Reviewerly/exHarmony huggingface dataset:
Cloud Computing Resources: Survey of Advantage, Disadvantages and Pricing

Cloud computing has become widespread in the last decade. Many companies and organizations have moved from using traditional computing to using resources via the cloud. In this paper we will explore the advantages and disadvantages of using cloud computing and the pricing methods and their advantages.
MM-Net: Learning Adaptive Meta-metric for Few-Shot Biometric Recognition

Deep Convolutional Neural Network (DCNN) has pushed forward the development of deep biometric recognition. However, it is resource-consuming to train existing deep networks with plenty of labeled training data in actual scenarios. Therefore, in this paper, we exploit few-shot learning for biometric recognition, which can solve many actual problems with limited training samples, such as video surveillance and suspect detection. Specifically, we propose a novel adaptive meta-metric network (MM-Net) for few-shot biometric recognition. Instead of traditional task-invariant distance metrics, a novel meta-metric learner is proposed to measure the similarities between queries and class embeddings under different tasks adaptively. Moreover, we introduce focal loss to address the class imbalance problem in few-shot biometric recognition, and make the classifiers focus on hard-classified samples. Experimental results on three different biometric datasets demonstrate the superior performance of our MM-Net over other few-shot learning approaches.
IMPLEMENTASI QR CODE VALIDATION PADA SISTEM INFORMASI SURAT PERINTAH PERJALANAN DINAS

Abstract— Quick Response Code (QR Code) is a two-dimensional barcode that is able to provide more information than a one-dimensional barcode. Smart phone technology that develops very fast with advanced camera facilities equipped with scanning and decoding qr code features. The camera reads the QR code in five parts, namely pattern, alignment, timing pattern, quiet zone, and data area. The five sections used the qr code library to translate back in numbers or letters quickly. Compared to one-dimensional barcodes, qr code is more useful because it can store numbers or letters, while one-dimensional barcodes can only store numbers. The advantage of the QR code that can store numbers and letters is the choice of the company in using the QR code as a support system for official travel orders made in a website-based program.
Does Growth Enhancement Support Scheme (GESS) Contribute to Youth Development in Informal Farm Entrepreneurship? Evidence from Rural Communities in Nigeria

Purpose – The purpose of this paper is to critically examine the impact of a growth enhancement support scheme (GESS) on youth development in informal farm entrepreneurship in Nigeria. Its special focus is to investigate the impact of the GESS on rural youths’ adoption of new technologies needed to sustainably increase food security in Nigeria.Design/ methodology/ approach – This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 800 rural youths were sampled across the six geopolitical zones of Nigeria.Findings – The result from the use of a bi-variate probit model indicate that the GESS has a significant impact on rural youths’ innovations in farming.Practical implication – This suggests that information and communication technology (ICT) could provide new opportunities for making farming more interesting and enterprising for rural young people.Social implication – It implies that while old male and female farmers are less likely to adopt the new farming technologies needed to achieve Nigeria’s agricultural transformation agenda (ATA), a younger generation can help introduce new technologies, whilst also learning from traditional methods.Originality/ value – This research adds to the literature on informal farm entrepreneurship and rural communities’ debate in developing countries. It concludes that engaging youths in GESS should form the foundation of the ATA in Nigeria, which, in turn, would offer adequate combination of new and traditional solutions to address the challenges of food insecurity in sub-Saharan Africa.
Load Balancing Using SJF-MMBF and SJF-ELM Approach

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.
Classification Based Hierarchical Clustering Prediction Variability in the Ensembles of Models Using a Statistical Approach

A diversity of model decisions in ensembles is the basis for using group decision-making methods. However, creating a diversity of individual decisions and using them to create an ensemble is not easy. It is also not easy to develop a method of forming a decision based on a diversity of opinions. Often, decisions strongly correlate and the individual opinion of a particular model is little taken into account in the general decision of the ensemble. Similar solutions are often grouped and outweigh the solutions that differ from the group in their total number. In this regard, a hierarchical classification with clustering based on the features of the correlation of decisions is proposed. This allowing to aggregate strongly similar opinions and to a greater extent take into account the alternative opinion, at the next level of the hierarchy. At the same time, conditions are formed for taking into account the maximum diversity of local decisions with a general unbiased assessment of ensemble decisions. Clustering a decision based on the features of correlation allowing to replace a group of similar decisions with a general cluster decision. The hierarchical structure allows dividing decisions into local and global beyond the hierarchy level.
An Efficient Approach for Patterns of Oriented Motion Flow Facial Gesture Classification Using Depth Video

We have a rule that proposes an exceptional segment depiction methodology by a substitution incorporate descriptor, Pattern of Oriented Motion Flow (POMF) from the optical stream data, to perceive the right outward appearance over the live video. The POMF forms absolutely uncommon angular development data and encodes the angular stream data with extended neighborhood surface little models. since it gets the spatial-common variations of face expression improvements in the optical streams and allows the USA to look at each nearby and all-inclusive structures, as it provides its quality in seeing face expression data. The POMF reference diagram is made useful to set up the enunciations model through the covered Markov model (HMM). So for the process of tutoring through HMM, the objective plan an area unit made by periods of codebooks abuse the K-mean agglomeration techniques. The presentation of a foreseen procedure has been evaluated over an RGB and significance camera-based accounts. Examination result shows that the foreseen POMF descriptors is additional intense in extracting facial information and gives prevalent request rates than different existing promising strategies.
Improved divisor arithmetic on generic hyperelliptic curves

The divisor class group of a hyperelliptic curve defined over a finite field is a finite abelian group at the center of a number of important open questions in algebraic geometry, number theory and cryptography. Many of these problems lend themselves to numerical investigation, and as emphasized by Sutherland [14, 13], fast arithmetic in the divisor class group is crucial for their efficiency. Besides, implementations of these fundamental operations are at the core of the algebraic geometry packages of widely-used computer algebra systems such as Magma and Sage.
Toward a geo-ontology model for economic activity zoning

This work is part of a research that is interested in the development of a global geo-ontology model in the field of urban planning which aims to provide a semantic and computer-readable descriptions of georeferenced spatial data in this field. Our final objective is to make it possible to all stakeholders (architects, public decision-makers, urban planners, ...) operating in urban planning from many organizations such as urban agencies, municipalities,. to collaborate together in the development of urban planning documents. In this paper, we will focus on the Economic Activity Zoning concept by presenting a detailed geoontology model of the planning of activities zoning at a national scale. To evaluate our model, we applied it to the town planning document of Ain Chock municipality if Casablanca city.
Digital Identity and the Blockchain: Universal Identity Management and the Concept of the ‘Self-Sovereign’ Individual

While ‘classical’ human identity has kept philosophers busy since millennia, ‘Digital Identity’ seems primarily machine related. Telephone numbers, E-Mail inboxes, or Internet Protocol (IP)-addresses are irrelevant to define us as human beings at first glance. However, with the omnipresence of digital space the digital aspects of identity gain importance. In this submission, we aim to put recent developments in context and provide a categorization to frame the landscape as developments proceed rapidly. First, we present selected philosophical perspectives on identity. Secondly, we explore how the legal landscape is approaching identity from a traditional dogmatic perspective both in national and international law. After blending the insights from those sections together in a third step, we will go on to describe and discuss current developments that are driven by the emergence of new tools such as ‘Distributed Ledger Technology’ and ‘Zero Knowledge Proof’. One of our main findings is that the management of digital identity is transforming from a purpose driven necessity towards a self-standing activity that becomes a resource for many digital applications. In other words, whereas traditionally identity is addressed in a predominantly sectoral fashion whenever necessary, new technologies transform digital identity management into a basic infrastructural service, sometimes even a commodity. This coincides with a trend to take the ‘control’ over identity away from governmental institutions and corporate actors to ‘self-sovereign individuals’, who have now the opportunity to manage their digital self autonomously. To make our conceptual statements more relevant, we present several already existing use cases in the public and private sector. Subsequently, we discuss potential risks that should be mitigated in order to create a desirable relationship between the individual, public institutions, and the private sector in a world where self-sovereign identity management has become the norm. We will illustrate these issues along the discussion around privacy, as well as the development of backup mechanisms for digital identities. Despite the undeniable potential for the management of identity, we suggest that particularly at this point in time there is a clear need to make detailed (non-technological) governance decisions impacting the general design and implementation of self-sovereign identity systems.
An Empirical study of Exploring Relevant Metrics to Assess Software Product Quality

This paper proposes an empirical study of exploring relevant and operable metrics to assess software product quality based on quality characteristics of ISO/IEC 25010. Both data-driven and Goal-Question-Metric approach are applied to excavate as many full-life-cycle metrics as possible. Goal-Question-Metric approach is applied to identify software metrics affecting product quality in a specific domain. Machine learning algorithm that support incremental training is applied to learn the relationship between code metrics and quality characteristic marks from historical data. Thus full-life-cycle software metrics are identified and we build a software quality assessment model based on historical code metric data.Two case studies are conducted based on actual projects from the past 2 years to verify the feasibility of methodology, including an empirical investigation of mostly concerned quality factors in an electronic equipment software institute, a collection of code metrics and quality characteristic marks of 82 aviation embedded software projects, as well as a comparation of the performance of logistic regression, k-Nearest Neighbor and BP neural network algorithm for quality assessment. Those cases are performed with actual data in actual institutes and projects. Additionally, a support tool for project data management and quality assessment is developed.Through feedback from cooperative engineers, we still see room for improvements to fill the gap between methodology and actual software engineering process. Nevertheless, the empirical validation shows the feasibility of the metrics for quality assessment.
Detection of Proximal Femoral Bone Fracture Using Mask R-CNN

Detection of femoral fractures from X-ray images requires multiple scans which can compromise on the procedure being cost effective as well as delay the need for immediate surgery. Bone mass or bone mineral content is currently assessed most commonly via dual-energy X-ray absorptiometry (DEXA). Procedures and cross-sectional techniques like magnetic resonance imaging (MRI) and quantitative computed tomography (qCT) have been proved to provide useful information along with the DEXA scans like the metrics of the bone structure, density, and quality of the bone. However, such procedures are extremely costly and unaffordable for large masses. To make the process cost effective, faster, and efficient, it is proposed to extract a lot of information from the X-ray scans itself as they are cheap and economically affordable. The large population which suffers from osteoporosis is the older generation with an average age greater than 60 years, undergoing and bearing the cost of frequent MRI, qCT, or DEXA scans which is economically difficult for a greater chunk of this population. Hence, it is very necessary to compute and detect these fractures in early stages and from economically viable options like X-rays. Hence, this paper intends to analyze X-ray scans using CNNs and Mask R-CNNs and extract regions of fractures mainly intracapsular and extracapsular which are the major regions of occurrences of proximal femoral fractures.
Computational Linguistic Assessment of Textbooks and Online Texts by Means of Threshold Concepts in Economics

The ongoing digitalization of educational resources and the use of the internet lead to a steady increase of potentially available learning media. However, many of the media which are used for educational purposes have not been designed specifically for teaching and learning. Usually, linguistic criteria of readability and comprehensibility as well as content-related criteria are used independently to assess and compare the quality of educational media. This also holds true for educational media used in economics. This article aims to improve the analysis of textual learning media used in economic education by drawing on threshold concepts. Threshold concepts are key terms in knowledge acquisition within a domain. From a linguistic perspective, however, threshold concepts are instances of specialized vocabularies, exhibiting particular linguistic features. In three kinds of (German) resources, namely in textbooks, in newspapers, and on Wikipedia, we investigate the distributive profiles of 63 threshold concepts identified in economics education (which have been collected from threshold concept research). We looked at the threshold concepts' frequency distribution, their compound distribution, and their network structure within the three kinds of resources. The two main findings of our analysis show that firstly, the three kinds of resources can indeed be distinguished in terms of their threshold concepts' profiles. Secondly, Wikipedia definitely shows stronger associative connections between economic threshold concepts than the other sources. We discuss the findings in relation to adequate media use for teaching and learning—not only in economic education.
Jingyang Jiang &amp; Haitao Liu (eds.), Quantitative analysis of dependency structures (Quantitative Linguistics 72). Berlin: De Gruyter Mouton, 2018. Pp. xii + 368.

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Terrorist Activities in the Dark and the Surface Web

This chapter presents terrorists’ activities both at Surface Web and the Dark Web. Terrorists used to disseminate their propaganda and attract new supporters and recruits through social media, but so far this does not only take part in Surface Web as their online activity is being detected by law enforcement agencies. Recently terrorists turned to the anonymity and secrecy that the Dark Web provides in order to stay undetected from law enforcement agencies. That way, their online activity cannot be controlled by any government, and they can easily and undisturbed plan their actions. In order to communicate, beyond the Dark Web, terrorists use end-to-end encrypted apps such as Telegram and WhatsApp. With these mobile apps, the communication and coordination of their actions are immediate and secret. Lastly, the Dark Web is precious for terrorist groups as all the funding of their operations, and in general significant part of their overall income, comes from Dark Web transactions using cryptocurrencies such as Bitcoin.
System Implementation and Evaluation

This chapter presents a detailed description of the implementation of the proposed access control architecture that is presented in Chap. 3 . This chapter aims to show how the proposed architecture can achieve significant improvement by reducing the number of policies required for specifying access control settings while providing efficient access control in an IoT environment. Standard XACML [1] based policy scheme is used. The proof of concept prototype is implemented using a physical testbed experiment to demonstrate the feasibility of the proposed approach. Note, the system’s operations are tested with both symmetric and asymmetric key cryptography based approaches.
Towards efficient and verified virtual machines for dynamic languages

The prevalence of dynamic languages is not commensurate with the security guarantees provided by their execution mechanisms. Consider, for example, the ubiquitous case of JavaScript: it runs everywhere and its complex just-in-time compilers produce code that is fast and, unfortunately, sometimes incorrect.
What is a Hedge or Safe Haven Asset for Bitcoin Investors?

This paper investigates a hedge and safe haven asset for Bitcoin investors. Bitcoin has been receiving high attention from finance investors because of its high upside return and volatility. The recent finance literature focused upon Bitcoin characteristics as an alternative asset. We take Bitcoin investors’ perspectives and consider how to manage the high volatility of Bitcoin. We employ the definitions of hedge and safe haven based on the finance literature and conduct the respective statistical analyses. Our definition distinguishes a weak and strong hedge (safe haven). Our empirical results show that traditional assets such as global equities and global bonds are weak hedges for Bitcoin. Furthermore, we observe that gold acts as a strong hedge against Bitcoin during an extreme bearish Bitcoin market, although the impact is marginal. There is no strong safe haven asset identified in our data period. Our results imply that the fundamental value of Bitcoin is still unclear, and it is difficult for Bitcoin investors to manage their portfolio risk.
Research on syntactic dependency tree and Ontology constraint in remote Supervising relation extraction

Abstract At present, research on remote-supervised relationship extraction fail to fully consider the position information among words in a sentence, and the extracted relationship may be inconsistent with the actual relationship. Therefore, this paper combines syntactic dependency tree and ontological constraint to carry out remote-supervised relationship extraction. The syntactic dependency tree is used to obtain the position weight of each word in the sentence and the domain ontology constraint is introduced to improve the accuracy of the extraction relationship. Experiments on Freebase+NYT data set show that this model can effecttively reduce the noise interference of wrong labels, and the model improves the accuracy by 2% compared with other reference models, thus better realizing relation extraction and laying a relevant foundation for the construction of high-precision knowledge map.
Surveillance capitalism - a new techno-economic paradigm?

This paper look at surveillance capitalism as described in the book by Shoshana Zubof, and discuss whether surveillance capitalism represents a new stage of capitalist development. This is done by using the theory of techno-economic paradigms as a theoretical framework.
The new approach to IT testing

Traditional IT projects have rolled out newly developed software or systems after iterating manual tests based on the scenarios and cases that are considered sufficient. However, due to the time and budget limitation of IT projects, these traditional tests almost always fail to include all the possible scenarios and cases of the real world. Thus, we cannot eliminate all potential defects before go-live and unexpected failures might occur as a result, which can lead to severe damage to both customers and IT project contractors.
Diagramming Counterarguments: At the Interface Between Discourse Structure and Argumentation Structure

The chapter addresses the diagramming of counterarguments, i.e. of discourse moves that amount to the presentation of reasons against the acceptance of the conclusion of an extant argument. Counterarguments are of interest to those who seek to tie reconstructions and normative conceptions of argumentation to discursive evidence and to the semantics of a variety of concessive and adversative linguistic constructions. The paper reviews a lineage of approaches to the diagramming of counterarguments originating from works in informal logic, A.I. and computational linguistics, with a special attention to Peldszus and Stede (2013). An alternative style of analysis is then proposed, which captures insights from Pragma-Dialectics, relying on confrontation as a primitive instead of attack. The proposed diagramming system, which extends traditional diagrams of the argumentation structure used in Pragma-Dialectics, is more expressive than the reviewed alternative approaches, while remaining suitable for corpus annotation and ensuring a direct representation of the contribution of the semantics of adversative and concessive constructions.