Reviewerly/exHarmony huggingface dataset:
Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of labeled data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the labeled source network, this paper proposes to learn network-invariant and label-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain label-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into label-discriminative subspaces. In the end, a support vector machine model trained on the labeled source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules.
Are Cryptocurrency Markets Running Behind the Fed? A Significant Shift in Crypto Markets Microstructure

In this research we show that 2021 became a year when crypto markets significantly adjusted behavioural patterns, showing an increased institutional influence. We have come to two key conclusions that might indicate significant changes in the cryptocurrency market microstructure.Firstly, in contrast to recent research, we note that BTC/USD was sensitive to major Fed policy announcements in Q2-Q3 2021 similar to main asset classes.Secondly, OTC Liquidity Providers tend to provide twice as narrow spreads in comparison to Centralised Crypto Exchanges during market volatility related to macroeconomic news.
Open Problems Column

I invite any reader who has knowledge of some area to contact me and arrange to write a column about open problems in that area. That area can be (1) broad or narrow or anywhere inbetween, and (2) really important or really unimportant or anywhere inbetween.
Optimized Three-Dimensional Security Framework to Mitigate Risks Arising From BYOD-Enabled Business Environment

Bring your own device (BYOD) has infiltrated the work environment and businesses are enjoying the benefits coupled with the adoption of the trend. At the same time, the adoption of BYOD has introduced a number of security threats that businesses are failing to match. In the pursuit of addressing security threats that are introduced by the adoption of this technology trend, this chapter recommends a three-dimensional (3-D) security framework that can be used to mitigate the risks emanating from a BYOD enabled environment. Data was collected from the employees of two banks in Africa that supported BYOD trend to measure individual and organizational practices. This chapter investigates further on these practices in addressing implications of BYOD. Three categories of security threats are delineated in this chapter. A review of existing security frameworks is presented in this chapter. The chapter concludes by outlining a 3-D security framework as a potential solution to protect BYOD enabled business environment.
Human Skin Detection in Color Images Using Deep Learning

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.
‘Robust chronologies’ or ‘Bayesian illusion’? Some critical remarks on the use of chronological modelling

The explanatory power of Bayesian chronological modelling is often overestimated, leading to an uncritical belief in the reliability of each isolated model without the necessary look at archaeological connections between different models. The methodical pitfalls of this approach, especially in combination with inaccurate use of typochronological methods, are highlighted for Linear Pottery Culture (ger. Linienbandkeramik – LBK) and Middle Neolithic chronological models from Central Europe (Jakucs et al. 2016; Denaire et al. 2017; Bánffy et al. 2018). A more critical approach to Bayesian modelling, considering possible mathematical artefacts and the deficits of the actual calibration curve as well as the inherent imprecision of the used typochronological dates, seems to be required.
Grundfos opens digital showroom in Dubai

Grundfos has officially opened its iFoss Lab in Dubai, a digital showroom and regional hub for cloud enabled water solutions.
New exploration of the mixed teaching mode about student-centered on information technology platform

With the rapid development of information technology, especially the spread of the Internet, the traditional classroom teaching appears many disadvantages, which is not conducive to the development of education in the future. Aiming at the problems of course content, teaching form and course awarding process in traditional teaching, this paper combines with the idea of student-centered, and explores the new method of mixed teaching. Firstly, design of student-centered blended teaching model comes from teaching objectives, teaching content, teaching process, teaching form and so on. Then, this research takes advantage of information technology platform to assist the teaching model. Finally, the teaching about course of logistics Information Technology is taken as an example to verify the effectiveness of the method. The results show that, compared with the traditional method, student-centered blended teaching can improve teaching quality, and further speed up the professional development of higher education modernization.
A Novel Approach for Data Analysis using Explainable AI

Explainable artificial intelligence (XAI) has become popular in the last few years. The Artificial intelligence (AI) community in general and the Machine learning (ML) community in particular is coming to the realization that in many applications, for AI to be trusted, it must not only demonstrate good performance in its decision making, but it also must explain these decisions and convince us that it is making the decisions for the right reasons. This paper presents a novel way of representing statistical features from machine learning datasets to image based representation and exploring Conventional neural networks (CNN’s) based networks for better data interpretation. This technique found to be extremely powerful as compared to ML and found to improve more than 10% accuracy on various statistical datasets available.
The Business, Careers, and Challenges of Security and Loss Prevention

Security is “big business” globally, and there are many career opportunities and challenges in this profession. Risks, threats, and hazards keep security and loss prevention practitioners busy as they protect people, assets, and business operations. Besides providing an overview of the security industry, this chapter offers strategies that can be applied by practitioners in their work. The limitations and budget constraints of government, especially the criminal justice system, require security and loss prevention practitioners to carefully and creatively use the resources available to them as they plan and implement protection strategies.
Cloud-Based Platforms and Infrastructures

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.
Proportionality and Limitations on Freedom of Speech

In this paper, I review the depth of judicial and scholarly consensus respecting the principle of proportionality and offer an expository and critical account of proportionality analysis. I aim to introduce readers to different understandings of proportionality, showing how the judicial and scholarly consensus is only surface deep. Of course, the fact of disagreement between and within jurisdictions need not deny that the principle of proportionality has merit. But it does put in doubt the confident claims by some proponents that proportionality is ‘essential’, ‘indispensable’, and ‘unavoidable’ and that, when it comes to rights, it ‘is all and only about proportionality’. To explore these thoughts, and to demonstrate how much of the US debates from the 1950s and 1960s remain apposite today, I begin by exploring the appeal of proportionality and balancing for the adjudication of freedom of expression claims and then review proportionality’s four evaluations. I conclude by revisiting an idea that animates much proportionality reasoning: that freedom of expression cannot be absolute. It is an idea that is oft-repeated, but one that, I aim to show in brief, is based on a mistaken premise.
The Effect of English for Specific Purposes (ESP) Learning-Language Lab vs. Mobile-Assisted Learning

In recent years, the rapid advancement of information technology has had a great impact on our daily life and changed the world in which we operate; in particular, mobile devices have become more portable and powerful than ever. As a result, mobile-assisted language learning (MALL) and ubiquitous learning have been widely adopted in a variety of studies. However, the purpose of this study was to investigate the effects of teaching English for specific purposes (ESP), i.e., the Business Language Testing Service (BULATS) for college students in a language laboratory and through the mobile LINE app. A total of 72 college students were randomly assigned to two groups, a language lab group and a mobile learning group, for 10 weeks of instruction. The findings revealed that the students in the mobile learning group had significantly positive attitudes toward the teaching method and learning content and high user satisfaction. In addition, the learning performance of the students in the mobile learning group was better than that in the language lab group.
Instant Messaging Tutoring: A Case of South Africa

This research study was focused on determining the role social media, and in particular, instant messaging can have in providing tutor assistance to high school students. The study investigates the implementation of Dr. Maths, an instant messaging tutoring service that ran successfully in South Africa from 2007 to 2013. With the demise of the Dr Maths initiative, there hasn't been any further reconstruction of this sort. The results of this study strongly indicate that learners see value in having a tutor assist them with schoolwork, over the WhatsApp IM platform. This will allow for access to much required educational resources particularly in disadvantaged communities in developing countries such as South Africa. However, it is recommended that any solution implemented be as platform agnostic as possible to avoid a recurrence of the issue faced by Dr. Maths.
Analysis of Digital Media Image Data in "A Place to Nowhere"

With the popularization of digital media technology and equipment, movies are playing an increasingly important role in people's lives. Digital media image processing technology mainly relies on computers for design, and its application has brought a very big change of the movie viewing effect. This article introduces the meaning of emerging technologies of digital media, the practical application of computer image processing, and takes the film "A Place of Nowhere" as an example, and analyze its image processing data to discuss the application value of digital media image data onto movies.
Working shooting scenes

Both authors have the benefit of having worked for law enforcement agencies and as private forensic scientists, in criminal and civil cases, for plaintiffs, defendants, and prosecutors. The observations and opinions in this chapter are largely the results of nearly 80 combined years of the authors’ experiences from across the United States and internationally. The “fresh” crime scene can be an almost surreal place for the modern investigator. For those working in the law enforcement sector, it is commonly known that such a fresh scene is a fluid, seldom stable, or static environment where new information is regularly being inserted into the workings of the investigation. Avenues of investigation originally thought to be valid may be found to be fruitless or moving in the wrong direction. Avenues first thought to be unimportant may become the main crux of the investigation. The adrenaline is pumping, and the excitement of assembling some semblance of order from the scattered pieces of the event can be fascinatingly rewarding and intellectually satisfying. Additionally the mixture of publicity and public speculation with administrative law enforcement pressure can make the entire endeavor a precarious balancing act of science, personalities, and bureaucracy.
Geochemical Aspects of Geothermal Utilization

Exploration to obtain subsurface composition and thus information on temperature, origin and flow direction, and monitoring to obtain information on potential changes are the backbone of geochemical studies during utilization of geothermal resources. The most important problems involve cooling, scaling, corrosion and the possible emission of polluting substances. Geothermometers are used for monitoring temperature, and isotope composition as well as dilution methods involving tracers, to measure flow. The studies involve collection, analysis of samples and the interpretation of results applying speciation and reaction path programs. The formation of scales can be preddicted. Corrosion studies are mostly field studies entailing monitoring the rate of formation on coupons. Solutions to chemical problems may involve changes in conditions of wellhead pressure and flow, the addition of inhibitors, as well as cleaning wells by redrilling. Corrosion studies involve the testing of materials to be used for different parts of the equipment. Studies on deposition and corrosion and potential solutions are described for the Krafla geothermal field in Iceland and the Asal geothermal field, Djibouti. In the former case the potential effects of magmatic events that may lead to problems such as acid fluids are considered and in the latter case several potential inhibitors to certain deposits are described.
Multi-Agent System Approach for Trustworthy Cloud Service Discovery

Accessing the advantages of cloud computing requires that a prospective user has proper access to trustworthy cloud services. It is a strenuous and laborious task to find resources and services in a heterogeneous network such as cloud environment. The cloud computing paradigm being a form of distributed system with a complex collection of computing resources from different domains with different regulatory policies but having a lot of values could enhance the mode of computing. However, a monolithic approach to cloud service discovery cannot help the necessities of cloud environment efficiently. This study put forward a distributive approach for finding sincere cloud services with the use of Multi-Agents System for ensuring intelligent cloud service discovery from trusted providers. Experiments were carried out in the study using CloudAnalyst and the results indicated that extending the frontiers MAS approach into cloud service discovery by way of integrating trust into the process improves the quality of service in respect of response time and scalability. A further comparative analysis of the Multi-Agents System approach for cloud service discovery to monolithic approach showed that Multi-Agents System approach is highly efficient, and highly flexible for trustworthy cloud service discovery.
Partnership-based Collaborative Learning Approach from Decentralized Data

When training models to implement machine learning tasks in the Internet of Things (IoT) environment, large-scale collection of the data stored on decentralized devices is inappropriate in view of the individual privacy, and federated learning as an alternative to centralized machine learning method has been proposed. However, in such a method, individual updates of the participating devices before the global aggregation only adopt biased native information, which may be sensitive to the specific classes that have larger data size. In this paper, considering that the integration of more balanced estimates could be superior to the aggregation of skewed calculations, we propose a novel machine learning approach that enables decentralized IoT devices to cooperatively train a global model based on the direct and indirect interactions among devices while keeping the raw data locally. We term our approach Partnership-based Collaborative Learning Approach (CLAP). The proposed approach consists of two stages, including the top-down stage and the bottom-up stage. In the former stage, to improve the model accuracy of the device itself, each device is allowed to make active choice to find partners, so that it can directly interact with partners to reduce the influence of biased data distributions on individual estimates. In the latter stage, from a global perspective, considering that single devices may only offer partial information about the given task, CLAP tends to aggregate the results of the diverse nodes to provide more extensive features, leaving a more accurate collective estimate. In order to study the effect of direct interactions among partners in a local context, outcomes of the nodes in the simulation are determined by three components: their own estimates, the estimates of partners and the effect degree of the estimates of their respective partners. Experimental results demonstrate that CLAP based on direct and indirect interactions performs better than other distributed learning methods, and two characteristics including diversity and conditional dependence make contribute to the superior performance of CLAP.
The Rhythmotron

This paper describes the Rhythmotron: a percussion-centered robotic orchestrion, commissioned for the CoLABS festival in Sydney in 2017. The authors describe how they electronically reimagined the mechanical components of a cylinder piano by using a variant of the XronoMorph software, and they consider the synergy between algorithmically generated rhythms in a digital environment alongside its analogous mechanical counterpart. The authors detail the idiosyncratic behaviors of linear actuators when used to power drumming robots, and they discuss the aesthetic implications of the Rhythmotron.
Open Access Journal in Bioinformatics

Bioinformatics is rapidly growing, interdisciplinary field of science, where methods from information technology, computer science, mathematics, and statistics are used to solve problems of biological science. To access latest scholarly articles in such an important branch one cannot deny the importance of open access journals. In this chapter an attempt has been made to access the current status of open access journals of bioinformatics which are covered by Directory of Open Access Journals (DOAJ) on various parameters like country and language of publication, their currency, impact factor, article processing charges, copyright licensing model they are using, platform for hosting and their coverage in abstracting/indexing databases.
Two Galois Fields Cryptographic Applications

This last chapter is devoted to illustrating Galois Fields possibilities in cryptography. Thus, as an example, two cryptographic applications of the circuits described in previous chapters are presented.
Introduction

This chapter provides an introductory discussion why the privacy of each living person is so important and discusses the deployment of privacy-preserving deep learning due to its development of artificial intelligence and the performance advantage of their various implementation.
Cloud Computing

Cloud computing was a cloud technology pioneered by Amazon for a long time due to its software technology that is based on the online shopping platform. After Google, Microsoft also follow up, and this technology, in fact, already exists in our lives, and applications continue to expand, become an integral part of life. With the rapid development of the Internet and the demand for high-speed computing of mobile devices, the simplest cloud computing technology has been widely used in online services, such as “search engine, webmail,” and so on. Users can get a lot of information by simply entering a simple instruction. Further cloud computing is not only for data search and analysis function, but also can be used in the biological sciences, such as: analysis of cancer cells, analysis of DNA structure, gene mapping sequencing; in the future more Smart phone, GPS and other mobile devices through the cloud computing to develop more application service.
Language Translation

This chapter introduces the machine translation part. Machine translation (or text-to-text translation) is one component of speech translation and is used for translating automatic speech recognition (ASR) output into text-to-speech (TTS) input. This section will also explain about neural machine translation, which is the most major translation methodology in recent years.
Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning

Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network (HCN), which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis (ACEA). The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification (ARSC). Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.