Reviewerly/exHarmony huggingface dataset:
Smart Contracts for Global Sourcing Arrangements

While global sourcing arrangements are highly complex and usually represent large value to the partners, little is known of the use of e-contracts or smart contracts and contract management systems to enhance the contract management process. In this paper we assess the potential of emerging technologies for global sourcing. We review current sourcing contract issues and evaluate three technologies that have been applied to enhance contracting processes. These are (1) semantic standardisation, (2) cognitive technologies and (3) smart contracts and blockchain. We discuss that each of these seem to have their merit for contract management and potentially can contribute to contract management in more complex and dynamic sourcing arrangements. The combination and configuration in which these three technologies will provide value to sourcing should be on the agenda for future research in sourcing contract management.
AUTONOMOUS CORRECTION OF A SINGLE-CHANNEL INERTIAL CONTROL SYSTEM

The problem of algorithmic design of the system of inertial control of a moving object is considered, in which the compensation of number errors caused by errors of accelerometers without external sources of information about the navigation elements of the object is carried out. The principle of solving the problem is demonstrated on the model of a single-channel positional inertial control system. Algorithms of instantaneous a posteriori estimation of calculated variables are investigated, which allow to obtain estimates that are invariant to measurement errors, and to correct the inertial control channel without an external positioning system. For the operating conditions of the system, under which the values of measurement errors and the disturbing force are represented by random numbers that preserve constant values over the observation interval, estimates of the calculated variables corresponding to almost complete compensation of the calculation errors are obtained.
Total PV Power Prediction over a Wide Area Using Forecasted Values of Solar Radiation at Multiple Observation Points

Electric retailers need to predict spot market prices in the Japan Electric Power Exchange for efficient daily power procurement. And it is necessary to predict demand and PV output when predicting the price. Therefore, this paper deals with the simple and accurate estimation methods of total PV power over a wide area. We propose two kinds of simple methods to calculate the total PV output in the Kanto and Tohoku areas in Japan based on the predicted values of solar radiation at multiple observation points that are easy to obtain from the Japan Meteorological Agency.
The Bird Is Not the Migration Data: Insights from an Artwork Exploring Machine-Animal Collaboration

This paper documents the process of building a Python-based musical synthesizer that generates musical scores from GPS drift, specifically related to the movement of Golden eagles. The paper walks through the design process of the synthesizer, engaging in philosophical questions that explore data gathering practices and interpretation through the lens of art, poetics, and imagination. It concludes by addressing objectivity and neutrality as forms of an imaginary which, if acknowledged and engaged as such, could create a framework for valuing greater diversity in data science. This paper documents the process of building a Python-based musical synthesizer that generates musical scores from GPS drift, specifically related to the movement of Golden eagles. The paper walks through the design process of the synthesizer, engaging in philosophical questions that explore data gathering practices and interpretation through the lens of art, poetics, and imagination. It concludes by addressing objectivity and neutrality as forms of an imaginary which, if acknowledged and engaged as such, could create a framework for valuing greater diversity in data science.
Classification of Tibetan Compounds and Their Modeling in the Formal Grammar and the Computer Ontology

This article presents the continuation of work on a consistent formal grammatical and ontological description of the model of the Tibetan compounds system, developed and used for automatic syntactic and semantic analysis of Tibetan texts, on the material of a hand-verified corpus. The study of new texts made it possible to develop and correct classes, created for different types of compounds in the formal grammar. New types of compounds were also discovered. In addition, the current research contains the description of the external semantic relations between concepts denoted by compounds components and the overall meaning of a compound.
Incremental Search for Conflict and Unit Instances of Quantified Formulas with E-Matching

We present a new method to find conflicting instances of quantified formulas in the context of SMT solving. Our method splits the search for such instances in two parts. In the first part, E-matching is used to find candidate instances of the quantified formulas. In principle, any existing incremental E-matching technique can be used. The incrementality avoids duplicating work for each small change of the E-graph. Together with the candidate instance, E-matching also provides an existing node in the E-graph corresponding to each term in this instance. In the second part, these nodes are used to evaluate the candidate instance, i.e., without creating new terms. The evaluation can be done in constant time per instance. Our method detects conflicting instances and unit-propagating instances (clauses that propagate new literals). This makes our method suitable for a tight integration with the DPLL(\(\mathcal {T}\)) framework, very much in the style of an additional theory solver.
Generative Model based Data Augmentation for Special Person Classification

Big data leads to a great success of deep learning in computer vision. Unfortunately, big datasets are often not balanced in all dimensions and rare cases are often underrepresented. On-board data collection by a moving vehicle can capture thousands of normal pedestrians and vehicles, but what about special persons like police officers, road workers, and school guards? Not only that those types of classes are hard to get, they are crucial to be recognized and classified as such for the task of automated driving. Future self-driving cars need to interact with their environment and need to also understand and follow the signals and instructions of those special persons. In this paper, we show how to classify special person types using Convolutional Neural Networks. The big data imbalance is handled by data augmentation using Generative Models, showing a clear advantage over classical data augmentation. The classification performance of special persons can be significantly improved using our Generative Model based Data Augmentation.
Rubbing Character Recognition with Machine Learning

Rubbings are among the oldest ancient literatures and presumably contain a lot of knowledge yet to be discovered. However, as of now, the recognition process of rubbing characters is now completely done manually. Such process, due to varying styles of characters, takes an enormous amount of effort, and can be done by a small number of specialists. Still, the aging process, which may damage characters or add noises, further increases the difficulty of recognition. In this paper, we propose a method by combining deep learning and lexical analysis for increasing the accuracy of rubbing character recognition. This work is currently in progress, and the experimentation uses rubbing character database developed by Kyoto University. In the experiment, we use a rubbing image for recognition, and the experimental results verify the effectiveness of the proposed method.
Scale-Dependent Relationships in Natural Language

Language, like other natural sequences, exhibits statistical dependencies at a wide range of scales (Lin & Tegmark, 2016). However, many statistical learning models applied to language impose a sampling scale while extracting statistical structure. For instance, Word2Vec creates vector embeddings by sampling context in a window around each word, the size of which defines a strong scale; relationships over much larger temporal scales would be invisible to the algorithm. This paper examines the family of Word2Vec embeddings generated while systematically manipulating the size of the context window. The primary result is that different linguistic relationships are preferentially encoded at different scales. Different scales emphasize different syntactic and semantic relations between words, as assessed both by analogical reasoning tasks in the Google Analogies test set and human similarity rating datasets WordSim-353 and SimLex-999. Moreover, the neighborhoods of a given word in the embeddings change considerably depending on the scale. These results suggest that sampling at any individual scale can only identify a subset of the meaningful relationships a word might have, and point toward the importance of developing scale-free models of semantic meaning.
Significant energy use analysis and energy conservation on Diponegoro University

Abstract In the current era of technology and information advancements, consumer growth and electrical energy are certainly getting more significant over time. Therefore, Energy Conservation can be carried out to find detailed information on energy usage. How much should be paid in using that energy, Biggest potential user, and ultimately this Conservation energy will show recommendation on Operational Equipment, even the processes. According to the Regulation No. 13 of 2012, concerning an infrastructure that requires large-scale energy use, conservation energy needs to be done as the will for maintaining the balance of energy using further on maintaining Eco saving energy for further generation come as well as Psychology Faculty Universitas Diponegoro from 2016 to 2019 have a specific large number of Energy consumer as education infrastructure that included from 13 Faculty and 5 unit operation. From that faculty, there is spread more inside each faculty called Unit. Overall, In this case from 13 Faculty and 5 Unit, we analyze the detailed recommendation for the Psychology Faculty of Diponegoro University as the newest building infrastructure and Non-Exact type Educational Departement such as Potential energy user of Equipment and Lightning.
Template Enhancement and Mask Generation for Siamese Tracking

Siamese tracking methods have become the focus of visual tracking in recent years. Advanced Siamese trackers perform well on certain benchmarks, but there are still some limitations. First, most Siamese trackers adopt the initial frame as a single template, which leads to underfitting and reduces the ability to predict instances. Second, mainstream trackers report a rectangular bounding box as a prediction, resulting in poor accuracy of non-rigid objects. Therefore, we propose the template enhancement and mask generation for Siamese tracking. Given that the essence of Siamese trackers is instance learning, we propose constructing an alternative template explicitly to address the underfitting of the instance space. Moreover, in order to improve the tracking accuracy, we obtain the descriptor aggregation to transform the semantic segmentation outputs for mask prediction. Finally, we propose the SiamEM through the fusion of the above approaches. Comprehensive experiments show that template enhancement and mask generation significantly improve Siamese trackers on benchmarks.
Automated Currency Recognition Using Neural Networks

Paper currency recognition (PCR) is one sort of insightful framework which is a significant need of the present computerization frameworks in the cutting edge universe of today. It has different potential applications including electronic banking, cash checking frameworks, cash trade machines, and so on. This paper proposes a programmed paper cash acknowledgment framework for paper currency. It utilizes InceptionV3 for extraction and neural network for classification and utilizes the instance of Indian paper currency as a model. The strategy is very sensible regarding exactness. The framework manages 3100 pictures. The pictures are dispersed in six classifications—10, 20, 50, 100, 200, and 500, and they are being utilized for examination and characterization. The proposed calculation is completely programmed and requires no human intercession. To approve the adequacy of system and appropriateness of neural network for cash picture arrangement, examinations have been finished with different classifiers like K-nearest neighbors (KNN) and support vector machine (SVM). The proposed procedure delivers very palatable outcomes as far as acknowledgment and effectiveness as it has accomplished an average accuracy of 97.2% over six categories.
WFaceNets

Although the algorithm based on deep convolutional neural network for face recognition has high accuracy, it needs huge computing resources. Besides, it is difficult to run offline on mobile devices. A series of network WFaceNets is proposed to maintain the accuracy of recognition and reduce the cost of computing resources. First of all, adjusted neural network structure achieved that a model with only about 1 million parameters hardly decreasing the feature learning ability to the aligned facial image. Secondly, for higher accuracy in face validation tasks, the Squeeze-and-Block structure is introduced at the bottleneck layer. SEBlock operates on the entire input feature, enabling the perception field of convolution network to cover the entire input feature, which is a beneficial semantic information supplement for the neural network. Finally, a new neural network structure is proposed, named WLayer, which is adaptive to calculate the importance of feature channels, so that the network can capture the features of key areas of the face. The experiment proved that based on 0.5 million face images as training samples, the accuracy was 99.35%, 99.28%, 99.25% and 99.12% in the LFW data set with 1.16m, 0.96m, 0.79m and 0.62m parameters, respectively.
Research on Information Interaction Technology Between Multi-service Application Systems of Power Grid

This paper analyzes information interaction and sharing technology of power information collection system for power supply service command system and 95598 business support system. Through the analysis of information interaction characteristics and business information exchange topology among the three platforms, the business requirements of supporting the other two platforms with power information collection system are summarized, and the issues in the interaction of multi-business application systems are investigated. This paper presents an information interaction model for multi-service application system structure and designs a data interface platform based on the proposed information interaction model and improved schema. The scheme is applied in the information transmission process of low voltage distribution network repair in a region. The superiority of this scheme is verified by the comparison of related function data after the new interface design comes online. It provides a research basis for the future business development and practical promotion of smart grid.
Investigating a Streaming Analytics Framework for Data Analytics Applications in the Aircraft Cabin

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Aircraft cabin operations shift towards data-driven processes. Cabin-wide multi-system communication networks are introduced to share required data for corresponding novel data-driven applications. Examples are data-driven predictive maintenance applications to reduce the downtime of systems and increase the period of scheduled maintenance or video analytics usage to detect a strained or unruly atmosphere amongst passengers. These applications require a network to transport the associated data and resources for actual computation. Costs and weight have always been the most important factors deciding if new services are introduced within the aircraft cabin. Thus, re-using hardware with free computation capacity that is already installed in the aircraft cabin can target both aspects, weight and costs. Examples for such hardware resources could be the In-flight Entertainment (IFE) equipment being installed in every seat. By means of distributed computing these resources can be combined in order to solve a computational task. In this paper, the Apache Spark Streaming Framework is investigated as an example for a distributed computing framework with respect to its suitability for being deployed in the aircraft cabin. For this purpose, it is evaluated how Spark scales with resource-constrained computing nodes. As this type of framework is designed for a robust execution of tasks, it can also be used for non-safety, but business-critical applications. In addition, Spark is a well-established commercial off-the-shelf (COTS) software, thus, possible advantages of such software like a reduction of non-recurrent engineering costs and support for developers creating applications for the aircraft cabin are also considered.&lt;/div&gt;&lt;/div&gt;
A Study on the Synergistic Relationship between Lexical Structure and Word Frequency, Polysemy and Synonym in Modem Chinese

In this study, the concept of structural complexity was introduced to classify the level of Chinese vocabulary structure. Based on Lancaster Corpus of Mandarin Chinese, the econometric method was adopted to explore the synergistic relationship between Chinese lexical structure and word frequency, Polysemy and Synonym, and to verify the relevant theoretical assumptions and mathematical models. The results show that there is a reverse dependency relationship between Chinese lexical structure and word frequency, Polysemy and Synonym. The function model y=Ax <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-cx</sup> is more accurate than y=Ax <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-b</sup> in fitting the observed data of word frequency, while the relationship between Polysemy, Synonym and lexical structure can be respectively described by the models y=Ax <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> and y=Ax <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cx</sup> .
Video Smoke Removal from a Single Image Sequence

In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.
Introducing Quantum Key Distribution

Quantum key distribution (QKD) is arguably the most developed task of quantum cryptography, both from a theoretical and experimental point of view. In this Chapter we first present some of the security principles of QKD in Sect. 3.1. We then describe the BB84 protocol as an example of QKD protocol and compute its key rate (Sect. 3.2). Section 3.3 is bit more technical, here we define and prove the security of a generic QKD protocol in the finite-key scenario. We conclude the Chapter by providing an incomplete overview of the most recent state-of-the-art QKD experiments (Sect. 3.4). In the Appendix (Sects. 3.5 and 3.6) we report the details of some proofs of the main text.
A Thermal Deformation Test Technique Robust to Change in Room Temperature Using Multiple Periodic Heating

As the shape accuracy requirement of space-based space observation instruments becomes stricter, the measurement accuracy of thermal deformation tests needs to improve. The conventional test technique using static heating and evaluation in the equilibrium states before and after heat input is influenced by disturbances such as the change in room temperature and the thermal deformation of the jigs fixing the test article and measurement devices. The authors proposed a test technique using periodic heating and evaluation in the frequency domain. The evaluation in the frequency domain can eliminate the disturbances with a frequency sufficiently different from the heat input. The thermal deformation of multiple parts of a test article can be evaluated simultaneously by applying periodic heating with different frequencies to each part. An experiment and numerical analysis using a truss structure are conducted to evaluate the performance of the proposed test technique. From the experimental and numerical results, the elimination of the effect of room temperature change and the simultaneous evaluation of multiple parts of a test article are demonstrated. As a result, the effectiveness of the proposed thermal deformation test technique is shown.
Pengetahuan dan Sikap Anak tentang Makanan Jajanan Sehat melalui Permainan Ular Tangga

This study aimed to determine the knowledge and attitudes of healthy snack children through snake and ladder games in Lubuk Pakam sub-district. This research is a quantitative study with a quasi-experimental design of two groups of the pretest-posttest system. The results showed that by using the statistical test, the value of p = 0.000 &lt;0.05. Based on the pretest and post-test results on 54 students, the average change in knowledge before the intervention was 12.8, and the closest intervention was 15.5. The shift in attitude before the intervention was 10.2, and after the intervention was 12.8. In conclusion, there is the influence of snake and ladder game counseling on healthy snack foods on elementary school children's knowledge and attitudes.&#x0D; &#x0D; Keywords: Healthy Snacks, Knowledge, Attitudes, Snakes, and ladders
Korean Neural Machine Translation Using Hierarchical Word Structure

Korean neural machine translation may significantly suffer from low-resource issues. We thus propose an enhancement method that fully exploits the hierarchical Korean word embedding structure from source representation. To our best knowledge, this is the first attempt for such Korean NMT tasks. Every Korean word can be decomposed into characterand jamo-level (sub-character) units. Therefore, We merge the character- and jamo-level representations with word embeddings to capture important combining word meaning. And then the merged representations are fed into NMT model. Our simple and novel method achieves BLEU improvements (up to 0.6) compared to word-based NMT baselines on Korean-to-Chinese and Koreanto-English translation tasks.