Reviewerly/exHarmony huggingface dataset:
Optimum Distributed Estimation of a Spatially Correlated Random Field

The distributed estimation of a spatially correlated random field with decentralized sensor networks is studied in this paper. Nodes in the network take spatial samples of the random field, then each node estimates the values of arbitrary points on the random field by iteratively exchanging information with each other. The objective is to minimize the estimation mean squared error (MSE) while ensuring all nodes reach a distributed consensus on the estimation results. We propose a distributed iterative linear minimum mean squared error (LMMSE) algorithm that contains a state consensus stage and a local estimation stage in each iteration. The proposed algorithm requires the knowledge of the second-order statistics of the random field, and they are estimated by using a distributed learning algorithm with the help of distributed consensus. The key parameters of the algorithm, including an edge weight matrix and a sample weight matrix, are designed to minimize an MSE upper bound at all nodes when the number of iterations is large. It is shown that the optimum performance can be achieved by distributively mapping the high dimension measurement samples from all nodes into a low dimension subspace related to the covariance matrices of data and noise samples. The low-dimension mapping is achieved in a distributed manner through iterative information propagation. The low dimension mapping can significantly reduce the amount of data exchanged in the network, thus improve the convergence speed of the iterative algorithm.
Successes in Deployed Verified Software (and Insights on Key Social Factors)

In this talk, we will share our experience in the successful deployment of verified software in a wide range of application domains, and, importantly, our insights on the key factors enabling such successful deployment, in particular the importance of the social aspects of a group working effectively together. Our formally verified microkernel, seL4, is now used across the world in a number of applications that keeps growing. Our experience is that such an uptake is enabled not only by a technical strategy, but also by a tight integration of people from multiple disciplines and with both research and engineering profiles. This requires a strong social culture, with well designed processes, for working as one unified team. We share our observations on what concrete social structures have been key for us in creating real-world impact from research breakthroughs.
Reading Level System on Fairy Tale Stories for Children Using Dolch Sight Word Vocabulary with Majority Voting Algorithm

Children need appropriate reading contents to improve their imagination and power of thought. Fairy tales become one of reading contents that are good for children growth, because it can arise children imagination. However, there is no reading content categorization for Indonesian fairy tale stories. Therefore, in this research, a Reading Level System was proposed to rank reading contents for Indonesian children. Adopting Dolch Sight Word Vocabulary, it enabled to build text clustering using the occurrences of empirical probability of words for each Dolch Sight Word Vocabulary in a fairy tale. The values of empirical probability levels were calculated for each fairy tale and the results of calculating the highest probability value for each fairy tale using the Majority Voting Algorithm. The results found that the majority of Reading Level System using Dolch Sight Word Vocabulary were level 3 and level 4, where at these levels the words component of the Dolch Sight words were larger than the rest two levels.
Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation.

We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model's ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module. These extensions result in a generation process that is faster, more fluent, and more accurate.
Automated systems only: why CISOs should switch off their dumb machines

Cyber security is supposed to reduce risk and be cost effective at the same time. It's supposed to take the burden of legwork away from the CISO and his or her team. It's supposed to reduce the financial and reputational risk posed by malicious actors in an efficient, intelligent manner. Cyber security is supposed to reduce risk. What it's not supposed to do is generate reams of near-to-useless information, burying the security team in an avalanche of flags and alerts. Automating processes ensures that common threats are caught and dealt with early. Security orchestration, automation and response (SOAR) can help, stitching together an organisation's disparate policy enforcement infrastructure, such as firewalls, gateways and other types of controllers, as well as existing event management systems, explains Spencer Young of Imperva.
Research on Scientific Bibliography Clustering Technology Based on Citation Information Merging

Clustering analysis is an important step in extracting and analyzing technology bibliography trend. The bibliographic records, consisting of the title, authors, keywords, and publications, are short texts carrying less information than long texts, while bibliographic network has the citation relations among bibliographies. The experimental dataset therein refers to bibliographies in the computer field crawled from the existing literature databases. Based on traditional Hierarchical Clustering algorithm, this paper merges the cocitation relations. The experiment result shows that the mean silhouette coefficient of the Agglomerative Clustering algorithm that merges the co-citation relations among the bibliographies is improved obviously, thus effectively improving clustering result among bibliographies with strong co-citation relations, and good clusters provide a solid research basis for the follow-up bibliography trend analysis.
Operational Matrix Basic Spline Wavelets of Derivative for linear Optimal Control Problem

The main importance goal in this paper is studying the interesting properties of basic spline wavelets functions (BSWFs) and derived some new basic formulations of them. The important operational matrix is devoted in two ways, the first one is the derivative of BSWFs in terms of the lower order of  BSWFs  while the second is the derivative of BSWFs in terms of the same order of BSWFs.  The expression formula for the operational matrix  is determined for different orders. In addition an useful formulas concerning the power function and BSMSFs are also presented. The polynomials and wavelets expansions together with operational matrices can be employed to solve problems in applied science and other fields of approximation theory. In this work, two optimal control problem are tested with the aid of operational matrix of derivative for BSWFs with satisfactory results.
Learned Distractor Rejection during Strong Target Guidance

Most theories of visual attention posit a selective mechanism that allows relevant information to proceed through processing while filtering out irrelevant, distracting, stimuli. Visual search is more efficient when target-features are known in advance, through guidance with a target-template. Further, visual attention can learn to reject distractors through experience with statistical regularities, by establishing an experience-based template for rejection. The current study was aimed at determining whether visual attention could learn to reject distractors through feature-based experience in the presence of strong target guidance, and under which circumstances this learning occurred. In three experiments, we presented individuals with two-color, spatially unsegregated visual search displays. Prior to each search display, individuals were presented with a word cue either indicating the target color or the word “neutral”. For each individual, the target never appeared in one color: the learned distractor color. In Experiment 1a, we presented neutral cues only, and observed learned distractor rejection, namely, faster mean RTs on trials with the learned distractor color present than absent. In Experiment 1b, to demonstrate learned distractor rejection with target guidance, we presented both types of cues, and observed a target cueing effect (faster mean RTs on trials following a target cue than a neutral cue), and critically, learned distractor rejection following both neutral cues and target cues. In Experiment 2, we asked whether learned distractor rejection required the neutral trials to establish learning, and found that it did not. Finally, in Experiment 3, we provided more direct target guidance, namely, the cue directed individuals to the only color-matching item in the display and we still observed learned distractor rejection during target guidance. Thus, we argue that visual attention can establish both target templates and templates for rejection and use both templates to guide visual search.
Comparison of Human Fall Acceleration Signals Among Different Datasets

Falls can have a major impact on physical and psychological health on elderly people who experience them. To reduce the negative consequences of a fall event, automatic fall detection systems are being developed to correctly identify when a person falls and alert the caregivers to provide assistance on time. Performance of fall detection algorithms is tested with datasets containing measurements of falls and regular activities of daily living. In this work we acquired fall signals with accelerometer sensors and compared them with digitized fall signal records from 6 different datasets. Additionally, three threshold-based algorithms for fall detection were implemented and their performance was tested with the analyzed datasets. The results suggest that a heterogeneity among the fall data in distinct datasets exist and that it affects the performance measures of tested datasets.
Discussion on the reform of university public computer curriculum under the background of new curriculum standard

With the gradual advancement of the new curriculum reform of the information technology in senior high school, university public computer curriculum faces problems such as curriculum content lags, separation of theory and practice, uneven student level, unreasonable evaluation system, single teaching model and platform. In response to these problems, this paper proposes some strategies for students to learn public computer courses on the basis of personalized learning based on the new curriculum standards of high school information technology, such as the Advanced Placement, the implementation of stratified teaching, the development of exemption systems, the implementation of the online + offline mixed teaching model, the improvement of the examination system, etc. It is expected to provide some feasible ideas for the better development of relevant teaching work.
Remixing Headlines for Context-Appropriate Flavor Text

We describe a prototype of Churnalist, a headline generator for creating contextually-appropriate fictional headlines that can be used as flavor text in games. Churnalist creates new headlines by remixing existing headlines. It extracts seed words from free text input, searches for related words in a dataset of word embeddings and uses these words in the new headlines. The system requires no linguistic expertise or hand-coded language models from the user.
Programming by Example: Efficient, but Not "Helpful"

Programming by example (PBE) is a powerful programming paradigm based on example driven synthesis. Users can provide examples, and a tool automatically constructs a program that satisfies the examples. To investigate the impact of PBE on real-world users, we built a study around StriSynth, a tool for shell scripting by example, and recruited 27 working IT professionals to participate. In our study we asked the users to complete three tasks with StriSynth, and the same three tasks with PowerShell, a traditional scripting language. We found that, although our participants completed the tasks more quickly with StriSynth, they reported that they believed PowerShell to be a more helpful tool.
PENGARUH SISTEM INFORMASI AKUNTANSI PENJUALAN KREDIT DAN SISTEM INFORMASI AKUNTANSI PENERIMAAN KAS TERHADAP EFEKTIFITAS PENGENDALIAN PIUTANG

The purpose of this research is to find out and learn how much influence thesales accounting information system, the cash receipt accounting information system onthe effectiveness of controlling accounts receivable. The population in this study is limitedto PT Tera Data Indonesia's employees. The sampling technique was 30 respondents.Data collection techniques use primary and secondary data with the help of SPSS version20. For windows. From the test results it can be concluded that the credit salesaccounting information system, the cash receipt accounting information system has aneffect on the effectiveness of the accounts receivable control.Keyword : Credit Sales Accounting Information Systems, Accounting InformationSystems Cash Receipts, Accountability Control Effectiveness.
IR-Net: Forward and Backward Information Retention for Highly Accurate Binary Neural Networks

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation; (2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods.
Detection and Automatic Deletion of Staples in Images of Wound of Abdominal Surgery for m-Health Applications

Patients undergoing abdominal operations have to perform a post-operative follow-up during long periods of time after surgery. These face-to-face consultations have an economic cost and affect patients’ quality of life. To simplify the follow-up of these patients, a smartphone application could be used. Based on an image of their surgical wound, post-operative complications may be detected. There are two cues to detect wound infection: inflammation and reddening. In this work, we aim at detecting and segmenting the wound. The first step of this process consists of locating and deleting the staples that maintain the wound closed because they distort the overall color of the image. To do this, we applied the discrete fuzzy morphological dilation operation to the whole image. Next, we applied an inpainting algorithm to restore the regions detected as staples. Finally, using fuzzy sets we determine if there are areas of the wound that present a deviation with respect to the red color, and study if the wound is reddening.
Closer: A New Design Principle for the Privileged Virtual Machine OS

In most of today's virtualized systems (e.g., Xen), the hypervisor relies on a privileged virtual machine (pVM). The pVM accomplishes work both for the hypervisor (e.g., VM life cycle management) and for client VMs (I/O management). Usually, the pVM is based on a standard OS (Linux). This is source of performance unpredictability, low performance, resource waste, and vulnerabilities. This paper presents Closer, a principle for designing a suitable OS for the pVM. Closer consists in respectively scheduling and allocating pVM's tasks and memory as close to the involved client VM as possible. By revisiting Linux and Xen hypervisor, we present a functioning implementation of Closer. The evaluation results of our implementation show that Closer outperforms standard implementations.
ICU Patient Prediction for Moving with Decision Tree C4.5 and Naïve Bayes Algorithm

Critical patients need intensive care and supervision by the medical team in the Intensive Care Unit (ICU), including ventilators, monitors, Central Venous Pressure (CVP), Electrocardiogram (ECG), Echocardiogram (ECHO), medical supply, and medical information that is fast, precise, and accurate. In the ICU treatment room requires data that needs to be processed and analyzed for decision making. This study analyzed the ventilator, CVP and also Sepsis Diagnosis related to the data of moving patients and patients dying. This study also uses the decision tree algorithm C.45 and Naive Bayes to determine the level of accuracy of patient care and supervision information in the ICU. The results showed that the decision tree algorithm C.45 has an accuracy of 81.55% and Naive Bayes of 81.54%. The decision tree C.45 algorithm has almost the same advantages as the Naive Bayes algorithm.
A Comparative Study on Text Representation Models for Topic Detection in Arabic

Topic Detection (TD) plays a major role in Natural Language Processing (NLP). Its applications range from Question Answering to Speech Recognition. In order to correctly detect document’s topic, wes hall first proceed with a text representation phase to transform the electronic documents contents into an efficiently software handled form. Significant efforts havebeen deployed to construct effective text representation models, mainly for English documents. In this paper, we realize a comparative study to investigate the impactof using stems, multi-word terms and named entities as text representation models on Topic Detection for Arabic unvowelized documents. Our experiments indicate that using named entities as text representation model is the most effective approach for Arabic Topic Detection. The performances of the two other approaches are heavily dependent on the considered topic. In order to enhance the Topic Detection results, we use combined vocabulary vectors based on stems and named entities (respective lystems and multi-word terms) association to model topics more accurately. This approach effectiveness has been endorsed by the enhancement of the system performances.
Large-scale machine translation evaluation of the iADAATPA project

This paper reports the results of an indepth evaluation of 34 state-of-the-art domain-adapted machine translation (MT) systems that were built by four leading MT companies as part of the EU-funded iADAATPA project. These systems support a wide variety of languages for several domains. The evaluation combined automatic metrics and human methods, namely assessments of adequacy, ﬂuency, and comparative ranking. The paper also discusses the most effective techniques to build domain-adapted MT systems for the relevant language combinations and domains.
CICLO DE VIDA DA INFORMAÇÃO NO SUPORTE AO PROCESSO DE INOVAÇÃO: UMA PROPOSTA DE MODELO INTERATIVO

The objective of this article is to propose a theoretical model representative of the information flow in the innovation process. Managing information is crucial to the practice of innovation, especially in the context of adopting the model of open innovation and intensive use of information and communication technologies. From an exploratory research on secondary sources and the use of bibliographic research techniques, an interactive theoretical model based on the information life cycle stages of Beal (2012) and the innovation process phases of Tidd and Bessan (2015). In the proposed interactive model, the stages of the information lifecycle manage to interact with the phases of the innovation process, supporting its execution in the management of innovation in organizations. In addition, its elaboration contemplates an extension in the way initially presented for the life cycle of the information referenced by other authors.
Bidirectional Text Compression in External Memory

Bidirectional compression algorithms work by substituting repeated substrings by references that, unlike in the famous LZ77-scheme, can point to either direction. We present such an algorithm that is particularly suited for an external memory implementation. We evaluate it experimentally on large data sets of size up to 128 GiB (using only 16 GiB of RAM) and show that it is significantly faster than all known LZ77 compressors, while producing a roughly similar number of factors. We also introduce an external memory decompressor for texts compressed with any uni- or bidirectional compression scheme.
CROA: A Content-Based Recommendation Optimization Algorithm for Personalized Knowledge Services

Information explosion is a typical feature of the Big data. Learners can easily find a wide variety of knowledge information online. However, the expansion of information also makes it difficult for learners to retrieve the information they want and fall into the problem of knowledge overload. In order to solve this problem effectively, we propose a content-based recommendation optimization algorithm for personalized knowledge services (CROA). First, the feature vector model is established, where each item information is represented by specific words and key information is highlighted by weighting. Second, a dynamic user vector model is then established to capture the latest preference information of the user. Finally, the feature vector model is combined with the multi-user comparison results thereby obtaining a list of recommendations. Experimental results indicate that CROA outstands in knowledge recommendation mechanism and can also alleviate knowledge overload.
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation

We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet, we automatically construct a semantic knowledge base called DisDict, which provides refined feature words that highlight the differences among word senses, i.e., synsets. Second, we automatically generate new sense-labeled data by DisDict from unlabeled corpora. Third, these generated data, together with manually labeled data and unlabeled data, are fed to a neural framework conducting supervised and unsupervised learning jointly to model the semantic relations among synsets, feature words and their contexts. The experimental results show that KDSL outperforms several representative state-of-the-art methods on various major benchmarks. Interestingly, it performs relatively well even when manually labeled data is unavailable, thus provides a potential solution for similar tasks in a lack of manual annotations.