Reviewerly/exHarmony huggingface dataset:
Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation

Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes. In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden. We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses. With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on the larger WMT 2014 machine translation dataset, our MoSboosted Transformer yields 29.6 BLEU score for English-toGerman and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.9 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task.
A course scheduling algorithm based on improved genetic algorithm with multi-objective constrains

In order to meet the humanized requirements of course scheduling in colleges and universities, a multi-objective optimization course scheduling model with soft constraints is established. According to the characteristics of many constraints and large search space of course scheduling model, the unique coding method and optimized initial population generation operator are designed, and genetic operators such as crossover and mutation are also designed for the problem. The experimental results demonstrate that the proposed algorithm can obtain the high quality course schedule according to the proposed soft constraints, and it can avoid typical genetic algorithm to fall into local optimum problem.
Introduction to ScaDL 2019

Scope of the WorkshopRecently, Deep Learning (DL) has received tremendous attention in the research community because of the impressive results obtained for a large number of machine learning problems.The success of state-ofthe-art deep learning systems relies on training deep neural networks over a massive amount of training data, which typically requires a large-scale distributed computing infrastructure to run.In order to run these jobs in a scalable and efficient manner, on cloud infrastructure or dedicated HPC systems, several interesting research topics have emerged which are specific to DL.The sheer size and complexity of deep learning models when trained over a large amount of data makes them harder to converge in a reasonable amount of time.It demands advancement along multiple research directions such as, model/data parallelism, model/data compression, distributed optimization algorithms for DL convergence, synchronization strategies, efficient communication and specific hardware acceleration.This intersection of distributed/parallel computing and deep learning is becoming critical and demands specific attention to address the above topics which some of the broader forums may not be able to provide.The aim of this workshop is to foster collaboration among researchers from distributed/parallel computing and deep learning communities to share the relevant topics as well as results of the current approaches lying at the intersection of these areas.
A New Adaptive Curve Model Tracking Algorithm and Its Application

A new adaptive filtering method based on curve model is proposed to solve the problem of large positioning error caused by the mismatch between maneuvering target modeling and actual target motion. The method in the paper abandons the traditional method which estimates the turn rate according to the direction angle and calculates the tangential acceleration based on the interpolation acceleration. The paper takes the turn rate and the target acceleration both as the state variables, expanding the state vector, which improves the estimation accuracy of tangential acceleration, and alleviates the large computational burden caused by the traditional two-layer filter structure. The expression of process noise covariance after state vector expansion is derived. In addition, the design of direction angle is optimized based on the arc tangent library function atan2() in the embedded software, combined with the transfer relationship of direction angle between four quadrants. Through simulation, it is proved that the proposed method is more adaptable to maneuvering target tracking and has higher positioning accuracy.
Learning Multiple Maps from Conditional Ordinal Triplets

Ordinal embedding seeks a low-dimensional representation of objects based on relative comparisons of their similarities. This low-dimensional representation lends itself to visualization on a Euclidean map. Classical assumptions admit only one valid aspect of similarity. However, there are increasing scenarios involving ordinal comparisons that inherently reflect multiple aspects of similarity, which would be better represented by multiple maps. We formulate this problem as conditional ordinal embedding, which learns a distinct low-dimensional representation conditioned on each aspect, yet allows collaboration across aspects via a shared representation. Our geometric approach is novel in its use of a shared spherical representation and multiple aspect-specific projection maps on tangent hyperplanes. Experiments on public datasets showcase the utility of collaborative learning over baselines that learn multiple maps independently.
LibGuides: Database Help: About Databases & Search Tips

The Cleary U. Library facilitates access to almost 30 (mostly academic) databases. Which one should you use? How can you get the most out of using the databases? This guide will help you answer those questions!
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PERUMAHAN DI BANDAR LAMPUNG

Houses are one of the basic human needs besides clothing and food. There are many factors that influence everyone to choose a house to live in, including a house model, house prices, land area, building area, distance to location, environment and so on. In Bandar Lampung, there are many housing estates. But the absence of a system that can be used by the community as prospective buyers to choose housing to be purchased in accordance with the desired criteria. This has resulted in consumers, especially buyers of houses or housing, still finding it difficult to find housing according to their desired and financial needs. In this study a housing decision support system was applied. And the results of decision support systems can help decision makers, namely buyers in choosing housing that fits the desired criteria and according to needs and finances.
When Digital Manuscripts Burn: Information Security and Digital Heritage in Eastern Europe

In Eastern Europe, the growing digitalization of memory and heritage goes hand in hand with instrumentalization of the past. Memory wars raging within and between post-socialist states are increasingly projected into digital spaces, where different historical narratives compete for users’ attention. Under these conditions, both conventional and non-conventional actors increasingly weaponize digital heritage to mobilize supporters and stigmatize opponents. To do so, these actors selectively use contents of online archives and museums to promote or undermine specific interpretations of the past. Yet, as digital heritage turns into another weapon in the ongoing political struggles, it also becomes a possible target of adversarial attacks used to manipulate or destroy it. In our paper, we discuss the ongoing digitalization of digital heritage in Eastern Europe from the information security point of view. For this purpose, we critically review existing literature on the information security in the domain of digital heritage and couple it with the discussion of known instances of adversarial attacks against online museums and collections in the Eastern European context. Using the CIA model (i.e. confidentiality, integrity and availability), we classify most common types of attacks against digital heritage and discuss the possible short- and long-term implications of these attacks for collective memory practices in the region. We conclude by proposing several strategies for countering adversarial attacks and safeguarding digital heritage which take into account specific technical and political aspect of digital remembrance in Eastern European countries.
[PDF] NCERT Solutions for Class 4 English Unit-2 The little fir tree

NCERT Solutions for Class 4 English Unit-2 The little fir tree, are part of  NCERT Solutions for Class 4 English. Here we have given NCERT Solutions for ...
Teaching Machines to Extract Main Content for Machine Reading Comprehension

Machine reading comprehension, whose goal is to find answers from the candidate passages for a given question, has attracted a lot of research efforts in recent years. One of the key challenge in machine reading comprehension is how to identify the main content from a large, redundant, and overlapping set of candidate sentences. In this paper we propose to tackle the challenge with Markov Decision Process in which the main content identification is formalized as sequential decision making and each action corresponds to selecting a sentence. Policy gradient is used to learn the model parameters. Experimental results based on MSMARCO showed that the proposed model, called MC-MDP, can select high quality main contents and significantly improved the performances of answer span prediction.
Marco analítico para el aprovechamiento de Tecnologías de Información y Comunicación. / Analytical Framework for the Use of Information and Communication Technologies.: Diagnóstico en organizaciones gubernamentales y de la sociedad civil en México / Diagnosis in Government and Civil Society Organizations in Mexico

The objective of this paper is to propose an analytical framework for the use of ICT in governmental and civil society organizations for social development, which was integrated through the review of previous studies and surveys. Data from an online survey applied to 203 mexican organizations focused on the development of young people were analyzed based on this framework. The findings show the level of use of ICT in relation to three levels of use (basic, intermediate and high) and 11 dimensions. It is found that the use of ICTs is mainly for administrative and dissemination purposes and to a lesser extent to enhance their capacities to respond to the social problems they address. This study generates knowledge about these organizations which are key actors for development and social change. Addtionally, this proposal is replicable in other organizations and contexts.
GRO AND WeGO - ALGORITHMIC APPROACHES TO INTEGRATE THE HETROGENOUS DATABASES AND ENHANCE THE EVALUATION OF ONTOLOGY MAPPING SYSTEMS IN THE SEMANTIC WEB

In the present day world, where information driven economy and information enhanced living standards rule everything, the sources of data from which the information is derived, are highly heterogeneous. The heterogeneity necessitates a mechanism for integrating data, before it is presented to the user. The internet and WWW are forming the backbone of information. Semantic web is an initiative in achieving the goal of 'machine processed information' being available to us than requiring human intelligence for processing information. This work is carried out to address the heterogeneity problem that exists among data sources and provides a solution through the application of ontology. Ontology is a conceptual tool for handling semantic heterogeneity. The algorithmic approach is adopted in the mapping solution system. The elements of ontology are compared and similarity analysis is carried out to arrive at the degree of matching of individual nodes as well as the ontology in totality for an ontology alignment.
Data Collaboratives: Trusted Data Intermediary Business Models

To address complex societal issues, cross-sector partnerships are needed that specifically aim to create value to address such challenges. Data collaboratives are initiatives that encourage and promote such partnerships, by the collection, sharing, or processing of data. Data collaboratives are faced with barriers that complicate collaboration between data contributors and data users. As a result, the potential of public value creation is not being reached. To overcome these barriers, decision-makers in data collaboratives need a better understanding of the Trusted Data Intermediary as a coordination mechanism. Trusted Data Intermediaries are entities entrusted with enabling data transactions between data contributors and data users across sectors, thereby generating value for collaborators, and enabling the creation of public value. Currently, academic knowledge on how these intermediary entities is absent. By a qualitative, exploratory multiple-case study, descriptive business models of six cases are developed and analyzed. From the analysis, it was found that Trusted Data Intermediaries implement different business models, depending on their characteristics in specialization to specific segments of data contributors and users. Further, the implementation may depend on the profit motive and chosen centrality of data storage. Based on these dependencies, two Trusted Data Intermediaries' business model archetypes are developed: the Generic archetype and the Specialized archetype. In addition, variations to these archetypes are discussed. As the two archetypes offer an initial theory on Trusted Data Intermediaries, next steps may include the testing of the archetypes on more cases, as well as extending the theory for Trusted Data Intermediaries with other characteristics.
Version Capability for BGP

In this document, we introduce a new BGP capability that allows theadvertisement of a BGP speaker's version.
Proposal for the Reengineering of Processes in the Management of Information and Communication Technology

This article describes the strategies implemented in the improvement of the processes in the management of information and communication technology (ICT) on the “Universidad Nacional de Loja” carried out by the “Unidad de Telecomunicaciones e Información” (UTI). First, the analysis of the processes carried out in the management of ICT is narrated. Subsequently, the experiences and best practices of information technology management implemented are described, considering the processes recommended by the Technology Infrastructure Library of Information (ITIL v3). Finally, the results obtained from the process of reengineering in ICT management carried out by the UTI are shown, using free software and open source tools.
An Improved Hierarchical Datastructure for Nearest Neighbor Search

Nearest neighbor search is a fundamental computational tool and has wide applications. In past decades, many datastructures have been developed to speed up this operation. In this paper, we propose a novel hierarchical datastructure for nearest neighbor search in moderately high dimension. Our proposed method maintains good run time guarantees, and it outperforms several state-of-the-art methods in practice.
Dynamic Quantum Proxy Blind Signature Scheme with Cluster State

As the quantum proxy blind signature provides the anonymity protection for message owner, it can be applied to the fields of E-business, E-government and etc. However, the existing quantum proxy blind signature schemes have not considered the issue of the dynamic property of the proxy signers. In this paper, we propose a dynamic quantum proxy blind signature scheme with cluster states using the measurement property of Greenberger-Horne-Zeilinger state and the controlled-NOT gate. The scheme can remove any authorized proxy signers from the proxy group and add new proxy signers into the proxy group. The correlation of cluster states is served as the verification criteria to guarantee the security of the scheme.
Particle Swarm Loss for Lightweight Object Detection

Currently in object detection, deep learning based detectors are gaining their momentum. However, the supervision involved in the widely-used anchor paradigm within the detection pipeline is inadequate. Traditional object detectors opt for densely picking anchors to increase the training samples for faster convergence and better detection quality. However, dense anchor scheme requires extra computational budget which renders it infeasible for lightweight detectors. To address the problem, inspired by the cognitive consistency, we propose a novel Particle Swarm Loss for lightweight object detection. Experiments upon the MS-COCO challenge show that detectors compensated by PS loss can not only converge faster but also acquire better detection quality than their vanilla versions (YOLOv3 and SSD improves 2.0% and 2.5% on the harsh AP50 respectively) without extra computational overhead. In addition, we propose a dapper backbone with high cost-efficiency for the resource-limited scenarios.
In Memory of R. Sh. Liptser (20.03.1936 -- 02.01.2019)

An obituary of Robert Shevilevich Liptser, who passed away on January 2, 2019. He was a prominent scientist and author in the field of random processes and their applications. His books are widely known and have been serving to educate specialists in the field for generations.
Retrieving Curated Stack Overflow Posts from Project Task Similarities (S)

Software development depends on diverse technologies and methods and, as a result, software development teams often need to handle issues in which team members are not experts.To address this lack of expertise, developers typically rely on information obtained from web-based Q&A sites such as Stack Overflow, a popular platform to find solutions to specific technology-related problems.However, access to these Q&A websites is currently not explicitly integrated with software development projects.Therefore, software developers often need to search for solutions to similar and recurring issues multiple times.This lack of integration not only hinders the reuse of the knowledge obtained but also compels developers to perform repeated searches for recurring problems.In this paper, we investigate an approach that explicitly associates project tasks with Stack Overflow posts that have already been curated by developers, and use project task similarities to investigate the possibility to suggest curated Stack Overflow posts.Precision and accuracy were 71.60% and 77.78%, respectively.We also found indications that project task elements such as the process activity, influence accuracy and precision if attempting to reuse curated Stack Overflow posts.
Deep-learning based precoding techniques for next-generation video compression

Several research groups worldwide are currently investigating how deep learning may advance the state-of-the-art in image and video coding. An open question is how to make deep neural networks work in conjunction with existing (and upcoming) video codecs, such as MPEG AVC/H.264, HEVC, VVC, Google VP9 and AOMedia AV1, as well as existing container and transport formats. Such compatibility is a crucial aspect, as the video content industry and hardware manufacturers are expected to remain committed to supporting these standards for the foreseeable future. We propose deep neural networks as precoding components for current and future codec ecosystems. In our current deployments for DASH/HLS adaptive streaming, this comprises downscaling neural networks. Precoding via deep learning allows for full compatibility to current and future codec and transport standards while providing for significant savings. Our results with HD content show that 23%-43% rate reduction takes place under a range of state-of-the-art video codec implementations. The use of precoding can also lead to significant encoding complexity reduction, which is essential for the cloud deployment of complex encoders like AV1 and MPEG VVC. Therefore, beyond bitrate saving, deep-learning based precoding may reduce the required cloud resources for video transcoding and make cloud-based solutions competitive or superior to state-of-the-art captive deployments.
[PDF] NCERT Solutions for Class 4 English Unit-5 Poem Pont be afraid of the dark

NCERT Solutions for Class 4 English Unit-5 Poem Pont be afraid of the dark, are part of NCERT Solutions for Class 4 English. Here we have given NCERT ...
Computational Intelligence through Human Machine Interaction in Cyber World

Recent studies have focused on single occupant and coarse-grained activity recognition (AR), while fine-grained multi-occupancy AR has received little attention. This presents a semantic-enabled approach to fine-grained multi-occupancy AR and estimating AR confidence level (ARCL). The approach leverages ontology modeling techniques for generic and personalized activities of daily living (ADL) descriptions, incremental semantic reasoning, and belief-based importance values for estimating confidence level. To calculate ARCL, the segmented sensor observations and candidate activity class are analyzed at coarse- and fine-grained levels. The importance values are specified for each key action and context for a given ADL. ARCL is calculated twofold: coarse-grained confidence level (CCL) and fine-grained confidence level (FCL). The CCL algorithm extracts and takes location, key objects, and time interval into consideration, whereas FCL inspects the occupant’s interactions with objects to detect fine-grained actions using predefined thresholds. In addition, the multi-occupancy AR (MAR) approach is proposed to detect, identify, and associate an occupant’s actions with sensor observations. MAR leverages time window analysis and a location-based approach to detect multi-occupancy activities and fingerprint sensors to identify and associate object interactions. A fusion of ambient sensors and dense sensors for a non-invasive and non-obstructive data collection approach is proposed and applied to a kitchen application scenario to illustrate its use.
Research on Interval Constraint Range Domain Algorithm for 3D Reconstruction Algorithm Based on Binocular Stereo Vision

For 3D reconstruction algorithm based on binocular stereo vision only approximately restores the 3D shape of the object, but it does not analyze the performance of using 3D reconstruction algorithm based on the model of binocular stereo vision measuring distance device to restore object, an interval constraint range domain algorithm is proposed in this paper. In this paper, this algorithm finds the boundary points of the object, the performance of using 3D reconstruction algorithm to restore object depends on the proportion of the number of points that fall within the constraint interval to the total number of points. The interval constraint range domain algorithm analyzes the performance of the 3D reconstruction algorithm to recover the object, that is, analyzes the measurement accuracy of the algorithm. Under the condition of the resolution of the image sensor used in the algorithm is identical, the simulation results show that the accuracy of the algorithm is determined by the spacing of the image sensor, the position, and shape of the constraint interval.
[PDF] NCERT Solutions for Class 4 English Unit-7 Poem Hiawatha

NCERT Solutions for Class 4 English Unit-7 Poem Hiawatha, are part of  NCERT Solutions for Class 4 English. Here we have given NCERT Solutions for Class ...