Reviewerly/exHarmony huggingface dataset:
Commonomics: Rhetoric and Reality of the African Growth Tragedy

This exploratory study addresses the question of a feasible complementary economic model for the teeming population in sub-Saharan Africa (SSA) that lives at the subsistence level. The study rationalized the contention that SSA is in a development straitjacket and its much-touted economic rise is more rhetoric than reality. Postmodern Keynesian economics has failed in most of SSA; therefore, an economic paradigm shift is advocated toward “commonomics.” The study draws upon documentary evidence to posit that SSA’s rise is trumpeted by international organizations, nongovernmental organizations (NGOs), and SSA governments for various “self-serving” reasons adduced in the study. Despite this purported rise, in 2018, 17 of the world’s 20 least competitive economies are in SSA; the middle class is rapidly disappearing; corruption has become a means of livelihood across all socioeconomic classes; and the youth are fleeing their countries, as exemplified by the treacherous transatlantic crossings into Europe. In SSA’s quandary, Africans who are nostalgic about the “good old days” opine that, until the postcolonial era, SSA’s resource base enabled people to meet their basic needs cost-effectively and sustainably and that the consumerism and greed that typify the postmodern era of Keynesian economics were nonissues in traditional SSA contexts. In these societies, commonomics, the term used for the model prescribed in this study, was the economic ideology and model. Commonomics aims to meet people’s basic needs through collaborative grassroots initiatives, where the inputs and outputs of production, distribution, and consumption of goods and services are initiated, governed, managed, and sustained by grassroots citizens. Guidelines for implementing commonomics are outlined in the study.
Towards Optimizing Data Analysis for Multi-dimensional Data Sets

The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used for data analysis. It identifies existing samples in a dataset which are similar to a new sample by using a distance metric. The new sample can then be classified via a class majority voting of its most similar samples, i.e. nearest neighbors. The KNN algorithm can be applied in many fields, such as recommender systems where it can be used to group related products or predict user preferences. In many cases, the performance of the KNN algorithm diminishes as the size of the dataset increases because the number of comparisons performed increases exponentially. In this paper, we propose a KNN optimization algorithm which leverages vector space models to enhance the nearest neighbors search for a new sample. It accomplishes this enhancement by restricting the search area, and therefore reducing the number of comparisons necessary. The experimental results demonstrate significant performance improvements without compromising the algorithm's accuracy.
A Character-Level Deep Lifelong Learning Model for Named Entity Recognition in Vietnamese Text

Lifelong Machine Learning (LML) is a continuous learning process, in which the knowledge learned from previous tasks is accumulated in the knowledge base, then the knowledge will be used to support future learning tasks, for which it may be only a few of samples exists. However, there is a little of studies on LML based on deep neural networks for Named Entity Recognition (NER), especial in Vietnamese. We propose DeepLML-NER model, a lifelong learning model based on using deep learning methods with a CRFs layer, for NER in Vietnamese text. DeepLML-NER includes an algorithm to extract the knowledge of "prefix-features" of Named Entities in previous domains. Then the model uses the knowledge stored in the knowledge base to solve a new NER task. The effect of the model was demonstrated by in-domain and cross-domain experiments, achieving promising results.
Driving forces for information and communication technology innovations in smart health in St. Petersburg

The article analyses institutional environment of the process of introduction of information and communication technologies while developing the smart health concept. The best practices of innovations in healthcare, based on information-communication technologies, are analyzed. To define the driving forces for information and communication technology innovations the expert opinion analysis is held — the article uses results of the survey of the opinion of the heads of public and private medical organizations in St. Petersburg. Recommendations for regulatory authorities and medical organizations concerning enhancement of the process of introduction of innovations are formulated.
Progressively Decomposing Graph Matching

Existing approaches to graph matching mainly include two types, i.e., the Koopmans-Beckmann's QAP formulation (KB-QAP) and Lawler's QAP formulation (L-QAP). The former is advantageous in scalability but disadvantageous in generality, while the latter is exactly the opposite. In this paper, we present a novel graph matching method, called progressively decomposing graph matching (PDGM), which can simultaneously possess the merits of the scalability of KB-QAP and the generality of L-QAP. Our method is motivated by a key observation that, the matching accuracy of KB-QAP can be dramatically boosted by properly introducing a guidance term into the formulation. Based on this observation, the proposed PDGM method progressively incorporates edge affinity information into the optimization procedure of KB-QAP through a guidance term, which mainly involves two iterative steps, i.e., solving the guided KB-QAP and updating the guidance matrix. The extensive experiments on both synthetic data and real image datasets demonstrate that our method can outperform the state-of-the-art in terms of the robustness to noise/deformation and outliers, and the good balance between effectiveness and computational efficiency.
VizRob: Effective Visualizations to Debug Robotic Behaviors

Building and debugging robotic programs is known to be difficult. The robotic community has produced numerous tools, APIs and middlewares to help debug and trace the construction and execution of robotic behaviors. However, most of available debugging tools are text and log-oriented, leading to a tedious and ad-hoc debugging activity. In this paper we fully describe VizRob, a tool to debug robotic behaviors using logs and execution time. VizRob produces interactive visualizations built from log traces within a state machine model, that is, the visual representation of the behavior. VizRob is founded on deficiencies we empirically found from semi-structured interviews and a revision of tutorial materials. A small case study received an initial feedback of VizRob in a robotic software engineering team. Our case study shows: (i) VizRob helps engineers solve intricate debugging scenarios and (ii) engineers perceive VizRob as filling a relevant gap within the current tools for building robotic behavior.
Research on Addressing Method in XML File Based on XPointer

XML is a markup language that describes data and is widely used to exchange data across platforms. Currently, there are three main tools for addressing XML document: XLink, XPath, and XPointer. Among them, XPointer is an advanced addressing tool, which can not only address elements, but also can address specific data such as strings, points and ranges in XML documents. However, for the positioning based on XPointer, how to extract the non-well-formed data content in XML document is still a problem. This paper focuses on the extraction of non-well-formed data content in XML documents. Based on XPath 3.0, extracting and filtering nodes was analyzed. Based on XSLT template, the content of XML documents was selectively output. Finally, a location system based on XPointer was derived, and ultimately achieved advanced addressing for XML documents. 20 XML files were selected as experiment samples to verify the model proposed in the paper. The experimental results demonstrate that the proposed method can locate and represent non-well-formed as well as well-formed data content in XML documents.
A Large-Scale Repository of Deterministic Regular Expression Patterns and Its Applications

Deterministic regular expressions (DREs) have been used in a myriad of areas in data management. However, to the best of our knowledge, presently there has been no large-scale repository of DREs in the literature. In this paper, based on a large corpus of data that we harvested from the Web, we build a large-scale repository of DREs by first collecting a repository after analyzing determinism of the real data; and then further processing the data by using normalized DREs to construct a compact repository of DREs, called DRE pattern set. At last we use our DRE patterns as benchmark datasets in several algorithms that have lacked experiments on real DRE data before. Experimental results demonstrate the usefulness of the repository.
Cross-Language Information Matching Technology Based on Term Extraction

Based on the analysis and summary of current achievements on terms extraction, this paper puts forward a method of term extraction based on multi feature fusion. This method firstly uses term word formation rules to filter words depending on the result of preprocessing, and then determines the boundaries of words according to information entropy. The paper introduces the concept of word co-occurrence according to the features of field terms, and uses bag of words model to align terms in both English and Chinese, and obtains the corresponding translation terms in one field. Finally, it establishes the link between Chinese and English by using the term alignment results in both English and Chinese retrieval model. This paper carries out repeated experiments on sets of policy documents. The experiments test and verify the availability of the method by the analysis and comparison between the method introduced in this paper and the traditional methods.
Virtualization in Cloud Environment: Bandwidth Management

Cloud computing recently emerged as an interesting model that enables computing and other related internet activities to take place anywhere, anytime.Cloud service providers centralize all servers, networks, and applications to allow their users' access at any time and from any location.Cloud computing uses already existing resources like server, CPU and storage memory but runs on a new technology known as virtualization.The core idea of virtualization is to create several virtual versions of one single computing device or resource.This enables many user operating systems to work on such a single underlying piece of device.Network bandwidth is one of the critical resources in a cloud environment.Bandwidth management involves the use of techniques, technologies, tools, and policies to help avoid network congestion and ensure optimal use of the subscribed bandwidth resources while also being a bedrock of any subscription-based access network.Bandwidth management is being utilized by organizations to allow them to efficiently utilize their subscribed bandwidth resources.Bandwidth management deals with the measurement and control of packets or traffic on a network link in order to refrain from overburdening or overloading the link which can lead to poor performance and network congestion.In this paper, the highest development with respect to virtualization in cloud computing is presented.This study review papers available on cloud computing and relevant published literature in multiple areas like conferences, journals etc.This paper examined present mechanisms that enable cloud service providers to distribute bandwidth more effectively.This paper is therefore a study of virtualization in cloud computing, and the identification of bandwidth management mechanisms in the cloud environment.This will benefit forthcoming cloud providers and even cloud users..
Efficient Attribute-Based Signatures for Unbounded Arithmetic Branching Programs

This paper presents the first attribute-based signature (ABS) scheme in which the correspondence between signers and signatures is captured in an arithmetic model of computation. Specifically, we design a fully secure, i.e., adaptively unforgeable and perfectly signer-private ABS scheme for signing policies realizable by arithmetic branching programs (ABP), which are a quite expressive model of arithmetic computations. On a more positive note, the proposed scheme places no bound on the size and input length of the supported signing policy ABP's, and at the same time, supports the use of an input attribute for an arbitrary number of times inside a signing policy ABP, i.e., the so called unbounded multi-use of attributes. The size of our public parameters is constant with respect to the sizes of the signing attribute vectors and signing policies available in the system. The construction is built in (asymmetric) bilinear groups of prime order, and its unforgeability is derived in the standard model under (asymmetric version of) the well-studied decisional linear (DLIN) assumption coupled with the existence of standard collision resistant hash functions. Due to the use of the arithmetic model as opposed to the boolean one, our ABS scheme not only excels significantly over the existing state-of-the-art constructions in terms of concrete efficiency, but also achieves improved applicability in various practical scenarios. Our principal technical contributions are (a) extending the techniques of Okamoto and Takashima [PKC 2011, PKC 2013], which were originally developed in the context of boolean span programs, to the arithmetic setting; and (b) innovating new ideas to allow unbounded multi-use of attributes inside ABP's, which themselves are of unbounded size and input length.
Design of a Computer-Aided Learning Platform Based on the Intercultural Communication Teaching

After years of the teaching practice and discussion, the intercultural communication still has the potential to lead to the lack of the essential core contents and key directions of the curriculum teaching due to the wide scope of the curriculum itself. If there is no clear understanding of this course, it will easily lead to a general discussion of the cultural knowledge in class, and the lack of the effective improvement of the intercultural communicative competence. It is necessary to avoid the misunderstanding of the current intercultural communicative teaching in terms of the teaching objectives, teaching contents, teaching focus and teaching means, so as to cultivate the students’ intercultural communicative competence, as well as the practical talents who can correctly deal with the different intercultural communication activities.
Example of Application of n-grams: Authorship Attribution Using Syllables

As we described in the previous chapters, mainstream of the modern computational linguistics is based on application of machine learning methods. We represent our task as a classification task, represent our objects formally using features and their values (constructing vector space model), and then apply well-known classification algorithms. In this pipeline, the crucial question is how to select the features. For example, we can use as features words or n-grams of words (sequences of words) or sequences of characters (character n-grams), etc. An interesting question arises: Can we use syllables as features? It is very rarely done in computational linguistics, but there is certain linguistic reality behind syllables. This chapter explores this possibility for the authorship attribution task; it follows our research paper [99]. Note that syllables are somewhat similar to character n-grams in the sense that they are composed of several characters (being not too long).
A Practical Collision-Based Power Analysis on RSA Prime Generation and Its Countermeasure

We analyze the security of RSA prime generation implemented on embedded devices by a practical power analysis attack. Unlike previous differential power analysis-based attack on primality tests of RSA prime generation exploiting the deterministic relationship among multiple prime candidates manipulated by consecutive primality tests, we propose a collision-based power analysis attack on the Miller-Rabin test for a single prime candidate which can recover the secret prime with a single attempt by exploiting collision characteristics of simple power analysis resistant modular exponentiation algorithms. Hence, our attack does not require the incremental prime search assumption and is applicable when countermeasures against previous attacks are deployed since it also does not require the assumption of trial divisions with small primes on prime candidates. For a realistic setting, where five 512-bit modular exponentiations are operated on an ARM Cortex-M4 microcontroller as recommended by FIPS 186-4 standard, we successfully recover the secret exponent to an extent that a feasible exhaustive search is needed for the full recovery of the secret prime. This is a first practical result of recovering a full secret of modular exponentiation which manipulates 512-bit RSA primitives with collision-based power analysis in a single attempt, where the previous attack demonstrates the result for 192-bit ECC primitive implementations. We also present a countermeasure against our attack which requires only one additional modular subtraction for the loop of square-and-multiply-always exponentiation algorithm. An experimental result for the effectiveness of our proposed countermeasure is presented.
A Multi-level Data Sensitivity Model for Mobile Health Data Collection Systems

Mobile Health Data Collection Systems (MHDCS) use a combination of encryption and user access control to grant or deny permissions to data collectors. However, the data in MHDCS is of diverse value and types which calls for different security measures. The level of sensitivity of data in electronic health is a function of the context characterised by the social environment, patient and content among others. When mobile devices are used for data collection and tracking participants, there is need for a more refined security system that allows finer controlled access to specific data elements. In this paper, we provide a conceptual design and prototype implementation for a data sensitivity model that enables attribute-based data access control, based on the level of sensitivity of the data involved. By allowing specific form data elements to have different security levels, we enhance the security of MHDCS and allow more use cases including the use of a single form to collect data for different stakeholders with diverse data needs and concerns.
A Generic Framework for Cross Domain Recommendation

Cross-domain recommender systems research focusesKhan, Muhammad Murad on recommendation improvementIbrahim, Roliana in the target domain based on knowledge gathered from the source domain. Recently, a number of review studies were published, however, they did not provide their reader with a step-by-step guide to conduct cross-domain recommender systems research. In this paper, we present a generic cross domain recommender systems framework which identified different phases and components required to execute a cross-domain recommendation research. The proposed framework is based on analysis of primary studies collected from recently published high impact review studies. It was found that all of the collected primary studies included identified phases, however, each component had multiple options (approaches) which can be applied based on scenarios. The proposed framework can be used for analysis and comparison of existing primary studies, and also explains why cross domain recommender systems approaches are evaluated the same as conventional recommender system approaches. Finally, conclusion and future direction are presented.
Stream-Based Perception for Agents on Mobile Devices

Multi-Agent Systems (MAS) lack advanced concepts for data stream processing, which inhibits their effective use in mobile ecosystems, where built-in smartphone sensors can provide valuable data about the current physical environment of the mobile user. With beliefs, plans, and goals, cognitive agent frameworks provide useful abstractions for the development of complex systems but do not contain effective mechanisms to sufficiently bridge the abstraction gap that exists between low-level streaming data and high-level percepts. The main contribution of this paper is an enhanced perception approach, which integrates two new abstractions, namely expectations and interpretations, into the commonly used perceive-deliberate-act cycle. Expectations and interpretations address the challenges of sensor data and provide higher-level knowledge to the agent’s deliberation, which allows mobile agents to make situation-aware decisions in dynamically changing environments.
Humanists’ Virtual Knowledge Space: Model and Usage

The purpose of this chapter is to present a conceptual model of a virtual knowledge space for use by digital humanists. Following the basic knowledge management strategies, the proposed model supports codification and personalisation, exploitation and exploration. In particular, it incorporates the ability to capture and organise explicit knowledge repositories, tools for knowledge discovery and visualisation, support for tacit knowledge sharing and collaboration with other researchers, as well as for new knowledge creation. The proposed virtual knowledge space is intended to help users to make humanistic knowledge in digital media more manageable and usable. As an illustration, the chapter presents two cases showing different ways humanities scholars may interact with their data in the context of the Republic of Letters. The first case describes a creative process of visual exploration by association, while the second case focuses on deeper methods of analysis and visualisation. Overall, the chapter suggests that the proposed virtual knowledge space may serve as reference for implementing various digital humanities projects relevant to a wide range of humanities disciplines.
An Interpretable Neural Model with Interactive Stepwise Influence

Deep neural networks have achieved promising prediction performance, but are often criticized for the lack of interpretability, which is essential in many real-world applications such as health informatics and political science. Meanwhile, it has been observed that many shallow models, such as linear models or tree-based models, are fairly interpretable though not accurate enough. Motivated by these observations, in this paper, we investigate how to fully take advantage of the interpretability of shallow models in neural networks. To this end, we propose a novel interpretable neural model with Interactive Stepwise Influence (ISI) framework. Specifically, in each iteration of the learning process, ISI interactively trains a shallow model with soft labels computed from a neural network, and the learned shallow model is then used to influence the neural network to gain interpretability. Thus ISI could achieve interpretability in three aspects: importance of features, impact of feature value changes, and adaptability of feature weights in the neural network learning process. Experiments on both synthetic and two real-world datasets demonstrate that ISI could generate reliable interpretation with respect to the three aspects, as well as preserve prediction accuracy by comparing with other state-of-the-art methods.
On the Distortion of Locality Sensitive Hashing

Given a notion of pairwise similarity between objects, locality sensitive hashing (LSH) aims to construct a hash function family over the universe of objects such that the probability two objects hash to the same value is their similarity.LSH is a powerful algorithmic tool for large scale applications and much work has been done to understand LSHable similarities, i.e., similarities that admit an LSH.In this paper we focus on similarities that are provably non-LSHable and propose a notion of distortion to capture the approximation of such a similarity by an LSHable similarity.We consider several well-known non-LSHable similarities and show tight upper and lower bounds on their distortion.