Reviewerly/exHarmony huggingface dataset:
International Law and its Discontents: Technology Laws

Technology enables trans-jurisdictional activity, both legal and illegal, by people, organisations, and governments. Technology is advancing much faster than the international laws that must cope with such progression.  Blockchain technology is examined as an opportunity for the law to catch up with technological advancements rather than fall further behind them.  Governments and corporations have made several disconnected attempts to harness the unique properties of blockchain technology to promote electronic voting and asset registration. The benefits of incorporating blockchain technologies may have the potential to alleviate some of international law’s current discontents.
Protecting the quantum

Quantum PhysicsRobust, fast, and with the capability of traveling vast distances, single photons are ideal carriers of quantum information. However, for on-chip applications, the photons inevitably interact with the surrounding medium and can be lost, which is perhaps even more true for delicate entangled photons where the nonclassical correlations between the photons can fizzle out. Recent developments have shown that topology can provide protection for photons and entangled photons against dissipation and disorder. Wang et al. demonstrate an integrated photonic chip approach in which the quantum correlations between two-photon states is given topological protection. A specially designed and fabricated photonic crystal provides topological boundary states within which the quantum correlations between entangled photons are maintained.Optica 6 , 955 (2019).
Depth from stacked light field images using generative adversarial network

The estimated depth map provides valuable information in many computer vision applications such as autonomous driving, semantic segmentation and 3D object reconstruction. Since the light field camera capture both the spatial and angular light ray, we can estimate a depth map throughout that properties of light field image. However, estimating a depth map from the light field image has a limitation in term of short baseline and low resolution issues. Even though many approach have been developed, but they still have a clear flaw in computation cost and depth value accuracy. In this paper, we propose a network-based and epipolar plane image (EPI) light field depth estimation technique. Since the light field image consists of many sub-aperture images in a 2D spatial plane, we can stack the sub-aperture images in different directions to handle occlusion problem. However, usually used many light field subaperture images are not enough to construct huge datasets. To increase the number of sub-aperture images for stacking, we train the network with augmented light field datasets. In order to illustrate the effectiveness of our approach, we perform the extensive experimental evaluation through the synthetic and real light field scene. The experimental result outperforms the other depth estimation techniques.
Learning definable hypotheses on trees

We study the problem of learning properties of nodes in tree structures. Those properties are specified by logical formulas, such as formulas from first-order or monadic second-order logic. We think of the tree as a database encoding a large dataset and therefore aim for learning algorithms which depend at most sublinearly on the size of the tree. We present a learning algorithm for quantifier-free formulas where the running time only depends polynomially on the number of training examples, but not on the size of the background structure. By a previous result on strings we know that for general first-order or monadic second-order (MSO) formulas a sublinear running time cannot be achieved. However, we show that by building an index on the tree in a linear time preprocessing phase, we can achieve a learning algorithm for MSO formulas with a logarithmic learning phase.
The Impact of Social Media on Improving the Services of the Department of Lands and Survey in Jordan

The present study aimed to identify the impact of social media on improving the services of the Department of Lands and Survey in Jordan. To meet the study&amp;rsquo;s goals, a forty-item questionnaire was developed. This questionnaire covers four areas. The questionnaire forms were distributed to 737 service recipients of the Department of Lands and Survey in Jordan. It was found that social media has a significant impact on improving the services of the Department of Lands and Survey in Jordan. It was found that Facebook, WhatsApp, YouTube, and Twitter have the greatest impact respectively on improving the services of the Department of Lands and Survey in Jordan. &amp;nbsp;It was found that there isn&amp;rsquo;t any statistically significant difference &amp;ndash;at the statistical significance level of (a=0.05)- between the respondents&amp;rsquo; attitudes in this regard which can be attributed to (gender or academic qualification). It was found that there is a statistically significant difference &amp;ndash;at the statistical significance level of (a=0.05)- between the respondents&amp;rsquo; attitudes in this regard which can be attributed to (nationality). The latter difference is for the favor of Jordanians. &amp;nbsp;Based on the afore mentioned results, the researcher recommends:&#x0D; &#x0D; &#x0D; Distributing pamphlets to the service recipients of the Department of Lands and Survey in Jordan. Such pamphlets must encourage them to follow the official page of the latter department on Twitter&#x0D; Responding faster to the inquiries of the service recipients of the Department of Lands and Survey that are sent through WhatsApp.&#x0D;
Handling a Large Team in a Team Project

Agile principles and modern iterative software development practices suggest that a software development team should consist of six to nine members. However, because of the complexity or size of a software project or the demanding client deadlines, you might have to accommodate more team members to meet the required targets. In this sort of a scenario, what Agile practices suggest is to divide the software project into modules, which can be handled by ideally sized Agile teams of six to nine people. This means multiple teams work together to achieve the project goal.
Modeling and Reconstructing Complex Heterogeneous Materials From Lower-Order Spatial Correlation Functions Encoding Topological and Interface Statistics

The versatile physical properties of heterogeneous materials are intimately related to their complex microstructures, which can be statistically characterized and modelled using various spatial correlation functions containing key structural features of the material's phases. An important related problem is to inversely reconstruct the material microstructure from limited morphological information contained in the correlation functions. Here, we present in details a generalized lattice-point (GLP) method based on the lattice-gas model of heterogeneous materials that efficiently computes a specific correlation function by updating the corresponding function associated with a slightly different microstructure. This allows one to incorporate the widest class of lower-order correlation functions utilized to date, including those encoding topological connectedness information and interface statistics, into the Yeong-Torquato stochastic reconstruction procedure, and thus enables one to obtain much more accurate renditions of virtual material microstructure, to determine the information content of various correlation functions and to select the most sensitive microstructural descriptors for the material of interest. The utility of our GLP method is illustrated by modelling and reconstructing a wide spectrum of random heterogeneous materials, including "clustered" RSA disks, a metal-ceramic composite, a two-dimensional slice of a Fontainebleau sandstone and a binary laser-speckle pattern, among other examples.
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains

Data dependent regularization is known to benefit a wide variety of problems in machine learning. Often, these regularizers cannot be easily decomposed into a sum over a finite number of terms, e.g., a sum over individual example-wise terms. The $F_\beta$ measure, Area under the ROC curve (AUCROC) and Precision at a fixed recall (P@R) are some prominent examples that are used in many applications. We find that for most medium to large sized datasets, scalability issues severely limit our ability in leveraging the benefits of such regularizers. Importantly, the key technical impediment despite some recent progress is that, such objectives remain difficult to optimize via backpropapagation procedures. While an efficient general-purpose strategy for this problem still remains elusive, in this paper, we show that for many data-dependent nondecomposable regularizers that are relevant in applications, sizable gains in efficiency are possible with minimal code-level changes; in other words, no specialized tools or numerical schemes are needed. Our procedure involves a reparameterization followed by a partial dualization -- this leads to a formulation that has provably cheap projection operators. We present a detailed analysis of runtime and convergence properties of our algorithm. On the experimental side, we show that a direct use of our scheme significantly improves the state of the art IOU measures reported for MSCOCO Stuff segmentation dataset.
THE DESIGN OF SCHOOL UNIFORM IN THE CONTEXT OF TRANSFORMATIVE SHAPING

To study and to define the main design components and principles of design of school uniform, to determine the methods of transformation used in the development of such range of products. To study the optimal composition of school uniform for boys and girls of the junior school group, to identify the main elements of transformation of school uniform and the ways to use them.
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods

Decision-tree-based ensemble classification methods (DTEMs) are a prevalent tool for supervised anomaly detection. However, due to the continued growth of datasets, DTEMs result in increasing drawbacks such as growing memory footprints, longer training times, and slower classification latencies at lower throughput. In this paper, we present, design, and evaluate RADE - a DTEM-based anomaly detection framework that augments standard DTEM classifiers and alleviates these drawbacks by relying on two observations: (1) we find that a small (coarse-grained) DTEM model is sufficient to classify the majority of the classification queries correctly, such that a classification is valid only if its corresponding confidence level is greater than or equal to a predetermined classification confidence threshold; (2) we find that in these fewer harder cases where our coarse-grained DTEM model results in insufficient confidence in its classification, we can improve it by forwarding the classification query to one of expert DTEM (fine-grained) models, which is explicitly trained for that particular case. We implement RADE in Python based on scikit-learn and evaluate it over different DTEM methods: RF, XGBoost, AdaBoost, GBDT and LightGBM, and over three publicly available datasets. Our evaluation over both a strong AWS EC2 instance and a Raspberry Pi 3 device indicates that RADE offers competitive and often superior anomaly detection capabilities as compared to standard DTEM methods, while significantly improving memory footprint (by up to 5.46x), training-time (by up to 17.2x), and classification latency (by up to 31.2x).
Cooperative transport system using unmanned aerial vehicles by cable connection

Activities of small Unmanned Aerial Vehicles (UAVs) have attracted attention in various fields in recent years. Especially, transportation of luggage using multi-rotor helicopter has already been the phase of verification test. However, when carrying out such transportation, the weight of the package that can be carried is limited due to the payload limitation of UAVs. Cooperative transport system using kinematics used in cable driven robot (Cable-Driven Parallel Robot: CDPR) has been proposed but it is not considered how to arrange the UAV.In this paper, using dynamics based on CDPR, cooperative transport system considering the UAV layout will be described.However, depending on the direction of the cables to which the UAV and loads are joined,the yaw angle of the package can not be controlled. In this paper, we describe the simulation considering the control of the yaw angle and its result.
The Importance of Evaluating Initial Kerogen Potential and Restoring Kinetic Schemes from Mature Samples, a Case Study from Kuwait

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Software Platform for Analise of Data in The Field of Waste Management

Integrative Journal of Conference Proceedings Software Platform for Analise of Data in The Field of Waste Management Epifanсev K* Department of Aerospace Instrumentation, Russia *Corresponding author:Hiroshi Bando, Medical Research, Nakashowa, Tokushima, Japan. Submission: July 07, 2019;Published: September 06, 2019 Volume2 Issue1September, 2019
Annotated Guidelines and Building Reference Corpus for Myanmar-English Word Alignment

Reference corpus for word alignment is an important resource for developing and evaluating word alignment methods. For Myanmar-English language pairs, there is no reference corpus to evaluate the word alignment tasks. Therefore, we created the guidelines for Myanmar-English word alignment annotation between two languages over contrastive learning and built the Myanmar-English reference corpus consisting of verified alignments from Myanmar ALT of the Asian Language Treebank (ALT). This reference corpus contains confident labels sure (S) and possible (P) for word alignments which are used to test for the purpose of evaluation of the word alignments tasks. We discuss the most linking ambiguities to define consistent and systematic instructions to align manual words. We evaluated the results of annotators agreement using our reference corpus in terms of alignment error rate (AER) in word alignment tasks and discuss the words relationships in terms of BLEU scores.
Tendencias educativas para enfrentar la cuarta revolución industrial

espanolEl titulo universitario tal como lo conocemos esta en pleno proceso de transformacion y, con ello, la educacion superior. Hoy estamos ante el umbral de una revolucion tecnologica sin precedentes que cambiara la forma en que vivimos, aprendemos, trabajamos y nos relacionamos con otros. La Cuarta Revolucion Industrial hara que la Universidad sea mas importante que nunca; sin embargo, para lograrlo, la Universidad tendra que regresar a su esencia. EnglishThe university title like we know it is on a process of transformation and with that, also superior education. Today we are at the dawn of a technologic revolution without any precedent that is going to change our way of living, learning, working and relating with others. The Fourth Industrial Revolution will make University more important than ever, nonetheless, to achieve it, University must return to its essence.
What is the impact of raw MT on Japanese users of Word: preliminary results of a usability study using eye-tracking

This paper presents preliminary results of a study of Japanese native speakers working with the Microsoft Word application in two modalities: the released Japanese version and a machine translated (MT) version (the raw MT strings incorporated into the MS Word interface). To explore the effect of translation modality on task completion, time and satisfaction, an experiment using an eye-tracker was set up with a group of 42 users: 22 native Japanese and 20 native English speakers. The results suggest that Japanese-native speakers have higher completion scores and are more efficient when working with the released versions of the product than with the MT version, but these differences are not significant. Their self-reported satisfaction, however, is significantly higher when working with the released product as opposed to the raw MT version.
Info Guides: Musical Instruments Resource Guide: Mandolin

Learn to play a variety of musical instruments using the Sun Life Musical Instrument Lending Library collection.
Arabic Query Reformulation using Harmony Search Algorithm

The search on the web helps users to find the most relevant information. However, this is not always verified because many users search with ambiguous queries to find information. The ambiguous queries can return irrelevant results. Query reformulation aims to get more relevant information to query despite the ambiguity in the query. It works to disambiguate the meaning of a word in a query and finds its alternative. This is a big challenge in Arabic queries. In addition, there is another challenge which is the delay in the online word preprocessing. The reducing of delay should be considered along with the increasing of the accuracy. This work takes into account these two challenges by using a powerful met heuristics search algorithm that is called harmony search. The algorithm is used to reformulate a query with high accuracy and low delay. The proposed system that uses this algorithm is tested on a set of ambiguous Arabic queries and two different Arabic corpora called ZAD and NLEL. The system is able to get more relevant documents to a user query. Experimental results show significant superiority of the proposed query reformulation system in terms of response time, precision, recall, and mean average precision. The proposed system outperforms the baseline system in a rate up to (33.22 %) and reduces the latency in an approximate rate up to (0.064) second for each query.
The Suitability of Body Scanning Measurement in Pattern Drafting Methods

There has been much recognition that body scanning can provide more data on the human body than traditional measurements alone.Nevertheless, it is not always possible to extract the many measurements that are required by existing methods of pattern construction, due to the differences in the measurements captured between manual and body scanning methods.The conventional methods that are used for drafting pattern blocks do not incorporate data pertaining to body measurements to a large extent.This can be traced back to the fact that traditional pattern drafting approaches are from a time when obtaining some measurements were difficult and certain measurements were easier to extract than others.To overcome the lack of data, post-drafting modifications are performed to accomplish an appropriate fit, and most pattern books are accompanied with detailed guidance as to how to adjust the blocks to take into consideration typical figure disparities.Body scanning technology makes it possible to acquire body configuration data that has been traditionally challenging to access.This type of technology can be employed to investigate body shapes and collate pertinent measurements.It can also be employed to delineate dimensions, something that was not previously possible.Moreover, appropriate scan data allows a challenge to existing drafting methods and the proposal of new ways of creating patterns that is based on actual measurements rather than proportional relationships.This study commences by analysing existing 2D pattern construction methods and the myriad outputs of body scanning technology to examine the extent to which body scanning can complement conventional pattern drafting approaches.Ten pattern-making techniques for bodices and trousers were assessed, and the measurements that were needed for these techniques were compared to the measurements that were generated by a body scanning system.The research established how well the measurements required for different drafting methods can be produced from 3D body scanning technology.The main contribution of this study is to highlight where measurements that are required for pattern construction be defined as outputs within body scanner systems.This would allow the body scanner to offer more suitable measurement support for pattern drafting methods.
PubChain: A Decentralized Open-Access Publication Platform with Participants Incentivized by Blockchain Technology

We design and implement Publication Chain (PubChain), a decentralized open-access publication platform built on decentralized and distributed technologies of blockchain and IPFS peer-to-peer file sharing systems. The existing publication platforms have some severe drawbacks. First, instead of promoting widespread knowledge sharing, access to publications on the platforms owned by publishers is often on a fee basis. This drawback of pay wall prevents researchers from "standing on the shoulders of giants". Moreover, the peer review process on most all existing publication platforms (including both open-access and publisher platforms) is prone to be ineffective, since there is no proper incentive to reviewers for performing high-qualified reviews. PubChain is an alternative platform to the existing publication venues aiming to address their drawbacks. No central third-party owns the contents (i.e., papers and reviews) of PubChain. Exploiting blockchain technology, we devise an elaborate incentive scheme on PubChain to incentivize key stakeholders (i.e., authors, readers and reviewers) to participate publication activities on PubChain in a substantive manner by earning credits and rewards through self-motivated interactions. We have performed simulations to investigate the robustness of our proposed incentive scheme against fraudulent publications and reviews. We also have implemented a prototype of PubChain to demonstrate its key concepts.
PENERAPAN METODE PICTURE AND PICTURE DALAM PEMBELAJARAN CERITA FANTASI

This research is motivated by the low value in writing fantasy stories on Madrasah Tsanawiyah students, this is proven by the results of observations with Indonesian teachers who say that students are less interested in learning to write fantasy stories because they are considered boring, so students lack learning and get grades less. Based on this, the formulation of the problem of this research is how the results before and after learning write fantasy stories using the method of picture and picture in class VII students. The purpose of this research is to find out the results before and after using the picture and picture method. The method used is an experimental one-group pretest-posttest design. The results showed that there were good and increasing differences between before and after using the picture and picture method. Based on the data obtained there are known differences from the test before using the method and the final test with the initial test average of 64.02 and the final test of 80.08 with a difference of 16.06. Judging from the increased student results it can be concluded that the use of the picture and picture method is suitable for use in learning to write fantasy stories.
Case studies on the flipped classroom with a MOOC in college contexts

This study investigated the effects of applying the flipped classroom approach with MOOC to the college educational system. A total of six undergraduate students participated in the 10-week innovative learning setting. The participants performed online activities using the learning content from a MOOC website; this was followed by a participatory learning process in the offline classroom. The semi-structured face-to-face interviews for the six participants after the classes were completed and analyzed. The results showed that the instructional method enabled students to be highly motivated and to perform learning activities. However, there were some limitations: (1) learning was impeded due to English language issues and (2) the Korean education culture was still unfamiliar with this pedagogical method. Finally, suggestions for future research are discussed.