Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF EXPERT ASSESSMENTS OF INDICATORS OF PROFESSIONAL READINESS OF FUTURE TEACHERS FOR THE ORGANIZATION OF EDUCATIONAL EXHIBITIONS

the article presents a study of approaches to the organization of professional training of future teachers for innovative activities. The author, based on a survey of experts (n=56), developed and described the indicators and levels of readiness of future teachers to organize educational exhibitions. Based on the results of an empirical study, the principles of organizing professional training of students of a pedagogical university for organizing educational exhibitions are described
Concept of Active Wireless Sensor Network in Checkability Aspect

This paper focuses on the development of the concept of an active wireless sensor network (WSN) as a basis for improving the checkability of high-risk facilities in post-accident monitoring. The use of a drone system as WSN nodes provides a mobility network, the advantages of which are analyzed from the standpoint of improving the controllability and observability of the object in difficult post-accident conditions. The possibilities of renewing the resources of the mobile WSN, quick adaptation to the realities of training and reconfiguration as factors of ensuring high checkability of post-emergency objects are investigated. An analogy is drawn in the rating of checkability for digital circuits in critical systems and post-accident checkability of increased risk objects. The possibilities of active WSN, provided by its mobility, are analyzed.
A secure extended LBP data hiding scheme based on octagon-shaped shell

Data hiding is an important technique in the research area of information security. A lot of methods have been proposed to embed secret data into different formats of compressed images. The JPEG and AMBTC formats attract much attention. The extended LBP (ELBP) is a new subject to deal with. In this paper, we apply the reference matrix-based data hiding technique to the ELBP cover images. Our major objective is to improve the security level of embedding scheme. Experimental results show that the proposed method not only improves the security but also enhances visual quality of the produced stego images. In addition, the new scheme provides a more flexible embedding capacity for different applications.
Metric Learning Based Vision Transformer for Product Matching

Product image matching is essential on e-commerce platforms since the target customers have to distinguish the same products organized in varied manners by different companies. Machine learning methods can be applied for such kind of task, which treat it as a classification problem. However, it is intrinsically intractable to enforce the model to expand to new products due to its invisibility to unknown categories. Metric learning has emerged as a promising approach to address this issue, which is designed to measure the distances between data points. In this paper, a metric learning based vision transformer (ML-VIT) is proposed to learn the embeddings of the product image data and match the images with small Euclidean distances to the same products. The proposed ML-VIT adopts Arcface loss to achieve intra-class compactness and inter-class dispersion. Compared with Siamese neural network and other pre-trained models in terms of F1 score and accuracy, ML-VIT is proved to yield modest embeddings for product image matching.
An Intelligent Recommendation-cum-Reminder System

Intelligent recommendation and reminder systems are the need of the fast-pacing life. Current intelligent systems such as Siri, Google Assistant, Microsoft Cortona, etc., have limited capability. For example, if you want to wake up at 6 am because you have an upcoming trip, you have to set the alarm manually. Besides, these systems do not recommend or remind what else to carry, such as carrying an umbrella during a likely rain. The present work proposes a system that takes an email as input and returns a recommendation-cum-reminder list. As a first step, we parse the emails, recognize the entities using named entity recognition (NER). In the second step, information retrieval over the web is done to identify nearby places, climatic conditions, etc. Imperative sentences from the reviews of all places are extracted and passed to the object extraction module. The main challenge lies in extracting the objects (items) of interest from the review. To solve it, a modified Machine Reading Comprehension-NER (MRC-NER) model is trained to tag objects of interest by formulating annotation rules as a query. The objects so found are recommended to the user one day in advance. The final reminder list of objects is pruned by our proposed model for tracking objects kept during the ”packing activity.” Eventually, when the user leaves for the event/trip, an alert is sent containing the reminding list items. Our approach achieves superior performance compared to several baselines by as much as 30% on recall and 10% on precision.
A Personalized Advertising Push Framework Based on Measurement Similarity and Data Integration

Efficient and accurate screening of key information with high credibility and user interest from massive information and data is one of the research focuses of the information service industry. The pull service method based on search engines and the information push service are currently the two main channels for obtaining information. The level of economic development in rural areas in my country is lagging, and farmers' cultural qualities are generally low. It is not realistic to adopt search engine-based information acquisition methods, and information push services are more suitable for rural areas. Based on the measurement of similarity and data integration, this paper studies the personalized advertising push framework used in the Internet era.
Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes

Abstract This chapter provides elements for implementing deep neural networks (deep learning) for continuous outcomes. We give details of the hyperparameters to be tuned in deep neural networks and provide a general guide for doing this task with more probability of success. Then we explain the most popular deep learning frameworks that can be used to implement these models as well as the most popular optimizers available in many software programs for deep learning. Several practical examples with plant breeding data for implementing deep neural networks in the Keras library are outlined. These examples take into account many components in the predictor as well many hyperparameters (hidden layer, number of neurons, learning rate, optimizers, penalization, etc.) for which we also illustrate how the tuning process can be done to increase the probability of a successful application.
Paraphrase Identification with Neural Elaboration Relation Learning

Paraphrases are phrases/sentences transition preserving the same sense but using different wording. In contrast, in the case that a phrase/sentence gives more detail compared with another phrase/sentence, it does not hold paraphrases. This paper follows the assumption and verifies how elaboration relation between phrases/sentences helps to improve the performance of the paraphrase identification task. We present a sequential transfer learning framework that utilizes contextual features learned from elaboration relation for paraphrase identification. The method learns the elaboration relation model at first until the stable and then adapts paraphrase identification. The results using the benchmark dataset, Microsoft Research Paraphrase Corpus (MRPC), show that the method attained at 1.7% accuracy improvement compared with a baseline model.
Dynamic Curriculum Learning with Co-training for Medical Dialogue Generation

The purpose of medical dialogue generation is to provide automatic and accurate responses to help doctors provide diagnosis and treatment advice in an efficient w ay. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dataset varies greatly. Some methods try to solve this problem by using curriculum learning that organizes training data from easy to hard and optimizes the model with an “easy-to-difficult” scheme, but they ignore the intrinsic nature of conversation dataset that has different categories. To solve this problem, in this article, we learn a Dynamic Curriculum with Co-training (DCC) for generative dialogue systems considering both the quality and category of the data. Under our framework, we first t rain two models (query generation model and reply generation model) from dual view by leveraging end-to-end deep Gaussian Mixture Variational Autoencoders (GMVAE) architectures that combine generative and unsupervised clustering tasks together. Then we promote the clustering performance via an ensemble method by combining the clustering distribution of the two networks. Finally, we let the two models determine the training order for each other in each category according to their loss and category confidence dynamically through single-task and multitask curriculum learning. Evaluation results on the widely used medical dialogue generation dataset indicate that our proposed learning approach makes significant improvements compared to strong baselines. varies greatly.
How to Prove Work: With Time or Memory

Proposed by Dwork and Naor (Crypto’ 92) as an anti-spam technique, proof-of-work is attracting more attention with the boom of cryptocurrencies. A proof-of-work scheme involves two types of participants, i .e., provers and verifiers. Provers are asked to solve a computational puzzle, and verifiers need to check the solution’s correctness. A widely adopted hash-based construction achieves an optimal gap in computational complexity between provers and verifiers. However, in industry, proof-of-work is done by highly dedicated hardware, e.g., “ASIC”, which is not generally accessible, let alone the high energy consumption rates. In this work, we turn our eyes back to the original meaning of “proof of work”. Under a trusted setting, we propose a framework and its constructions based on computationally hard problems and the unified definition of hard cryptographic primitives by Biryukov and Perrin (Asiacrypt’ 17). The new framework enables us to have a proof-of-work scheme with time-hardness or memory-hardness while cutting down power consumption and reducing the impact of dedicated hardware.
Task-based Classification of Reflective Thinking Using Mixture of Classifiers

This paper studies the problem of Reflective Thinking in children during mathematics related problem solving activities. We present our approach in solving task 2 of the AffectMove challenge, which is Reflective Thinking Detection (RTD) while solving a mathematical activity. We utilize temporal data consisting of 3D joint positions, to construct a series of classifiers that can predict whether the subject appeared to possess reflective thinking ability during the given instance. We tackle the challenge of highly imbalanced data by incorporating and analyzing several meaningful data augmentation techniques and handcrafted features. We then feed different features through a number of machine learning classifiers and select the best performing model. We evaluate our predictions on multiple metrics including accuracy, F1 score, and MCC to work towards a generalized solution for the real-world dataset.
Phenonizer: A fine-grained phenotypic named entity recognizer for Chinese clinical texts

Biomedical named entity recognition from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities, which could be used for profiling the clinical characteristics of patients in specific disease conditions. However, general approaches mostly rely on the coarse-grained annotations (e.g. mentions of symptom terms) of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g. “no fever”, “no cough” and “no hypertension”) in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. Thus, we constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer (Phenonizer). The results on the test set show that Phenonizer outperform those methods based on Word2Vec with Fl-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms, and avoids the interference of negated symptoms. Finally, we tested the generalization performance of Phenonier, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonier proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.
Distributed sensor for earthquake identification system to activate tsunami shelter finding system

Padang City as the capital of West Sumatera Province is at high risk of earthquakes and tsunamis due to its location between two continental plates and the Semangko Fault. Currently, there are several shelters in Padang that serve as evacuation sites as a rescue location when a tsunami hits. This paper presents an earthquake detection system that uses distributed sensors to activate the shelter search system. The proposed system will activate the shelter search system when the earthquake has medium or high magnitude intensity. The earthquake identification system is achieved by calculating the Peak Ground Acceleration (PGA) value from the p-wave and s-wave using piezoelectric and accelerometer sensors. The proposed system is a distributed node sensor placed in different shelters which communicate using MQTT protocol. To evaluate the system, system for earthquake detection using Raspberry Pi, piezoelectric sensor, accelerometer MPU-6050, and Xbee for data communication have been implemented. The result shows that the system can detect the magnitude and intensity of the earthquake.
Proxy Indicators for the Quality of Open-domain Dialogues

The automatic evaluation of open-domain dialogues remains a largely unsolved challenge.Thus, despite the abundance of work done in the field, human judges have to evaluate dialogues' quality.As a consequence, performing such evaluations at scale is usually expensive.This work investigates using a deep-learning model trained on the General Language Understanding Evaluation (GLUE) benchmark to serve as a quality indication of open-domain dialogues.The aim is to use the various GLUE tasks as different perspectives on judging the quality of conversation, thus reducing the need for additional training data or responses that serve as quality references.Due to this nature, the method can infer various quality metrics and derive a component-based overall score.We achieve statistically significant correlation coefficients of up to 0.7.
Conclusion

Abstract Summarising the book’s findings, algorithmic decentralisation is shown to be an inherent contradiction as spatial trajectories coalesce at different points around blockchain networks. This provides a starting point for understanding the economic geographies of distributed blockchain networks that, on one hand, are open for all to see and, on the other, work out of view underneath the surface of cryptographically concealed code. By following Bitcoin into different aspects of its network, its money/code/space is revealed to be a product of complex webs of humans and non-humans formed through cultural-economic practice as opposed to an autonomous machine world. In doing so, the Conclusion works to debunk the libertarian and liberatory claims of cryptocurrencies by illuminating modes of uneven power. It is argued: only by understanding these limitations can pathways be taken to building less inequitable, or at least sensationalist, blockchain forms.
The Construction of a Decentralized Digital Certification System for Cultural Archives Management Under the Background of Big Data

The advent of the data age is impacting the entire world, changing people's lives, work, and thinking. The advent of the big data era has also had a great impact and influence on the management work, and it is a new test of the archives department's archives management ability and level. The new data distributed storage technology-blockchain, provides new methods and ideas for data circulation and sharing through the characteristics of decentralization, timing, distributed ledger, open consensus, openness and transparency, and anti-tampering. Based on big data, this paper studies the construction of a decentralized digital authentication system for cultural archives management.
In-Corpo-Real Robot-Dreams: Empathy, Skin, and Boundaries

This document is part of the submission for the doctoral consortium on affective computing and outlines motivation, theoretical background, and my research plan for the PhD project on empathy and social robots. My project idea can be divided into two parts where the first is focusing on theoretical analyses of empathy through binary conceptualisations and re-configuring empathic processes for human-robot-interaction (HRI). I will be drawing from feminist philosophy and empirical work studying signal processing from measurements on the skin and in machines in order to build a model for empathy as a process of crossing boundaries. In the second part I plan to consider implementations of these theoretical ideas in the design of empathic robots. The first part is aiming to understand and dissolve conceptual boundaries whereas the second part is re-establishing material and conceptual boundaries in order to contribute to ethical affective robot design.
Image Edge Sharpening via Heaviside Substitution and Structure Recovery

In this paper, we propose a simple but efficient method to sharpen image edges. Starting from an image with blurry edges, we improve edge sharpness using transformed approximated Heaviside functions (AHFs) for better visualization. In particular, we provide an efficient method for directly computing the scaling and shifting factors of the transformed AHFs, so that blurred edges can be improved accurately. To recover more image structures, we give a sharp edge-preserving reconstruction model that is based on the result of the edge sharpening strategy. A gradient descent algorithm can efficiently solve the reconstruction model. Moreover, we apply the proposed method to image super-resolution and deblurring. Extensive experimental results demonstrate that the proposed method can efficiently obtain competitive performance, both visually and quantitatively.
An Agile Approach to Distributed Network Orchestrationan Agile Approach to Distributed Network Orchestration

This study investigates the outcomes of an agile approach to distributed network orchestration. Existing literature mainly focuses on centralized orchestration and capability development within firms. However, in case of power symmetry between network actors, orchestration responsibilities are distributed and requires capability development between firms. Drawing on action research, this paper presents results from a Dutch network which developed a new solution for obsolete spare parts in the railroad sector. Within this case, several agile-based interventions were made to address emerging collaboration issues. The findings in this paper suggest that agile practices influence the building of actor bonds, development of activity patterns, utilization of resource constellations and collective sensemaking. We are, to the best of our knowledge, the first to demonstrate that an agile approach contributes to network orchestration. Also, this research shows that orchestration capabilities are developed between firms instead of within firms. Agile practices provide a framework that shapes interaction patterns and facilitate capability development between firms. As such, agile practices can be considered micro-foundations for developing network orchestration capabilities between actors.
Versatile Circuit Designs of Digital Modulator and Demodulator

Digital Modulation has a complete scope of appli-cations due to its higher quality communications, compatibility, and security. The digital modulation techniques are being imple-mented in WiFi, RF modules, Bluetooth, and GSM. In this paper, we proposed versatile hardware designs that perform various modulations and demodulations with the same hardware. The generated versatile design can be configured with the control line to give the following modulations and demodulations: Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), Phase Shift Keying (FSK), Differential Phase Shift Keying (DPSK), Quadrature Phase Shift Keying (QPSK), and Minimum Shift Keying (MSK). The proposed generic design is able to cut down the expenditure of the hardware, as it replaces all the modulation circuits with a single optimized circuit.
Crypto Currencies: The Flag Bearer of Future Finance

Cryptocurrencies, as a part of decentralized financial system and powered by distributed ledger technology is the future of finance. Although no one is responsible for the ledger, no one can control or falsify the claims made through blockchain technology. Cryptocurrencies backed by blockchain are transformative technology and a forerunner of virtual monetary system. Cryptocurrencies are created out of perceived ‘privacy’ need and drive to exit government intermediaries and financial institutions as a regulator of financials. Thus, Government should focus on timely policy planning, implementation and governance of blockchain and virtual currencies so as to prevent halt in data economy.
Optimal Virtual Machine Allocations in Cloud Computing System

Cloud Computing (CC) is a popular computing paradigm by providing virtual resources as services over the internet. In the CC system, each user request can get services on a pay as you use basis. There is reward and system cost for serving each request, and the service provider can decide the amount of resources to assign to each request. Regardless of the random request arrivals and departures, a novel model of the Continous-Time Markov Decision Process (CTMDP) is explored. In this paper our goal is to maximize the total discounted expected reward for service providers. With the solution of the Bellman optimality equation by the value iteration method, an optimal policy can be achieved.
Finite Impulse Response Filter-Based Track Formation for Preceding Vehicle Tracking Using Automotive Radars

Automotive radars, which are used for preceding vehicle tracking, have attracted significant attention in recent years. However, the false measurements that occur in cluttered roadways hinders the tracking process in vehicles; thus, it is essential to develop automotive radar systems that are robust against false measurements. This study proposed a novel track formation algorithm to initialize the preceding vehicle tracking in automotive radar systems. The proposed algorithm is based on finite impulse response filtering, and exhibited significantly higher accuracy in highly cluttered environments than a conventional track formation algorithm. The excellent performance of the proposed algorithm was demonstrated using extensive simulations under real conditions.