Reviewerly/exHarmony huggingface dataset:
Approaches for Multi-View Redescription Mining

The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain redescriptions and allow for the exploration and analyses of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework can use any multi-target regression or multi-label classification algorithm, with models that can be represented as sets of rules, to generate redescriptions. Multi-view redescriptions are built using incremental view-extending heuristic from initially created two-view redescriptions. In this work, we use different types of Predictive Clustering trees algorithms (regular, extra, with random output selection) and the Random Forest thereof in order to improve the quality of final redescription sets and/or execution time needed to generate them. We provide multiple performance analyses of the proposed framework and compare it against the naive approach to multi-view redescription mining. We demonstrate the usefulness of the proposed multi-view extension on several datasets, including a use-case on understanding of machine learning models - a topic of growing importance in machine learning and artificial intelligence in general.
Industrial visual perception technology in Smart City

In order to study the application effect and function of industrial visual perception technology in smart city, the image processing and quality evaluation system was constructed by using convolutional neural network (CNN) and Internet of things (IoT) technology. The system was simulated, and then the quality performance of image and video obtained by using industrial visual perception technology was processed and analyzed. The results show that in the analysis of image local optimization effect, it is found that the classification performance of all algorithms decreases with the increase of noise, and the performance of local anisotropic mode (LAP) is superior, which has strong robustness to rotation, illumination, and noise. In the analysis of image feature similarity effect, it is found that the chi square distance between Log Gabor features is positively correlated with the degree of image distortion, and the validity of the measurement method is verified. Further analysis of the video processing effect of industrial visual perception technology shows that the video processing effect of test algorithm is significantly better than that of HM16.8 by comparing the distortion performance of the two algorithms with different sequences, with low distortion and significantly improved performance. Therefore, through the research, it is found that the improved CNN algorithm is superior to other algorithms in image and video processing.
Review of Pedagogical Principles of Cyber Security Exercises

Modern digitalized cyber domains are extremely complex ensemble.Cyber attacks or incidents against system may affect capricious effects for another system or even for physical devices.For understanding and training to encounter those effects requires an effective and complex simulation capability.Cyber Security Exercises are an effective expedient for training and learning measures and operations with their outcomes in that complex cyber domain.Learning in cyber security exercises is relevant for different level actors in organisation hierarchy.Technical experts are able to train the technical capabilities whereas decision makers are able to train the decision-making capabilities under hectic cyber incident.In this paper, the pedagogical aspects of cyber security exercises are discussed in accordance with the law of the lifecycle of the cyber security exercise: planning phase, implementation phase, and feedback phase.
An adaptive multiclass nearest neighbor classifier

We consider a problem of multiclass classification, where the training sample S_n={(X i ,Y i )} n i=1 is generated from the model ℙ( Y = m | X = x ) = η m ( x ), 1 ≤ m ≤ M , and η 1 ( x ), …, η M ( x ) are unknown α -Holder continuous functions. Given a test point X , our goal is to predict its label. A widely used k-nearest-neighbors classifier constructs estimates of η 1 ( X ), …, η M ( X ) and uses a plug-in rule for the prediction. However, it requires a proper choice of the smoothing parameter k, which may become tricky in some situations. We fix several integers n 1 , …, n K , compute corresponding n k -nearest-neighbor estimates for each m and each n k and apply an aggregation procedure. We study an algorithm, which constructs a convex combination of these estimates such that the aggregated estimate behaves approximately as well as an oracle choice. We also provide a non-asymptotic analysis of the procedure, prove its adaptation to the unknown smoothness parameter α and to the margin and establish rates of convergence under mild assumptions.
Unsupervised separation of dynamics from pixels

We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way. We introduce a probabilistic model that first generate noisy positions for each object through a separate linear state-space model, and then renders the positions of all objects in the same image through a highly non-linear process. Such a linear representation of the dynamics enables us to propose an inference method that uses exact and efficient inference tools and that can be deployed to query the model in different ways without retraining.
Analysis of Criminal Activities Exploiting Social Media: With Special Regards to Criminal Cases of Wechat Fraud in Chinese Jurisdiction

Abstract Social media provide a more convenient way for daily communication and business transaction, while they are also exploited by potential criminals to perpetrate offenses of different natures. Fraud is one of the most frequently reported offenses, some of which involve the use of WeChat, an application now used by 846 million users worldwide. The article is designed to give a comprehensive statement of features, causes, and types of WeChat fraud currently existing in China. The article also formulates important countermeasures based on academic conclusions, law enforcement opinions as well as written criminal judgments collected from Chinese courts during the research.
Symbolic Graph Embedding Using Frequent Pattern Mining

Relational data mining is becoming ubiquitous in many fields of study. It offers insights into behaviour of complex, real-world systems which cannot be modeled directly using propositional learning. We propose Symbolic Graph Embedding (SGE), an algorithm aimed to learn symbolic node representations. Built on the ideas from the field of inductive logic programming, SGE first samples a given node's neighborhood and interprets it as a transaction database, which is used for frequent pattern mining to identify logical conjuncts of items that co-occur frequently in a given context. Such patterns are in this work used as features to represent individual nodes, yielding interpretable, symbolic node embeddings. The proposed SGE approach on a venue classification task outperforms shallow node embedding methods such as DeepWalk, and performs similarly to metapath2vec, a black-box representation learner that can exploit node and edge types in a given graph. The proposed SGE approach performs especially well when small amounts of data are used for learning, scales to graphs with millions of nodes and edges, and can be run on an of-the-shelf laptop.
Extending A Model for Ontology-Based Arabic-English Machine Translation (NAN)

The acceleration in telecommunication needs leads to many groups of research, especially in communication facilitating and Machine Translation fields.While people contact with others having different languages and cultures, they need to have instant translations.However, the available instant translators are still providing somewhat bad Arabic-English Translations, for instance when translating books or articles, the meaning is not totally accurate.Therefore, using the semantic web techniques to deal with the homographs and homonyms semantically, the aim of this research is to extend a model for the ontology-based Arabic-English Machine Translation, named NAN, which simulate the human way in translation.The experimental results show that NAN translation is approximately more similar to the Human Translation than the other instant translators.The resulted translation will help getting the translated texts in the target language somewhat correctly and semantically more similar to human translations for the Non-Arabic Natives and the Non-English natives.
Blockchain in Digital Government: Research Needs Identification

The so-called disruptive technologies play an important role in shaping the next generation of digital government: Government 3.0. This new stage places the focus on the data-driven and evidence-based decision and policy making. The prerequisite in achieving this stage is the seamless access to government data. The use of blockchain supports the interoperability-by-default concept in the creation of public services. At the same time blockchain is addressing another important problem governments facing across all over the world, namely, low level of citizens’ trust. In this paper, the authors review literature and projects on blockchain as a tool for improving interoperability and trust in Government 3.0 and they outline the issues for further research in the area, taking into consideration the knowledge collected in existing projects and the opinions of experts in the domain. The research needs are synthesized a) by analysing recent EU-funded projects involving blockchain and b) by drawing a future scenario, which is evaluated by experts to formulate further research needs. Finally, fifteen research needs are identified for blockchain in digital government.
Cloud Cryptography: User End Encryption

Cloud computing has made the life of individual users and work of business corporations so much easier by providing them data storage services at very low costs. Individual users can store and access their data through shared cloud storage service anywhere anytime. Similarly, business corporation consumers of cloud computing can store, manage, process and access their big data with quite an ease. However, the security and privacy of users' data remains vulnerable in cloud computing Availability, integrity and confidentiality are the three primary elements that users consider before signing up for cloud computing services. Many public and private cloud services have experienced security breaches and unauthorized access incidents. This paper suggests user end cryptography of data before uploading it to a cloud storage service platform like Google Drive, Microsoft, Amazon and CloudSim etc. The proposed cryptography algorithm is based on symmetric key cryptography model and has been implemented on Amazon S3 cloud space service.
Factors Determine the Behavioural Intention in Adopting the Blockchain Technology by Malaysian Public Sector Officers

Recently, Blockchain technology has attracted great attention in both private organisations and the public sector around the world. However, not many are aware and understand this new technology. Thus, lack of understanding of the Blockchain technology could influence the intention in adopting the technology. Therefore, the objective of this paper is to identify the behaviour intention towards adopting Blockchain Technology in the Malaysian Public Sector. Data were gathered using a questionnaire to analyse the relationship between factors such as Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Trust with Behavioural Intention to adopt Blockchain Technology. The data were gathered from 100 officers in various government department. The survey revealed that majority of the government officers are aware about the Blockchain technology. However, most of them have inadequate exposure and knowledge about the technology. Findings from the regression analysis found that Trust, Performance Expectancy and Social Influence positively and significantly influence the behaviour intention of government officers in adopting Blockchain technology. Meanwhile, Effort Expectancy and Facilitating Conditions were found not significant. The findings from this study suggest that it is essential to develop strategies to implement a suitable Blockchain application in the public sector. Prior to such implementation, it is imperative for government officers to be equipped with knowledge, skills and resources related to the Blockchain technology.
ALGORITHM FOR PICKING, ORIENTING AND PLACING AN OBJECT WITH A 6-AXIS ROBOT AND A 2D VISUAL INSPECTION CAMERA

With the development of visual inspection and artificial intelligence tech-nologies, robots are increasingly required to replace humans in more complex operations that require the robot to "see" and "think." For this reason, the present develop-ment proposes, synthesizes, and puts into operation an algorithm for taking a part with a specific marker from a 6-axis robot, presenting it to a 2D camera for visual inspection and its correct orientation based on information received from the camera and its placement. on another part with a pre-marked direction of the marker. An improvement of the orientation algorithm with an algorithm for compensation of the gripping error is proposed.
Move to See Better: Towards Self-Supervised Amodal Object Detection.

Humans learn to better understand the world by moving around theirenvironment to get more informative viewpoints of the scene. Most methods for2D visual recognition tasks such as object detection and segmentation treatimages of the same scene as individual samples and do not exploit objectpermanence in multiple views. Generalization to novel scenes and views thusrequires additional training with lots of human annotations. In this paper, wepropose a self-supervised framework to improve an object detector in unseenscenarios by moving an agent around in a 3D environment and aggregatingmulti-view RGB-D information. We unproject confident 2D object detections fromthe pre-trained detector and perform unsupervised 3D segmentation on the pointcloud. The segmented 3D objects are then re-projected to all other views toobtain pseudo-labels for fine-tuning. Experiments on both indoor and outdoordatasets show that (1) our framework performs high-quality 3D segmentation fromraw RGB-D data and a pre-trained 2D detector; (2) fine-tuning withself-supervision improves the 2D detector significantly where an unseen RGBimage is given as input at test time; (3) training a 3D detector withself-supervision outperforms a comparable self-supervised method by a largemargin.
Medical Data Security Using Blockchain and Machine Learning in Cloud Computing

Cloud computing plays a significant role in executing, storing, and managing data as a third-party group. The key concern is data security and privacy with the defined classes of data before actually saving it to cloud storage. Issues related to medical data security and privacy require more studies to overcome the problems related to it. Medical records are maintained in medical organizations and research center’s databases. So due to security and privacy considerations, it is difficult to perform execution on the electronic health records (EHRs). Blockchain technology and the cloud system have independently proved their usability for handling the security issues of EHR. Also, the two technologies, namely, blockchain and machine learning, can be merged to improve analytical operations with security of various applications in healthcare industry. Blockchain is a highly secure and decentralized multi-computer networking platform, called nodes. Although machine learning algorithms perform better even with very less or no feature engineering, high risk factors are associated with EHR tasks. Blockchain also changes traditional healthcare practices to make them more reliable, allowing faster diagnosis and treatment by data exchange in a safe and secure way. In machine learning, expert systems are studied with a long background and performance in many fields. In this paper, the aim is to review various techniques for EHR security and privacy concerns on exchanging cloud medical data. This analysis offers an overview of new healthcare innovations and related applications focusing on blockchain, and highlights the potential of blockchain technology to revolutionize the healthcare sector.
FOUR-DIMENSIONAL ENCODING OF CHARACTER SEQUENCES AND EVALUATION OF THEIR SIMILARITIES AND DIFFERENCES

This paper describes a string encoding algorithm, which produces sparse dis-tributed representations (SDR) of text data. In essence, this is a modified version of a prior algorithm and the modifications have the following benefits: - the ability to decode data, without loss of information; - greatly increased capacity of the encoding space; - the possibility of performing more detailed comparisons of encoded strings. The main disadvantage compared to the prior algorithm is the increased complexity of the procedure for encoded string comparison. This is due to the use of a four-dimensional encoding space, instead of a two-dimensional space.
Static Type Analysis by Abstract Interpretation of Python Programs

Python is an increasingly popular dynamic programming language, particularly used in the scientific community and well-known for its powerful and permissive high-level syntax. Our work aims at detecting statically and automatically type errors. As these type errors are exceptions that can be caught later on, we precisely track all exceptions (raised or caught). We designed a static analysis by abstract interpretation able to infer the possible types of variables, taking into account the full control-flow. It handles both typing paradigms used in Python, nominal and structural, supports Python’s object model, introspection operators allowing dynamic type testing, dynamic attribute addition, as well as exception handling. We present a flow- and context-sensitive analysis with special domains to support containers (such as lists) and infer type equalities (allowing it to express parametric polymorphism). The analysis is soundly derived by abstract interpretation from a concrete semantics of Python developed by Fromherz et al. Our analysis is designed in a modular way as a set of domains abstracting a concrete collecting semantics. It has been implemented into the MOPSA analysis framework, and leverages external type annotations from the Typeshed project to support the vast standard library. We show that it scales to benchmarks a few thousand lines long, and preliminary results show it is able to analyze a small real-life command-line utility called PathPicker. Compared to previous work, it is sound, while it keeps similar efficiency and precision.
RESEARCH ON OPEN DIGITAL INFORMATION SYSTEMS FOR ANALYSIS OF RESEARCH ACTIVITY RESULTS OF HIGHER EDUCATION INSTITUTIONS ACADEMIC STAFF

In modern society there is a growing interest in assessing the quality of research activities of higher education institutions academic staff, in studying the possibilities of open digital information systems, the question regarding the indicators determination of scientific research effectiveness and their optimal representation in the global digital scientific space arises. The importance of analyzing the effectiveness of research results is due to the weight of publication activity in the most authoritative Ukrainian and international rankings. The article presents the results of the analysis of the Ukrainian and international experience in evaluating the research activities of higher education institution academic staff using indicators of international scientometric and bibliometric databases, analyzes the features of the most authoritative international scientometric and bibliometric databases - Scopus, Web of Science, Mendeley, Google Scholar, Research Gate, platforms for public access to research results – Zenodo, platforms for analyzing the prevalence of scientific publications on the Internet – Altmetric, their ability to search for publications and available tools for scientometric analysis of publishing activity of academic staff; the advantages and disadvantages of their use for the evaluation of research activity of higher education institution academic staff are identified. The peculiarities of the formation of research activity indicators of higher education institutions academic staff are determined, in particular from international scientometric and bibliometric databases, for optimal and complete analysis of their effectiveness.
Implementation of the Simple Multi Attribute Rating Technique Method (SMART) in Determining Toddler Growth

Toddler nutritional status is an important factor in efforts to reduce child mortality. The development of community nutrition can be monitored through the results of recording and reporting of community nutrition improvement programs reflected in the results of weighing infants and toddlers every month at the Pos Pelayanan Terpadu (Posyandu/ Integrated Service Post) , where these efforts aim to maintain and improve health and prevent and cope with the emergence of public health problems, especially aimed at toddlers. However, in carrying out the health service activities of Medical Officers, faced with an important problem that is still difficult in providing information related to the results of monitoring the growth and development of infants, because information on growth and development of infants owned is obtained from the data collection done manually such as; make records and calculations to find out the condition of a toddler declared good, less, or bad. Implementation of the SMART method in Toddler's growth and development, this method can be used based on the weights and criteria that have been determined. The criteria used are based on the Anthropometric index assessment criteria. The results of the analysis are the results of ranking the greatest value to be used as the material in the decision-making process.
Tree Rotations for Dependency Trees: Converting the Head-Directionality of Noun Phrases

To overcome the lack of NLP resources for the low-resource languages, we can utilize tools that are already available for other highresource languages and then modify the output to conform to the target language.In this study, we proposed an approach to convert an Indonesian constituency treebank to a dependency treebank by utilizing an English NLP tool (Stanford CoreNLP) to create the initial dependency treebank.Some annotations in this initial treebank did not conform to Indonesian grammar, especially noun phrases' head-directionality.Noun phrases in English usually have head-final direction, while in Indonesian is the opposite, head-initial.We proposed a variant of tree rotations algorithm named headSwap for dependency trees.We used this algorithm to convert the head-directionality for noun phrases that were initially labeled as a compound.Moreover, we also proposed a set of rules to rename the dependency relation labels to conform to the recent guidelines.To evaluate our proposed method, we created a gold standard of 2,846 tokens that were annotated manually.Experiment results showed that our proposed method improved the Unlabeled Attachment Score (UAS) with a margin of 32.5% from 61.6 to 94.1% and the Labeled Attachment Score (LAS) with a margin of 41% from 44.1 to 85.1%.Finally, we created a new Indonesian dependency treebank that converted automatically using our proposed method that consists of 25,416 tokens.The dependency parser model built using this treebank has UAS of 75.90% and LAS of 70.38%.