Reviewerly/exHarmony huggingface dataset:
Blockchain Arhitecture in Smart Pedagogy

Blockchain architecture in Smart Pedagogy offers valuable social propositions like trust, identity, transparency, immutability, smart contracts and disintermediation.There are multiple practices how to record learners' achievements and present learning transcripts where the most common practices are university issued diplomas and vendor issued certificates.There are social networks which allow users to publish their learning achievements for possible stakeholders; however, the common problem is to verify if published diplomas and certificates are valid.This study experiments with lifelong learning transcript called knowledge passport in blockchain architecture.The proposed scenario allows learners to publish evidence of their learning achievements in desired formats connected with blockchain network for instant authenticity verification.The experimental network consists of three nodes where one is located in Latvia, the second -in the USA, and the third -in Asia.Based on the proposed implementation, such approach much better validates learning evidence, eliminates knowledge passport fraud and reduces organizational workload overhead for stakeholders involved in verification of documents certifying a person's knowledge.
PENDETEKSI SERANGAN DDoS (DISTRIBUTED DENIAL OF SERVICE) MENGGUNAKAN HONEYPOT DI PT. TORINI JAYA ABADI

Information security is very important in today's technology. Information storage and crossing is currently no longer using paper media, but has used a lot of computer and internet media. PT. Torini Jaya Abadi is one that uses computer and internet media for information storage and crossing. However, there are disturbances such as heavy server access, which cannot be accessed and sometimes it cannot enter into the database server from the indication that the server might be exposed to a virus or DDoS (Distributed Danial of Service) type attacks occur on the server. Based on these problems, the author uses a honeypot to detect an attack based on alerts that have been accommodated in the log and can also provide information about the attacks that occur, from the results obtained honeyd is able to resemble an original system that pretends to have services that are not real , also able to detect attacks in real time and provide information from attackker, honeyd can emulate virtual hosts that are similar to the original computer, by obtaining logs from the honeyd admin system to find out information that occurs on the server, with honeyd network systems can help detect attacks in real time.
Usage of Accelerometers in the Medical Field of Application and Their Clinical Integration.

We conducted a literature review on the use of accelerometers in medical context, on Pubmed and IEEEXplore. This includes 440 relevant articles. The subsequently identified publications were classified with regard to the medical context (prevention, diagnostics, therapy) as well as according to medical-informatics field of application, e.g. activity-tracking, fall prevention/detection and gait analysis. Furthermore, we analyzed their clinical integration or potential for the clinical usage, including both the technical integration into the clinical structures and respective claims and requirements, e.g. privacy or hygiene. This analysis shows five categories ("without indication" to "concrete implementation"). In 90% no statement was made on clinical integration. Only two articles could be found with concrete implementations, but these descriptions are limited to a more conceptual technical side. This poor situation in final clinical integration has to change in the future, because only by the premise "from workbench to bedside" the medical benefit is given.
Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning

This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts.We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not.Following this classification task, we use a string matching algorithm with a gazetteer to identify the exact index of a toponym within the sentence.We evaluate different approaches in terms of machine learning classifiers, text pre-processing and location extraction on the SemEval-2019 Task 12 dataset, compiled for toponym resolution in the bio-medical domain.Finally, we compare the results with our system that was previously submitted to the SemEval-2019 task evaluation.
Statistics of nonnumeric data in forty years (review)

Forty years ago, the statistics of nonnumeric data was singled out into an independent area of mathematical research methods. First the term «statistics of objects of nonnumeric nature» was used. The textbook on nonnumeric statistics is entitled «Nonnumeric Statistics». Statistics of nonnumeric data is one of the four main fields of applied statistics (along with the statistics of numbers, multivariate statistical analysis, statistics of time series and random processes). Statistics of nonnumeric data is divided into statistics in spaces of general nature and sections devoted to specific types of nonnumeric data (statistics of interval data, statistics of fuzzy sets, statistics of binary relations, etc.). Currently, statistics in spaces of general nature is the central part of applied statistics, and the statistics of nonnumeric data that includes it is the main area of applied statistics. This statement is confirmed by analysis of publications in the section «Mathematical Methods of Research» of our journal. This article is devoted to analysis of the main ideas of statistics of nonnumeric data against the background of the development of applied statistics on the basis of the new paradigm of the methods of mathematical research. Different types of nonnumeric data are considered along with the history of development of the statistics of nonnumeric data and statistical science. The basic ideas of statistics in spaces of general nature are analyzed: mean values, laws of large numbers, extreme statistical problems, nonparametric estimators of the probability distribution density, classification methods (diagnostics and cluster analysis), and statistics of the integral type. Some statistical methods for data analysis in specific nonnumeric spaces are briefly discussed: nonparametric statistics (in most cases, real distributions significantly differ from normal), statistics of fuzzy sets, expert estimation theory (Kemeny median as a sample average of expert orderings), etc. Some unsolved problems in statistics on nonnumeric data are also discussed.
Steepest ascent can be exponential in bounded treewidth problems

We investigate the complexity of local search based on steepest ascent. We show that even when all variables have domains of size two and the underlying constraint graph of variable interactions has bounded treewidth (in our construction, treewidth 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum. This is an improvement on prior recursive constructions of long steepest ascents, which we prove to need constraint graphs of unbounded treewidth.
Generalised interval-valued intuitionistic fuzzy entropy with some similarity measures

Interval-valued intuitionistic fuzzy environment is appropriate for the most of the practical scenarios involving uncertainty, vagueness and insufficient information such as pattern recognition, medical diagnoses and decision making, etc. Entropy, similarity, distance, inclusion and cross entropy measures are few methods used for the measuring uncertainty and classifying interval-valued intuitionistic fuzzy sets. This paper presents the generalised entropy measure for the interval-valued intuitionistic fuzzy sets and relation is established that is used to define the generalised similarity measures using the proposed entropy measure. Further, the proposed entropy measure is compared with some existing measure of entropy with the help of an illustrative example and lastly we have demonstrated how the proposed measure can be used in decision making.
A Comprehensive Study on Large-Scale Person Retrieval in Real Surveillance Scenarios

Person retrieval is a hot research topic due to its important application potential for public security. Though existing algorithms have achieved impressive progresses on current public datasets, it is still a challenging task in the real surveillance scenarios due to the various viewpoints, pose variations and occlusions. Moreover, few of the existing works study the problem of person retrieval on large-scale gallery set, where lots of distractions may deteriorate the retrieval results heavily. To have a deep understanding on the above challenges, we perform a comprehensive study on current state-of-the-art person retrieval algorithms with a large-scale benchmark in real surveillance scenarios. In the study, two kinds of techniques, i.e., attribute recognition and person re-identification, including eight algorithms, are evaluated at both algorithm level and system level. Here, the system-level evaluations investigate the effects of the combinations of the above algorithms with the module of person detection, where lots of distractions in person detection results pose a big challenge for person retrieval in real scenes. Extensive evaluations with large gallery sizes (up to 243k) and comprehensive analyses are presented in the study, which will guide researchers to develop more advanced algorithms in future.
Learning Low-Level Behaviors and High-Level Strategies in Humanoid Soccer

This paper investigates the learning of both low-level behaviors for humanoid robot controllers and of high-level coordination strategies for teams of robots engaged in simulated soccer. Regarding controllers, current approaches typically hand-tune behaviors or optimize them without realistic constraints, for example allowing parts of the robot to intersect with others. This level of optimization often leads to low-performance behaviors. Regarding strategies, most are hand-tuned with arbitrary parameters (like agents moving to pre-defined positions on the field such that eventually they can score a goal) and the thorough analysis of learned strategies is often disregarded. This paper demonstrates how it is possible to use a distributed framework to learn both low-level behaviors, like sprinting and getting up, and high-level strategies, like a kick-off scenario, outperforming previous approaches in the FCPortugal3D Simulated Soccer team.
What’s in the Cloud? - An examination of the impact of cloud storage usage on the browser cache

Cloud storage is now a well established and popular service adopted by many individuals and organizations, often at a scaled cost, with free accounts also available.It provides users with the ability to store content on a cloud service provider's infrastructure, offering the benefit of redundancy, reliability, security, the flexibility of access, and the potential assumed the liability of the provider for data loss within the contexts of a licensing agreement.Consequently, this form of remote storage provides a regulatory challenge as content which once resided upon a seized digital exhibit, available for scrutiny during a digital forensic investigatory, may no longer be present where attempting to acquire access to it creates costing and juridical difficulties.This article offers a digital forensic examination of trace-evidence left in the Internet browser cache following cloud storage account usage and interaction.Following interactions with Dropbox and Google Drive in the Chrome browser, testing demonstrates the possibility to recover data capable of facilitating a partial reconstruction of a user's cloud storage account, with results offered and contextualized.
Acceptance Prediction for Answers on Online Health-care Community

With the development of e-Health, it plays a more and more important role in predicting whether a doctor's answer can be accepted by a patient through online healthcare community. Unlike the previous work which focus mainly on the numerical feature, in our framework, we combine both numerical and textual information to predict the acceptance of answers. The textual information is composed of questions posted by the patients and answers posted by the doctors. To extract the textual features from them, we first trained a sentence encoder to encode a pair of question and answer into a co-dependent representation on a held-out dataset. After that,we can use it to predict the acceptance of answers by doctors.Our experimental results on the real-world dataset demonstrate that by applying our model additional features from text can be extracted and the prediction can be more accurate. That's to say, the model which take both textual features and numerical features as input performs significantly better than model which takes numerical features only on all the four metrics (Accuracy, AUC, F1-score and Recall).This work proposes a generic framework combining numerical features and textual features for acceptance prediction, where textual features are extracted from text based on deep learning methods firstly and can be used to achieve a better prediction results.
Activity Monitoring of People with Dementia in a Cognitive Stimulation Intervention

Commercial activity trackers are increasingly being used to support healthcare research. While their accuracy has been questioned, they do provide more precise information on some parameters relevant to wellbeing than self-report, such as steps walked and sleep duration. Few studies, however, report on the use of these devices with people with dementia. We report the design and preliminary results of a cognitive stimulation therapy for people with dementia in which participants were monitored using activity trackers. We describe the strategies implemented to promote adoption and adherence of these devices, some of the issues we faced in the study and recommendations for future studies. An analysis of the results show that these devices are useful to show the differences in patterns of activity of the participants and were able to track differences in behavior in the days when the therapy intervention has held.
Anomaly Detection for Home Activity based on Sequence Pattern

In Malaysia, the elderly population continues to grow.At the same time, young adults are unable to take care of their elderly parents due to work commitments.This results in an increasing number of elderly people living in solitude.Therefore, it is crucial to monitor elderly people's behavior, especially the pattern of their daily home activities.Abnormal behaviors in carrying out home activities may indicate health concerns in elderly people.Past studies have proposed the use of complex machine learning algorithms to detect anomalies in daily sequences of home activities.In this paper, a simple, alternative method for detecting anomalies in daily sequences of home activities is presented.The experiment results demonstrate that the model achieved a test accuracy of 90.79% on a public dataset.
Sys-TM: A Fast and General Topic Modeling System

Topic models, such as LDA and its variants, are popular probabilistic models for discovering the abstract “topics” that occur in a collection of documents. However, the performance of topic models may vary a lot for different workloads, and it is not a trivial task to achieve a well-optimized implementation. In this paper, we systematically study all recently proposed samplers over LDA: AliasLDA, F+LDA, LightLDA, and WarpLDA, and discover a novel system tradeoff by considering the diversity and skewness of workloads. Then, we propose a hybrid sampler which can cleverly choose an efficient sampler with the tradeoff, and apply the hybrid sampler to LDA and its variants, including STM, TOT and CTM. Finally, we build a fast and general topic modeling system Sys-TM, which provides a unified topic modeling framework by integrating the hybrid sampler. Based on our empirical studies, the hybrid sampler outperforms the state-of-the-art samplers by up to 2× 2× over various topic models, and with carefully engineered implementation, Sys-TM is able to outperform the existing systems by up to 10× 10×.
Organoid model of mammographic density displays a higher frequency of aberrant colony formations with radiation exposure

Abstract Motivation Aberrant three-dimensional (3D) colony organization of premalignant human mammary epithelial cells (HMECs) is one of the indices of dysplasia. An experiment has been designed where the stiffness of the microenvironment, in 3D culture, has been set at either low or high level of mammographic density (MD) and the organoid models are exposed to 50 cGy X-ray radiation. This study utilizes published bioinformatics tools to quantify the frequency of aberrant colony formations by the combined stressors of stiffness and X-ray exposure. One of the goals is to develop a quantitative assay for evaluating the risk factors associated with women with high MD exposed to X-ray radiation. Results Analysis of 3D colony formations indicate that high stiffness, within the range of high MD, and X-ray radiation have an approximately additive effect on increasing the frequency of aberrant colony formations. Since both stiffness and X-ray radiation are DNA-damaging stressors, the additive effect of these stressors is also independently validated by profiling activin A-secreted protein. Secretion of activin A is known to be higher in tissues with a high MD as well as tumor cells. In addition, we show that increased stiffness of the microenvironment also induces phosphorylation of γH2AX-positive foci. The study uses two HMECs derived from a diseased tissue (e.g. MCF10A) and reduction mammoplasty of normal breast tissue (e.g. 184A1) to further demonstrate similar traits in the frequency of aberrant colony organization. Supplementary information Supplementary data are available at Bioinformatics online.
A Geometrical Realisation of Quasi-Cyclic Codes

We study and enumerate cyclic codes which include generalised Reed-Solomon codes as function field codes. This geometrical approach allows to construct longer codes and to get more information on the parameters defining the codes. We provide a closed formula in terms of Stirling numbers for the number of irreducible polynomials and we relate it with other formulas existing in the literature. Further, we study quasi-cyclic codes as orbit codes in the Grassmannian parameterizing constant dimension codes. In addition, we review Horn’s algorithm and apply it to construct classical codes by their defining ideals.
Graph Embeddings for Frame Identification

Lexical resources such as WordNet (Miller, 1995) and FrameNet (Baker et al., 1998) are organized as graphs, where relationships between words are made explicit via the structure of the resource.This work explores how structural information from these lexical resources can lead to gains in a downstream task, namely frame identification.While much of the current work in frame identification uses various neural architectures to predict frames, those neural architectures only use representations of frames based on annotated corpus data.We demonstrate how incorporating knowledge directly from the FrameNet graph structure improves the performance of a neural network-based frame identification system.Specifically, we construct a bidirectional LSTM with a loss function that incorporates various graph-and corpus-based frame embeddings for learning and ultimately achieves strong performance gains with the graphbased embeddings over corpus-based embeddings alone.
Predicting Sentiment of Polish Language Short Texts

The goal of this paper is to use all available Polish language data sets to seek the best possible performance in supervised sentiment analysis of short texts.We use text collections with labeled sentiment such as tweets, movie reviews and a sentiment treebank, in three comparison modes.In the first, we examine the performance of models trained and tested on the same text collection using standard cross-validation (in-domain).In the second we train models on all available data except the given test collection, which we use for testing (one vs rest cross-domain).In the third, we train a model on one data set and apply it to another one (one vs one cross-domain).We compare wide range of methods including machine learning on bag-of-words representation, bidirectional recurrent neural networks as well as the most recent pre-trained architectures ELMO and BERT.We formulate conclusions as to cross-domain and in-domain performance of each method.Unsurprisingly, BERT turned out to be a strong performer, especially in the cross-domain setting.What is surprising however, is solid performance of the relatively simple multinomial Naive Bayes classifier, which performed equally well as BERT on several data sets.
Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer.

131 Background: Machine learning (ML) algorithms can accurately identify patients with cancer at risk of short-term mortality and facilitate timely conversations about treatment and end-of-life preferences. We developed, validated, and implemented a ML algorithm to predict mortality in a general oncology setting, using electronic health record (EHR) data prior to a clinic visit. Methods: Our cohort consisted of patients aged ≥18 years who had an encounter in outpatient oncology practices within a large academic health system between February 1st and July 1st, 2016. We randomly split the sample into training (70%) and validation (30%) cohorts at the patient-encounter level. We trained three ML algorithms to predict 180-day mortality and describe performance in the holdout validation cohort. From October 2018 to February 2019, we used the best-performing algorithm to generate weekly lists of high-risk patients at a single community oncology practice and studied the impact on rates of documented serious illness conversations (SICs). Results: Among 62,377 encounters used to train the algorithms, 7.4% involved a patient who died within 180 days. Gradient boosting and/or random forest outperformed logistic regression in all metrics (Table), and the gradient boosting model had superior discrimination and calibration. In the gradient boosting model, observed 180-day mortality was 45.5% (95% CI 39.0-52.3%) in the high-risk group vs. 3.3% (95% CI 2.9-3.7%) in the low-risk group. In a survey of oncology clinicians, 59% of patients flagged as high-risk were appropriate for a serious illness conversation in the upcoming week (response rate 52%). Five months after implementing the intervention, average monthly documented SICs increased by 23% (31.7 to 39). Conclusions: A ML algorithm based on EHR data accurately identified patients with cancer at risk of short-term mortality, was concordant with oncologists’ assessments, and was associated with more SICs. [Table: see text]
High-Speed Quantum Photonics with Plasmonic Metamaterials Empowered by Machine Learning

We outline future directions in the development of a platform for high-speed integrated quantum photonics, the use of plasmonics to outpace quantum decoherence and the application of machine-learning techniques for photonics designs and quantum optical measurements.