during normal objective based learning, there is no direct selection for adaptability, which results in not only a missed opportunity to improve the ability to learn, but often degrading it too. The recent meta learning algorithm MAML makes it possible to directly and efficiently optimize for the ability to adapt. This paper demonstrates that even when indirect encoding can be detrimental to performance in the case of normal learning, when selecting for the ability to adapt, indirect encoding can outperform direct encoding in a fair comparison. The indirect encoding technique Hypernetwork was used on the task of few shot image classification on the Omniglot dataset. The results show the importance of directly optimizing for adaptability in realizing the powerful potential of indirect encoding.
Pl/sql design to determine the training data pattern on the Adaline neural network

Abstract Inventories of goods in business activities greatly affect the smooth running of business activities. The durian fruit sales business is also affected by inventory. Durian fruit sellers must be able to predict the needs of durian fruit customers so that sales business activities run stable. For sellers with low durian fruit sales, they will have no difficulty predicting demand for durian fruit. But for sellers of durian fruit in large quantities, it will take a long time and is prone to errors. An error in the prediction causes the sales business to lose. It takes high accuracy and fast time to make predictions. Adaline neural network has been proven to be able to solve prediction problems precisely. Adaline network performance is affected by training data. Durian fruit sales data is used as network training data to improve network performance. The focus of this research is the design of the pl/sql database to determine the training data input pattern in the android application to predict the number of durian fruit customer needs. Types of pl/sql used in this study are procedures and functions. Meanwhile, the method used to design pl/sql is an extreme method.