Reviewerly/exHarmony huggingface dataset:
Controlling Sequence-to-Sequence Models - A Demonstration on Neural-based Acrostic Generator

An acrostic is a form of writing for which the first token of each line (or other recurring features in the text) forms a meaningful sequence.In this paper we present a generalized acrostic generation system that can hide certain message in a flexible pattern specified by the users.Different from previous works that focus on rule-based solutions, here we adopt a neural-based sequence-to-sequence model to achieve this goal.Besides acrostic, users can also specify the rhyme and length of the output sequences.To the best of our knowledge, this is the first neural-based natural language generation system that demonstrates the capability of performing micro-level control over output sentences.
Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Document searching using queries that can understand the context can affect the intent and purpose of the user's desire when searching documents.Many studies have been conducted on understanding the context of the query, but differences in terms of language can lead to different methods of context understanding; therefore, methods implemented in the previous studies need to be improved.In this paper, we proposed a query expansion method based on BabelNet search and Word Embedding (BabelNet Embedding).Query expansion method focuses on developing queries based on semantic relationships on queries to understand the context of the query.Candidate queries were developed by finding synonyms, measuring similarity using WordNet, Word Embedding on all articles of Wikipedia, and BableNet Embedding on articles Wikipedia Online.We compared our proposed method with the existing semantic query expansion.Our result provided better result in retrieving relevant document with accuracy of 89% in searching Arabic documents.
An Acceleration Method for Docker Image Update

Docker encapsulates an application and its supporting software, system library files and other data such as configurations into the image. The updated version of application is encapsulated into a newer image and released by distributing the new image. The image distribution process will spend lots of time that can delay the container start process from milliseconds to seconds or minutes. However, a large amount of data duplication exists among different image versions. Currently, the Docker system adopts the inheritance and hierarchical image loading mechanism to reuse underlying file layers. Nevertheless, there is no reliable reuse mechanism for duplicated data within the application. We developed a system that applies the deduplication method into image distribution to enhance the efficiency by reusing the same data existing in the historical versions. As this method is resource sensitive, it might slowdown the image distribution process under certain situations. So, we propose a decision tree based strategy making method to find out which is the better distribution method between our solution and the default solution. Experimental results showed that our method will increase the speed of docker image update by 7X in our environment.
Managing confidentiality leaks through private algorithms on Software Guard eXtensions (SGX) enclaves

Many applications are built upon private algorithms, and executing them in untrusted, remote environments poses confidentiality issues. To some extent, these problems can be addressed by ensuring the use of secure hardware in the execution environment; however, an insecure software-stack can only provide limited algorithm secrecy. This paper aims to address this problem, by exploring the components of the Trusted Computing Base (TCB) in hardware-supported enclaves. First, we provide a taxonomy and give an extensive understanding of trade-offs during secure enclave development. Next, we present a case study on existing secret-code execution frameworks; which have bad TCB design due to processing secrets with commodity software in enclaves. This increased attack surface introduces additional footprints on memory that breaks the confidentiality guarantees; as a result, the private algorithms are leaked. Finally, we propose an alternative approach for remote secret-code execution of private algorithms. Our solution removes the potentially untrusted commodity software from the TCB and provides a minimal loader for secret-code execution. Based on our new enclave development paradigm, we demonstrate three industrial templates for cloud applications: ① computational power as a service, ② algorithm querying as a service, and ③ data querying as a service.
Activity approach in design of specialized visualization systems

The article discusses the application of activity approach in designing specialized interfaces and visualization systems.Activity approach is a psychological theory developed by the Russian academics of the XX century which suggests analyzing professional work as a type of activity.Activity presupposes consciousness, purposefulness and setting tasks, the accomplishment of which is aimed at achieving a goal.Activity can be broken down into actions serving to accomplish the tasks, and actions, in turn, are broken down into operations.The same activity can be carried out through different operations, and the same operations can be combined into different types of activity.Activity approach in interface design is applied to mass and professional instrumental interfaces.The article provides examples of activity analysis in terms of the used instrumental interfaces; it describes approaches to designing real interfaces for medical purposes, considers the design tasks for specialized visualization systems.For this purpose, the phenomenon of insight can be used as one of the criteria of visualization quality.The article also points out the issues of using virtual reality in scientific visualization.It provides the results of the experiment analyzing the influence of the presence phenomenon in virtual reality on the solution of intellectual tasks, and the basics of visualization system user activity.The article discusses the analysis of specialized (both professional and mass) interfaces serving as instruments in purposeful and productive activity.The analysis is carried out from the perspective of the activity theory and several topics in the fields of psychology and physiology.It is generally believed that the history of interface design clarifies some subtle aspects of modern interactive systems.Further, examples of prototype implementations of service interfaces are provided.Future possibilities for introducing the activity approach into the practical design of specialized interactive systems are also under review.
LOUVRE ABU DHABI: SOCIAL MEDIA IN MARKETING CULTURE

For almost two decades, The United Arab Emirates (UAE) has been on a mission to boost its culture sector through projects that promote cultural enrichment, openness and tolerance, a move from previously conservative notions.The UAE's interest and commitment to developing its culture sector is anchored on its Cultural Mandate, a document highlighting the country's cultural vision for the next thirty yearsthe Abu Dhabi Vision 2030 Master plan.This new cultural movement has witnessed the birth of several new museums of which Louvre Abu Dhabi stands out as a museum that celebrates global cultures.In the wake of attractive new media technologies and new online audiences, museums are challenged to market themselves with new technologies in order to be attractive and remain relevant in contemporary society.A mixed analysis of data revealed Louvre Abu Dhabi's strategic use of social media to market its activities as well as promote the UAE.The study observed the use of only four social media including, Twitter, Facebook, YouTube and Instagram.Louvre Abu Dhabi applied the marketing technique of corporate foresight in media selection and strategic messaging through images, questions and creating audience anticipation.This study argues that through social media, Louvre Abu Dhabi is able to market its activities and events and promote the UAE to global audiences online.By providing digital platforms for attracting audiences and gaining feedback, social media helps Louvre Abu Dhabi to fulfill its cultural function of propagating intercultural understanding and tolerance in the UAE.
Evaluating combined project management models: strategies for agile and plan-driven integration

The combination of agile and traditional practices is important for greater innovation in New Product Development (NPD).This paper analyzes ten combination proposals found in the literature, using the systematic bibliographical review method.Four of them did not show features associated with the combination.Four proposals recommend using agile project management associated with NPD process.Two proposals recommend using a general project plan, prepared through Work Breakdown Structure, scheduling, and integrated with agile iteration planning.The evaluation of these proposals indicated a shortage of recommendations and revealed two classes of combination strategies, namely bottom up and top down.
Incentive Mechanism of Medical Data Sharing Based On Information Entropy in Blockchain Environment

Abstract With the rapid development and wide applications of information technology in the medical field, the data sharing of medical information has become one of the focus that has been received much attention by most researchers in recent years. At present, the scheme of data sharing based on blockchain has been becoming more and more mature, and it has the features of decentralized, secure and tamper-resistant to address the problem of data security in the process of data sharing, thereby improving the quality of medical service of citizens, reducing medical cost and cutting down medical risk. Obviously, information technology is not the main factor to hinder data sharing between medical institutions. Actually, the low enthusiasm of medical institutions was due to lack a comprehensive incentive mechanism of medical data sharing. Combining the medical field, this paper proposes an incentive mechanism of data sharing based on information entropy, which can effectively promote more medical institutions to participate in data sharing and enhance the enthusiasm of medical data sharing. Analyses show that the approach is efficient and effective.
Multi-point codes from the GGS curves

This paper is concerned with the construction of algebraic-geometric (AG) codes defined from GGS curves. It is of significant use to describe bases for the Riemann-Roch spaces associated with some rational places, which enables us to study multi-point AG codes. Along this line, we characterize explicitly the Weierstrass semigroups and pure gaps by an exhaustive computation for the basis of Riemann-Roch spaces from GGS curves. In addition, we determine the floor of a certain type of divisor and investigate the properties of AG codes. Multi-point codes with excellent parameters are found, among which, a presented code with parameters $ [216,190,\geqslant 18] $ over $ \mathbb{F}_{64} $ yields a new record.
Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification

Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to overcome these problems, by exploiting space–time information from videos. One of the most challenging problems in video-based person re-identification is temporal alignment, in addition to spatial alignment. To address the problem, we propose an effective superpixel-based temporally aligned representation for video-based person re-identification, which represents a video sequence only using one walking cycle. Particularly, we first build a candidate set of walking cycles by extracting motion information at superpixel level, which is more robust than that at the pixel level. Then, from the candidate set, we propose an effective criterion to select the walking cycle most matching the intrinsic periodicity property of walking persons. Finally, we propose a temporally aligned pooling scheme to describe the video data in the selected walking cycle. In addition, to characterize the individual still images in the cycle, we propose a superpixel-based representation to improve spatial alignment. Extensive experimental results on three public datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches.
Mengukur Tingkat Pemahaman Pelatihan PowerPoint Menggunakan Quasi-Experiment One-Group Pretest-Posttest

The use of technology in the world of education is an effort to improve the delivery of material to students. Mastery of PowerPoint as the popular tools in making teaching material is needed to be well understood by teachers in order to be able to arrange teaching materials in accordance with their respective fields. These training activities aim to increase understanding of teachers related to PowerPoint so that they are able to use it well. The descriptive statistical research method and the Quasi-Experiment One-Posttest research design were used to determine whether this activity was able to influence the teachers. The teachers were asked to do a pretest before being given treatment to then be given the posttest. The results of the tests showed an increase in teachers' understanding of PowerPoint as the increase in the average value from 74.67 to 76.13 as well as the increase in the percentage of those who got the A, B, and C values from 76% to 84%. Besides this, the even distribution of knowledge has also been increased by showing a decrease in the standard deviation value from 10.93 to 10.12. These can be concluded that this activity can improve teachers' understanding of PowerPoint.
Multiple Graph Kernel Fusion Prediction of Drug Prescription

We present an end-to-end interpretable deep architecture that predicts the success of drug prescription based on multiple graph kernel fusion using a graphical representation of electronic health records. We formulate the predictive model as a binary graph classification problem with a set of graph kernels proposed to capture different aspects of graph structures through deep neural networks. Results using the Taiwanese National Health Insurance Research Database demonstrate that our approach outperforms current start-of-the-art models on accuracy and interpretability. The approach is in preliminary deployment.
CARVE: A Scientific Method-Based Threat Hunting Hypothesis Development Model

A threat hunting exercise is a hypothesis driven exploratory and explanatory research process, the exercise is inherently scientific in nature and lends itself to the application of the scientific method of hypothesis development. The exercise commences with exploratory steps in the threat hypothesis phase to develop a logical argument asserting an existential threat, then follows with explanatory steps in the threat hunt phase to validate the argument. To deem a threat credible, that is, valid and relevant, a threat hunting hypothesis must establish a correlational and causal relationship between the asserted threat and a targeted asset, the hypothesis must adhere to the constructs of the scientific method for the exercise to be defined and measured objectively, and yield valuable and repeatable outcomes. Lack of adherence to the scientific method increases the frequency of invalid and/or irrelevant propositions in threat hypotheses, which diminishes Return on Investment (ROI) in cybersecurity defensive efforts due to wasted cycles of threat hunting exercises. This paper proposes a scientific method-based model, Collect Analyze Relate Validate Establish (CARVE), which can be used to develop valid and relevant threat hunting hypotheses in the context of a given organization's information system and environment. The CARVE model is defined by the following five steps: Collect, Analyze, Relate, Validate, and Establish. The effectiveness of the model is demonstrated using a case study based on the technical alert United States Computer Emergency Readiness Team (US CERT) TA17-293A.
Digital Forensics – A Literature Review

Digital Forensics is a branch of forensic science that is aimed to retrieve, collect and examine the digital evidence of materials found in digital devices, in relation to computer crimes. This paper contains a brief review of the literature aimed to identify the relevant pieces of knowledge in the digital forensics field.
WordNet and Prolog: why not?

This paper describes a set of Prolog modules with predicates to access the information stored in the lexical database WordNet.The aim is to use the defined predicates for empowering (fuzzy) logic programming systems for approximate reasoning tasks.To achieve this goal it is necessary to have means to calculate the degree of relationship between the words.Because WordNet relates words but does not give graded information of the relation between those words, it is necessary to implement standard methods to compute that gradation.
Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding

We propose a novel method for selecting coherent and diverse responses for a given dialogue context.The proposed method re-ranks response candidates generated from conversational models by using event causality relations between events in a dialogue history and response candidates (e.g., "be stressed out" precedes "relieve stress").We use distributed event representation based on the Role Factored Tensor Model for a robust matching of event causality relations due to limited event causality knowledge of the system.Experimental results showed that the proposed method improved coherency and dialogue continuity of system responses.
Towards a Mobility Payment Service Based on Collaborative Open Systems

There is a need for a new strategy and approach to effectively develop mobility services in Europe. These services should be seen by customers as integrated services which are offered by a payment service provider using direct debit payments as established by the European Central Bank. The mobility service would enable a citizen to use multimodal transportation means including public transportation, tolling, parking lots, bicycle rental, etc. in Europe under a single contract. Competing mobility service providers need to be trusted and supervised by authorities based on digital supervision and auditing processes. Digital platforms need to smoothly deal with heterogeneous infrastructures of the different operators which validate utilizations using a variety of means such as card, mobile phone, or automatic vehicle identification systems. All transportation-related events need to be reliably communicated to the payment service providers. Detected failures need a clear and easy to follow resolution procedure. The variety of existing technologies and methodologies to develop informatics systems and processes automation make it difficult to reach such objectives and also an obstacle for authorities to effectively supervise the processes. An open system of systems framework approach combined with a collaborative network support infrastructure to facilitate information exchange and coordination among all involved stakeholders is proposed as a promising way to address these challenges. This paper further develops previous research in this area, better clarifying the challenges, and recommending a development strategy which has been proved in a number of partial implementations.
Implementation of a Beowulf Cluster and Analysis of its Performance in Applications with Parallel Programming

In the Image Processing Research Laboratory (INTI-Lab) of the Universidad de Ciencias y Humanidades, the permission to use the embedded systems laboratory was obtained.INTI-Lab researchers will use this laboratory to do different research related to the processing of large scale videos, climate predictions, climate change research, physical simulations, among others.This type of projects, demand a high complexity in their processes, carried out in ordinary computers that result in an unfavorable time for the researcher.For this reason, one opted for the implementation of a high-performance cluster architecture that is a set of computers interconnected to a local network.This set of computers tries to give a unique behavior to solve complex problems using parallel computing techniques.The intention is to reduce the time directly proportional to the number of machines, giving a similarity of having a low-cost supercomputer.Different performance tests were performed scaling from 1 to 28 computers to measure time reduction.The results will show if it is feasible to use the architecture in future projects that demand processes of high scientific complexity.
Benchmarking deep classifiers on mobile devices for vision-based transportation recognition

Vision-based human activity recognition can provide rich contextual information but has traditionally been computationally prohibitive. We present a characterisation of five convolutional neural networks (DenseNet169, MobileNet, ResNet50, VGG16, VGG19) implemented with TensorFlow Lite running on three state of the art Android mobile phones. The networks have been trained to recognise 8 modes of transportation from camera images using the SHL Locomotion and Transportation dataset. We analyse the effect of thread count and back-ends services (CPU, GPU, Android Neural Network API) to classify the images provided by the rear camera of the phones. We report processing time and classification accuracy.
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology

This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019 task 2 of Morphological Analysis and Lemmatization in Context.This task requires us to produce the lemma and morpho-syntactic description of each token in a sequence, for 107 treebanks.We approach this task with a hierarchical neural conditional random field (CRF) model which predicts each coarse-grained feature (eg.POS, Case, etc.) independently.However, most treebanks are under-resourced, thus making it challenging to train deep neural models for them.Hence, we propose a multi-lingual transfer training regime where we transfer from multiple related languages that share similar typology. 1
DNANet: De-Normalized Attention Based Multi-Resolution Network for Human Pose Estimation.

Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc.) have achieved significant performance on the task of human pose estimation by combining features from various resolutions. In this paper, we propose a novel type of attention module, namely De-Normalized Attention (DNA) to deal with the feature attenuations of conventional attention modules. Our method extends the original HRNet with spatial, channel-wise and resolution-wise DNAs, which aims at evaluating the importance of features from different locations, channels and resolutions to enhance the network capability for feature representation. We also propose to add fine-to-coarse connections across high-to-low resolutions in-side each layer of HRNet to increase the maximum depth of network topology. In addition, we propose to modify the keypoint regressor at the end of HRNet for accurate keypoint heatmap prediction. The effectiveness of our proposed network is demonstrated on COCO keypoint detection dataset, achieving state-of-the-art performance at 77.9 AP score on COCO val2017 dataset and 77.0 on test-dev 2017 dataset without using extra keypoint training data. Our paper will be accompanied with publicly available codes at GitHub.