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Intel SGX has started to be widely adopted. Cloud providers (Microsoft Azure, IBM Cloud, Alibaba Cloud) are offering new solutions, implementing data-in-use protection via SGX. A major challenge faced by both academia and industry is providing transparent SGX support to legacy applications. The approach with the highest consensus is linking the target software with SGX-extended libc libraries. Unfortunately, the increased security entails a dramatic performance penalty, which is mainly due to the intrinsic overhead of context switches, and the limited size of protected memory. Performance optimization is non-trivial since it depends on key parameters whose manual tuning is a very long process. We present the architecture of an automated tool, called SGXTuner, which is able to find the best setting of SGX-extended libc library parameters, by iteratively adjusting such parameters based on continuous monitoring of performance data. The tool is to a large extent algorithm agnostic. We decided to base the current implementation on a particular type of stochastic optimization algorithm, specifically Simulated Annealing. A massive experimental campaign was conducted on a relevant case study. Three client-server applications Memcached, Redis, and Apache were compiled with SCONE's sgx-musl and tuned for best performance. Results demonstrate the effectiveness of SGXTuner.

This paper presents a multi-metric and self-adaptive root cause diagnosis framework, named MS-Rank. MS-Rank decomposes the task into four phases: impact graph construction, random walk diagnosis, result precision evaluation, metrics weight update. Initially, we introduce the concept of implicit metrics and propose a composite impact graph construction algorithm, using multiple types of metrics to discover causal relationships between services. Afterwards, we propose a diagnostic algorithm in which forward, selfward and backward transitions are designed to heuristically identify the root cause services. In addition, we establish a self-adaptive mechanism to update the confidence of different metrics dynamically according to their diagnostic precision. Lastly, we develop a prototype system and integrate MS-Rank into real production system - IBM Cloud. Experimental results show that MS-Rank has a high diagnostic precision and its performance outperforms several selected benchmarks. Through multiple rounds of diagnosis, MS-Rank can optimize itself effectively. MS-Rank can be rapidly deployed in various microservice-based systems and applications, requiring no predefined knowledge. MS-Rank also allows us to introduce expert experiences into its framework to improve the diagnostic efficiency and precision.

Nyong Group is one of the Micro, Small and Medium Enterprises (MSME) located in Sleman, Yogyakarta. Nyong group has several business units, namely nyong shoes and care Yogya, shoes and care Cilacap, nyong donuts, and nyong stalls (food, snacks and chili sauce). The problems by IbM partners include; First, (1) There is no Point of Sales application that uses inventory to meet the needs of the production process or is sold again, (2) Not yet has this Inventory covering raw materials, in-process goods, and goods so (finished goods). The output targets in this program are (1) the application of point of sales applications for recording sales transactions, (2) Adding an inventory application to the created point of sales applications. The service program is carried out in 2 (two) forms; First, (1) design of a point of sales application for Nyong Group's sales transactions. Second, making an inventory application to be able to determine whether to record goods based on arrival time or the amount of stock you can determine to record goods based on arrival time or stock amount. The implementation of this program is designed in four stages of activity, namely: (1) Coordination and preparation stage (2) Design stage (3) Science and technology implementation stage (4) Evaluation and refinement stage.

Artificial Intelligence (AI) is an increasingly popular field that seeks to create machines that can perform tasks that would normally require human intelligence. AI systems are known for their ability to learn and make decisions using data and other inputs, and their capabilities have expanded rapidly in recent years. Some of the capabilities of AI include computer vision, natural language processing, predictive modeling, and decision-making. These capabilities have led to the development of applications such as self-driving cars, virtual assistants, and fraud detection systems. However, the rapid advancement of AI has also raised ethical and social concerns, including issues around job displacement, privacy, and bias. This paper provides an overview of the capabilities of AI and highlights the need for responsible and ethical development and implementation of this technology.

The objective of the research is to review the ability of online machine translator tools includes Google Translate (GT), Collin Translator (CT), Bing Translator (BT), Yandex Translator (YT), Systran Translate (ST), and IBM Translator (IT). This research applies descriptive qualitative. The documentation was used in this study. The result of the analysis shows that the translation results are different, both from the style of language and the choice of words used by each machine translation tool. Thus, directly or indirectly, whether consciously or not, each translation machine carries its characteristics. Machine translation technology cannot be separated from the active role of humans. In other words, it will always be the best choice for users to rely on expert translation rather than machine translation. But no machine translator can be as accurate as human skills in producing translation products. In particular, the field of translation is also concerned with machine translation to support the performance of translators in analyzing the diction used as an element of language. In this regard, it needs to be underlined that the existence of machine translation is an additional facility in the world of translation, not as the main means of translation because the sophistication of the machine will not be able to match the flexibility of the human brain's cognitive abilities in adjusting the translation results according to the existing context. Accurate translation is sometimes subjective, relatively often temporal. Therefore, it is permissible for translating by more than one machine translator

A sealed-bid auction is an essential tool for trading commodities in modern society. However, the non-winning bidders in previously proposed sealed-bid auction schemes lack privacy protection. In this paper, we define a sealed-bid auction with strong privacy protection, which can protect the privacy of all non-winning bidders. In order to fulfil the task, we first present a quantum secret sharing protocol and a secure quantum summation protocol and then design their corresponding quantum circuits, where the security of the latter is guaranteed by the former, while the former is information-theoretically secure. We further present a quantum seal-bid auction (QSA) scheme with strong privacy protection based on the designed quantum protocols. In our QSA scheme, each bidder first encodes a private vector by his private bid and public agreement information, and then all bidders jointly compute the summations of their respective private vectors without a trusted third party. Finally, we verify the correctness and the feasibility of the proposed QSA scheme by circuit simulations in IBM Qiskit.

The world of information technology is buzzing around the possibilities that quantum computers might open up for organizations. But what are quantum computers and what could be their business potential, eventually? Part of why quantum computers are steeped in mystery is because they rely on some very strange properties of the microcosm. From quantum interference to quantum entanglement, not to mention some of the underlying philosophical quantum implications like the possibilities for multiple universes and the foundations of quantum physics that revolve around peculiar mathematical formulations, quantum computers have been under development for decades, with IBM, D-Wave, and Google being amongst a handful of companies investing in this space.

In connection with the need for making instructional media applications by teachers in the distance learning process, PT. Bangun Satya Wacana (BSW) through the Diginusa department held training on the use of the Scratch visual programming application to create learning materials such as games, animations and interactive quizzes. Scratch is a graphical programming language using drag and drop command blocks. Besides that, Scratch can be used both online and offline, so it is very helpful for the distance learning process (DLP). This research aims to see how the level of acceptance of teachers to Scratch using the Technology Acceptance Model (TAM) method. The indicators measured are Self-Efficacy, Complexity, Perceived Usefulness, Perceived Ease of Use, Intention to Use and Actual Technology Use, then analyzed by linear regression method with the help of IBM SPSS software. From the results of the analysis carried out, it can be seen that the Intention to Use affects the Actual Technology Use. Perceived Usefulness and Perceived Ease of Use together affect the Intention to Use. Perceived Ease of Use, Self-Efficacy and Complexity together have a significant effect on Perceived Usefulness.

At present-day, computers are so communal in pharmaceutical research and development that it may be rough to visualize a time when there were no computers to help the medicinal chemist or biologist. Nowadays, computers are completely essential for generating, managing, and transmitting information. The primary computers were designed for military and accounting applications, but gradually it became apparent that computers would have a massive number of uses. At modern, computers are so common in pharmaceutical research and development. The purpose of this review is to give a brief description of the historical development in respect applications of computers in Research and Development.

Editor’s note: An open-source DFT flow is essential for any open-source solution. This article describes an approach to fill in this missing piece. — Sherief Reda, Brown University — Leon Stock, IBM — Pierre-Emmanuel Gaillardon, University of Utah

Editor’s note: This article proposes a new model testing and verification methodology, PyH2, using property-based random testing in Python. PyH2 leverages the whole Python ecosystem to build test benches and models. — Sherief Reda, Brown University — Leon Stock, IBM — Pierre-Emmanuel Gaillardon, University of Utah

Computer scientists and engineers have started down a road that could one day lead to a momentous transition: from deterministic computing systems, based on classical physics, to quantum computing systems, which exploit the weird and wacky probabilistic rules of quantum physics. Many commentators have pointed out that if engineers are able to fashion practical quantum computers, there will be a tectonic shift in the sort of computations that become possible ¶ But that's a big if. ¶ Quantum computers hold great theoretical promise, sure, but the hurdles that need to be overcome to build practical machines are enormous. Some skeptics have argued that the technical challenges are so immense that it's very unlikely that general-purpose quantum computers will become available anytime in the foreseeable future. Others, including the engineers now working very hard to build these machines at Google, IBM, Intel, and elsewhere, are more sanguine, anticipating that 5 or 10 more years of work may be enough to bring the first practical general-purpose quantum computers on line. Only time will tell. ≫

Modern analytical tasks in biomedical research require increasingly sophisticated methods of data analysis. In recent years, the term data analysis is not only related to classical statistical tests for hypothesis testing and correlation analysis for studying associations between variables. Classification tree or decision tree analysis is getting more and more frequently used in biomedical research. In this paper we present the use of classification trees in biomedical research and provide examples of their construction in the most commonly used statistical programs. The article is constructed as a problem solving exercise using classification trees with an example of a data set for creation of classification trees and description of how to build a classification tree model in IBM SPSS Statistics and StatSoft Statistica software. Moreover, we provide recommendations on how the results of this analysis should be presented in a scientific article. The use of the classification trees has a potential to contribute to better understanding of the factors behind the observed phenomena in medicine and biology.

In this interview, Bushra Anjum, senior editor Ubiquity, sits down with Xiaokui Shu, a Research Staff Member at IBM, and they chat about the latest emerging issues in cybersecurity. Is it possible to discover modern cyber threats before attackers accomplish their goals? The discussion then turns to the new era of dynamic cyber defense, which consists of detection strategies and procedures developed on the fly by security analysts based on live observations.

The last few years have seen a large number of initiatives on artificial intelligence (AI) ethics: intergovernmental-institution initiatives such as “Ethics Guidelines for Trustworthy AI” from the high-level expert group on AI of the European Commission [1] or the Organisation for Economic Cooperation and Development (OECD) Council Recommendation on Artificial Intelligence [2] , government initiatives such as that of the U.K. Parliament Select Committee on Artificial Intelligence [3] , industry initiatives on AI ethical codes such as those of Google, IBM, Microsoft, and Intel, academic initiatives such as the Montreal declaration for the responsible development of AI [4] , the Stanford University 100 Year Study on AI [5] or the Alan Turing Institute’s “Understanding Artificial Intelligence Ethics and Safety” [6] , and finally professional body initiatives such as the IEEE Global Initiative on Ethics of Autonomous/Intelligent Systems (A/IS) [7] . These initiatives, while acknowledging the potential of A/IS technologies to contribute to global socioeconomic solutions, highlight the increasing challenges posed by these technologies in the ethical, moral, legal, humanitarian, and sociopolitical domains.

IBM has deployed its Predictive Analytics for Server Incident Reduction (PASIR) solution to more than 360 information technology (IT) environments worldwide since 2013. These environments, covering sectors from banking to travel to e-commerce, are serviced by IBM support groups. Incidents occurring on servers, including problem descriptions and resolutions, are documented in client account-specific ticket management systems. PASIR uses machine learning to classify the incident tickets within an IT environment and identify high-impact incidents that involve server outages by using the respective ticket descriptions and resolutions. It then correlates these high-impact tickets with server properties and utilization measurements to identify problematic server configurations. Finally, for such configurations, PASIR uses statistical multivariate analysis and simulation methods to prescribe improvement and modernization actions. In this paper, we present the results achieved from deploying this solution. We describe the PASIR approach, from ticket classification to the recommendations of remediation actions (e.g., hardware and software upgrades). We demonstrate the model’s effectiveness by comparing predictions on the impact of prescriptive actions with actual system improvements. Since 2013, we have applied PASIR to more than 840,000 client servers, resulting in more precise upgrade spending and environmental stability, thus saving our clients an estimated $7 billion.

Today, one of the most discussed topics in the field of information technology is distributed registry systems. They attract investors and developers with their functionality. Distributed ledger systems are being introduced into business processes in many areas of human activity, which makes their contribution to development irreplaceable. One of the most vulnerable parts of such systems is the process of managing cryptographic keys, an attack on which can destroy the entire security of the distributed registry system. The aim of the research is to identify possible threats to the process of managing cryptographic keys, on the basis of which recommendations and standards for managing cryptographic keys in distributed ledger systems will be developed. Research methods: to achieve this goal, the structure of the life cycle of cryptographic keys was considered, an analysis of possible vulnerabilities in the process of managing cryptographic keys at each stage of the life cycle of a cryptographic key was carried out. In addition, the distributed ledger system was analyzed in the context of the identified vulnerabilities of the key management process using the example of the IBM blockchain and the possibility of outsourcing cryptographic key management systems was considered. Result: a set of possible threats to the process of managing cryptographic keys was proposed, the necessity of assessing the security of the key management system before deciding on the introduction of these systems into distributed registries was proposed, conclusions were drawn about the need to develop recommendations and standards for the process of managing cryptographic keys for such systems, as well as the possibility applicability of the recommendations for assessing the security of the implementation of outsourcing of cryptographic key management systems in distributed ledgers.

Serverless computing through Function-as-a-Service (FaaS) platforms is a popular new cloud computing model, offering high elasticity while being fully-managed. However, FaaS platforms do not quantify elasticity; thus, application developers are unaware of how elastic FaaS platforms are. This paper is an extended version of [18]. The original paper proposes an experiment design and corresponding toolkit for observing elasticity and its associated trade-offs with latency, reliability, and execution costs. Results for evaluating four popular FaaS platforms by AWS, Google, IBM, and Microsoft indicate significant short-comings in terms of elasticity for all but AWS's service offer. One year later, we reproduced all experiments and revisited our discussions. We report significant improvements by Google and IBM. Our results indicate that (currently) all but Microsoft's FaaS platform are elastic with differences in latency and execution costs.

A review of modern data storage architectures was conducted, the advantages and disadvantages of each of them were given. The data storage systems of the IBM FlashSystem family were analyzed, as well as Spectrum Virtualize software, which is responsible for virtualization, compression, distribution and replication of data stored on the storage system. A mathematical model of the data storage system of IBM Storwize v5030E was developed. Well-known metrics are used to evaluate its performance when using spindle and solid-state drives. The effect of hardware and software data compression on system performance has been experimentally revealed. Recommendations are formulated by which it is possible to determine which media and which technology stack should be used by a business user to complete the tasks assigned to him. Problems in programming 2020; 2-3: 82-93

IntroductionDeep learning (DL) has emerged as a powerful tool to solve a variety of complex problems that have been difficult to solve with traditional methods. However, domain experts attempting to apply DL methodology have to learn to code in order to use it. Numerous frameworks have been developed, such as TensorFlow and PyTorch, that simplify the task of building and training complex DL models, yet their efficient use requires a good working knowledge of Python language. Consequently, a variety of tools have been developed that provide easier to use DL models, ranging from the Keras API built on top of TensorFlow that still requires coding, to tools such as H2O that provide a point-and-click web-based interface to configure and train pre-built models. Among this new breed of tools, IBM Visual Insights (formerly IBM PowerAI Vision) \cite{120} and Google's AutoML Vision \cite{AutoML.Vision} have taken this concept further by providing a web-based graphical user interface (GUI) for configuring and training a variety of models, as well as tools and APIs for deploying these models on a variety of platforms. Both IBM Visual Insights and Google AutoML Vision implement complex workflows that connect together many services and computational resources to deliver complex functionality that until recently required a substantial coding effort. The functionality of these tools is still limited to just a few pre-arranged models that work well only for specific problems. The users are also restricted to tweak only some model parameters while leaving majority of the decisions to the computer. Yet these tools democratize access to complex DL models and empower domain scientists to take advantage of this new methodology. In this short article, we walk through an example of using IBM Visual Insights to train a DL model on a chest X-ray image dataset. Google's AutoML Vision's applicability to medical problems has been discussed in \cite{FAES2019e232}.

Purpose Job involvement can be linked with important work outcomes. One way for organizations to increase job involvement is to use machine learning technology to predict employees’ job involvement, so that their leaders of human resource (HR) management can take proactive measures or plan succession for preservation. This paper aims to develop a reliable job involvement prediction model using machine learning technique. Design/methodology/approach This study used the data set, which is available at International Business Machines (IBM) Watson Analytics in IBM community and applied a generalized linear model (GLM) including linear regression and binomial classification. This study essentially had two primary approaches. First, this paper intends to understand the role of variables in job involvement prediction modeling better. Second, the study seeks to evaluate the predictive performance of GLM including linear regression and binomial classification. Findings In these results, first, employees’ job involvement with a lot of individual factors can be predicted. Second, for each model, this model showed the outstanding predictive performance. Practical implications The pre-access and modeling methodology used in this paper can be viewed as a roadmap for the reader to follow the steps taken in this study and to apply procedures to identify the causes of many other HR management problems. Originality/value This paper is the first one to attempt to come up with the best-performing model for predicting job involvement based on a limited set of features including employees’ demographics using machine learning technique.

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors – including NVIDIA, Intel, AMD, and IBM – have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling – sometimes encouraged by restricted GPU memory – NVLink is less important.

This article is a summary of a three-hour discussion at Stanford University in September 2019 among the authors. It has been written with combined experiences at and with organizations such as Zilog, Altera, Xilinx, Achronix, Intel, IBM, Stanford, MIT, Berkeley, University of Wisconsin, the Technion, Fairchild, Bell Labs, Bigstream, Google, DIGITAL (DEC), SUN, Nokia, SRI, Hitachi, Silicom, Maxeler Technologies, VMware, Xerox PARC, Cisco, and many others. These organizations are not responsible for the content, but may have inspired the authors in some ways, to arrive at the colorful ride through FPGA space described above.

Pandemics that hit the world including Indonesia had an impact on all sectors, including the education sector. This has an impact on the introduction of distance learning with an online learning system. Because of this distance system the lecturer is required to be able to convey the course material well to students. This study aims to determine whether online learning carried out by lecturers at INSTIKA and UPN East Java can increase students' interest in studying the courses they take. The sample used in this study were 278 students. The analytical tool used was IBM SPSS Version 21. The results showed that the attitude of internet use, perception, experience, financial condition significantly influenced students' interest in learning. While the ability to learn independently does not have a significant effect on student interest in learning during online lectures.

The Evolution of APL , the HOPL I paper by Falkoff and Iverson on APL, recounted the fundamental design principles which shaped the implementation of the APL language in 1966, and the early uses and other influences which shaped its first decade of enhancements. In the 40 years that have elapsed since HOPL I, several dozen APL implementations have come and gone. In the first decade or two, interpreters were typically born and buried along with the hardware or operating system that they were created for. More recently, the use of C as an implementation language provided APL interpreters with greater longevity and portability. APL started its life on IBM mainframes which were time-shared by multiple users. As the demand for computing resources grew and costs dropped, APL first moved in-house to mainframes, then to mini - and micro -computers. Today, APL runs on PCs and tablets, Apples and Raspberry Pis, smartphones and watches. The operating systems, and the software application platforms that APL runs on, have evolved beyond recognition. Tools like database systems have taken over many of the tasks that were initially implemented in APL or provided by the APL system, and new capabilities like parallel hardware have also changed the focus of design and implementation efforts through the years. The first wave of significant language enhancements occurred shortly after HOPL I, resulting in so-called second-generation APL systems. The most important feature of the second generation is the addition of general arrays—in which any item of an array can be another array—and a number of new functions and operators aligned with, if not always motivated by, the new data structures. The majority of implementations followed IBM’s path with APL2 “floating” arrays; others aligned themselves with SHARP APL and “grounded” arrays. While the APL2 style of APL interpreters came to dominate the mainstream of the APL community, two new cousins of APL descended from the SHARP APL family tree: J (created by Iverson and Hui) and k (created by Arthur Whitney). We attempt to follow a reasonable number of threads through the last 40 years, to identify the most important factors that have shaped the evolution of APL. We will discuss the details of what we believe are the most significant language features that made it through the occasionally unnatural selection imposed by the loss of habitats that disappeared with hardware, software platforms, and business models. The history of APL now spans six decades. It is still the case, as Falkoff and Iverson remarked at the end of the HOPL I paper, that: Although this is not the place to discuss the future, it should be remarked that the evolution of APL is far from finished.

Labeling datasets is one of the most expensive bottlenecks in data preprocessing tasks in machine learning. Therefore, organizations, in many domains, are applying weak supervision to produce noisy labels. However, since weak supervision relies on cheaper sources, the quality of the generated labels is problematic. Therefore, in this article, we present Asterisk , an end-to-end framework to generate high-quality, large-scale labeled datasets. The system, first, automatically generates heuristics to assign initial labels. Then, the framework applies a novel data-driven active learning process to enhance the labeling quality. We present an algorithm that learns the selection policy by accommodating the modeled accuracies of the heuristics, along with the outcome of the generative model. Finally, the system employs the output of the active learning process to enhance the quality of the labels. To evaluate the proposed system, we report its performance against four state-of-the-art techniques. In collaboration with our industrial partner, IBM, we test the framework within a wide range of real-world applications. The experiments include 10 datasets of varying sizes with a maximum size of 11 million records. The results illustrate the effectiveness of the framework in producing high-quality labels and achieving high classification accuracy with minimal annotation efforts.

Bioscientists reading papers or patents strive to discern the key relationships reported within a document “D“ where a bioactivity “A” with a quantitative result “R” (e.g., an IC50) is reported for chemical structure “C” that modulates (e.g., inhibits) a protein target “P”. A useful shorthand for this connectivity thus becomes DARCP. The problem at the core of this article is that the community has spent millions effectively burying these relationships in PDFs over many decades but must now spend millions more trying to get them back out. The key imperative for this is to increase the flow into structured open databases. The positive impacts will include expanded data mining opportunities for drug discovery and chemical biology. Over the last decade commercial sources have manually extracted DARCP from ≈300,000 documents encompassing ≈7 million compounds interacting with ≈10,000 targets. Over a similar time, the Guide to Pharmacology, BindingDB and ChEMBL have carried out analogues DARCP extractions. Although their expert-curated numbers are lower (i.e., ≈2 million compounds against ≈3700 human proteins), these open sources have the great advantage of being merged within PubChem. Parallel efforts have focused on the extraction of document-to-compound (D-C-only) connectivity. In the absence of molecular mechanism of action (mmoa) annotation, this is of less value but can be automatically extracted. This has been significantly accomplished for patents, (e.g., by IBM, SureChEMBL and WIPO) for over 30 million compounds in PubChem. These have recently been joined by 1.4 million D-C submissions from three major chemistry publishers. In addition, both the European and US PubMed Central portals now add chemistry look-ups from abstracts and full-text papers. However, the fully automated extraction of DARCLP has not yet been achieved. This stands in contrast to the ability of biocurators to discern these relationships in minutes. Unfortunately, no journals have yet instigated a flow of author-specified DARCP directly into open databases. Progress may come from trends such as open science, open access (OA), findable, accessible, interoperable and reusable (FAIR), resource description framework (RDF) and WikiData. However, we will need to await the technical applicability in respect to DARCP capture to see if this opens up connectivity.

In the paper, we analyze problems of reliability and security in the world practice and in Russia. We consider aspects of modeling software/hardware systems from service resources and ready-made reuses with ensuring reliability and security. We present the formed basic and theoretical foundations of the modeling problem, the experience of using modern service tools SOA, SCA, SOAP in software/hardware systems and Web systems to ensure their reliability and security on the Internet. We note that software/hardware systems and Web systems are created by the assembly build method in modern environments: IBM WSDK + WebSphere, Apache Axis + Tomcat; Microsoft .Net + IIS, etc. Verification and testing of systems should be conducted for searching of errors that occur in exceptional cases (cyber-attacks, forbidden access to the database, etc.). We describe methods for analyzing such situations and applying reliability and security methods to ensure stable and trouble-free operation of software/hardware systems service components in the Internet information environment.

This article describes the family of emulators for IBM mainframe architectures, their development history, functional features and capability, as well as the experience of many years (since 1994) of emulators development and their implementation area. There was sold the relatively simple task (for modern standards) of creating a virtual machine in the VSE/ESA operating system for transferring legacy platform-dependent applications to this target environment. The problem was solved at first for EU computers in Russia, and then for IBM 9221 in Germany and in the other western countries. The transfer was made to the OS/390 environment, and to IBM AIX, quite modern at that time. The virtual execution of any existing IBM mainframe operating systems in the main server OS environments: Linux, Windows, AIX, Z/OS, ZLinux had been provided. There was developed the solution for combining any types of formed virtual computing nodes into heterogeneous geographically distributed computing networks that provide, in particular, multiple mutual redundancy of nodes in the network.

The aim of this paper is to analyze the sharing and management of files in a tertiary institution using blockchain architecture. It is expected to enable an online system that could provide a decentralized architecture for multiple transfers and sharing of files amongst participants, to design a system that can provide data integrity and security of files using IBM Blockchain technology, to provide a system that can allow multiple user and multiple transactions at once. The research was motivated because of the security challenges associated with existing system which include: delay in transfer and sharing of files, much authority and task given to a single user in a centralized system, high risk of attack and loss of files, reduced speed of file transfer and file access/retrieval, limited number of users per time. Methodology adopted was Object Oriented Analysis Design Methodology (OOADM) in conjunction with Unified Modeling Language (UML) and IBM Blockchain Technology while the programming language used was HTML, CSS, Java and Node Js. The result after design was a decentralized cloud based file sharing and management system that enables multi-shared, replicated and permissioned transactions amongst participants in a network.

Cloud service brokerage is an emerging technology that attempts to simplify the consumption and operation of hybrid clouds. Today's cloud brokers attempt to insulate consumers from the vagaries of multiple clouds. To achieve the insulation, the modern cloud broker needs to disguise itself as the end-provider to consumers by creating and operating a virtual data center construct that we call a meta-cloud, which is assembled on top of a set of participating supplier clouds. It is crucial for such a cloud broker to be considered a trusted partner both by cloud consumers and by the underpinning cloud suppliers. A fundamental tenet of brokerage trust is vendor neutrality. On the one hand, cloud consumers will be comfortable if a cloud broker guarantees that they will not be led through a preferred path. And on the other hand, cloud suppliers would be more interested in partnering with a cloud broker who promises a fair apportioning of client provisioning requests. Because consumer and supplier trust on a meta-cloud broker stems from the assumption of being agnostic to supplier clouds, there is a need for a test strategy that verifies the fairness of cloud brokerage. In this paper, we propose a calculus of fairness that defines the rules to determine the operational behavior of a cloud broker. The calculus uses temporal logic to model the fact that fairness is a trait that has to be ascertained over time; it is not a characteristic that can be judged at a per-request fulfillment level. Using our temporal calculus of fairness as the basis, we propose an algorithm to determine the fairness of a broker probabilistically, based on its observed request apportioning policies. Our model for the fairness of cloud broker behavior also factors in inter-provider variables such as cost divergence and capacity variance., Comment: This paper appears in the Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2018)

In a post-quantum world, where attackers may have access to full-scale quantum computers, all classical password-based authentication schemes will be compromised. Quantum copy-protection prevents adversaries from making copies of existing quantum software; we suggest this as a possible approach for designing post-quantum-secure password authentication systems. In this paper, we show an implementation of quantum copy-protection for password verification on IBM quantum computers. We also share our quantum computation results and analyses, as well as lessons learned., Comment: 7 pages, 8 figures, 4 tables

We developed an elaborated susceptible-infected-recovered (SIR) individual-based model (IBM) with pathogen strain drift, waning and cross immunity, implemented as a novel Java Runtime-Alterable-Model Platform (J-RAMP). This platform allows parameter values, process formulations, and scriptable runtime drivers to be easily added at the start of simulation. It includes facility for integration into the R statistical and other data analysis platforms. We selected a set of parameter values and process descriptions relevant to the current COVID-19 pandemic. These include pathogen-specific shedding, environmental persistence, host transmission and mortality, within-host pathogen mutation and replication, adaptive social distancing, and time dependent vaccine rate and strain valency specifications. Our simulations illustrate that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible strains. Our study highlights the need for adaptive vaccination rollouts, which depend on reliable real-time monitoring and surveillance of strain proliferation and reinfection data needed to ensure that vaccines target emerging strains and constrain escape mutations. Together with such data, our platform has the potential to inform the design of vaccination programs that extirpate rather than exacerbate local outbreaks. Finally, our RAMP concept promotes the development of highly flexible models that can be easily shared among researchers and policymakers not only addressing healthcare crises, but other types of environmental crises as well. Significance Statement Effective COVID-19 vaccine development has been unprecedented, but vaccinations are being delivered at contrasting rates across the globe. Here, using an innovative epidemiological Java runtime alterable modeling platform (J-RAMP), we demonstrate that seemingly reasonable vaccination programs may exacerbate rather than mitigate regional outbreaks when immune waning outpaces vaccinations and reinfection promotes escape mutations. Our simulations suggest that adaptive vaccination programs, requiring adequate strain and serology monitoring and surveillance, are needed to extirpate outbreaks. Our platform provides policymakers with an easy-to-use tool for designing effective vaccination programs. Our RAMP concept points the way to the development of highly flexible models that are easily shared among researchers and policymakers in all areas of systems analysis. Classification Major: Biological Sciences; Minor: Biophysics and Computational Biology

Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.

Since the inception of this new technology, inclusion of cloud computing in IT sectors has grown in popularity. This is a new movement in industrial technology that will continue to expand and develop their competency in the coming years. Because of numerous advantages of cloud computing over traditional computing, most businesses are fast switching to it. As per one of the report published by IBM in 2020 the average cost for a data breach place 3.8 million dollar. 1001 data breaches are came in to the figure in United States alone in 2020. More and more security issues and challenges are swelling with the growth in the complexity of this technology. So, to enable the proper security wheels with the services hosted by the CSPs is the leading concern. The motive here is to investigate security vulnerabilities & challenges of the cloud-computing environment which further provide an ease to applications deployment. It also present a taxonomy of various building blocks of Cloud Computing platform.

Tony Hoare has had an enormous impact on computing: early in his career, he created software for users; even when he addressed more theoretical topics, there was always the aim to affect the way that software could be created. A theme that can be seen throughout Tony’s work is a striving for useful abstractions and this can be seen clearly in many of the contributions gathered into this book. The contributors cover many of Tony’s seminal ideas. Something else, however, comes through in these essays, and that is the warmth and gratitude of his colleagues. As well as providing scientific insight and inspiration, it is clear that the collaborations achieved more than either party could possibly have done without the other. Tony has earned many honours; this book is connected to the A. M. Turing Award from the ACM—books are being created for each Turing Award. We, the editors of this volume, count our task as a privilege—one made so much easier by the enthusiastic agreement of the contributors to undertake their writing tasks in the peculiar year of 2020. This book contains six parts associated with major areas of Tony’s work. Part I contains what might be classed as biographical information: Chapter 1 gives a technical description of Tony’s research up to 1981 when he delivered this talk on receiving the ACM Turing Award; Chapter 2 looks at the context and development of some of Tony’s key insights. Part II focuses on program verification research, which is one area of Tony’s scientific contribution: Chapter 3 covers what is now commonly referred to as ‘Hoare logic’ and traces the impact of this seminal work; Chapters 4 and 5 reprint two papers that set out a major push to see verification ideas put into practice: the ‘Verified Software Initiative’; continuing this theme, Chapter 6 traces the early international successes of that initiative; and Chapter 7 provides a detailed account of a specific project undertaken by a UK team for the American NSA. Part III moves on to Tony’s research on concurrency: Chapter 8 contains a reprint of a key paper on ‘Communicating Sequential Processes’—as explained in the next chapter, this paper can be seen as a key stepping stone to an even more radical (and algebraic) view of concurrent software; Chapter 9 provides an inside account of the collaboration that has done so much to inform our understanding of concurrency. Part IV demonstrates that Tony’s research is not confined to the theoretical realm; it has had considerable impact on the industrial world and Tony believes passionately in disseminating his ideas as widely as possible: Chapter 10 covers his contributions to teaching and developing degree programmes at Belfast and Oxford; Chapter 11 covers one of the best-known collaborations that saw researchers at Oxford University work with developers at IBM’s UK Laboratory to use the ‘Z’ specification language on a key IBM software project; Chapter 12 is about the application of CSP in defining the programming language occam and applying it to the development of the Transputer system at Inmos Corporation. Part V covers the concluding research projects of Tony’s career: Chapter 13 is a gentle introduction to UTP, tracing its history, and the author’s own history of participating in the project; Chapter 14 focuses on deep algebraic properties on fundamental ideas of concurrency. Part VI (which contains Chapter 15) concludes the book with Tony Hoare’s views on research. In addition, there are five appendices that provide other facts about Tony’s career and publications. Appendix A contains the transcript of the interview recorded in 2015 for the ACM series on Turing Laureates (the video recording is available at https://amturing.acm.org/interviews/hoare_4622167.cfm); Appendix B pins down some key dates and facts of Tony’s long career; Appendix C lists the students who received Tony’s direct supervision in their doctoral research; Appendix D contains a numbered list of Tony’s publications; Appendix E provides pointers to a number of interviews and talks that are available on the Internet. One area not touched on adequately by the editors’ selection of contributors is Tony’s major impact on the general development of computing science. He has, for example, often given talks addressing the importance of the subject. Among frequent editorial contributions, one major example was the famous Prentice-Hall ‘red and white’ series that published over a hundred influential books. Tony’s generous support for other researchers does shine through in many of the essays in this volume. Another indication of how much researchers and friends value their contact with Tony was the instant agreement of the authors to contribute to this volume. This book is available in two formats: paper and online (where links are clickable). Some of the URLs point to items whose copyright is owned by third parties and might thus be behind a paywall. Acknowledgements The editors are, above all, grateful to Tony for his trust and encouragement. An added bonus of meetings and calls to Cambridge has been the supportive presence of Jill Hoare. Nor have we stopped there with consulting the family: Joanna Francis in particular provided valuable family information. The next acknowledgement is our enormous gratitude to the authors who have written the essays for this volume. Despite the many pressures on their busy professional lives—and the special difficulties of the year 2020—they have found time to write interesting and original papers based on their own close contacts with Tony. We also welcome the opportunity to thank Margaret Gray, who has provided invaluable editorial support with dependable accuracy and, frequently, real initiative. In parallel with her own doctoral research, Margaret not only undertook many tasks for the editors but also made a productive visit to meet Tony and Jill in Cambridge. The cooperation with ACM Books has been first-class. From an initial plan of a book that would have mainly consisted of Tony’s papers (this ran up against insurmountable permission costs), via a plan for two books, to this eventual realisation of ‘one book in two formats’, Sanjiva Prasad and the staff at ACM HQ have been perfect discussion partners showing flexibility and support all of the way. Furthermore, the selection of Compuscript Ltd. for production of the book proved to be ideal: faced with the challenge of a wide range of LaTeX styles, and somewhat different views on formatting, Karen Grace and her colleagues have produced a fine book and remained good humoured throughout the process. We are grateful to the Inamori Foundation, Japan, for use of the source material from the 2000 Kyoto Prize presentation ceremony in honour of Sir Tony Hoare. The material includes two photographs reprinted on pages xi and 391 of this book, as well as his commemorative lecture and the acceptance speech which appear in the online edition. The Leverhulme Foundation has provided support under grant RPG-2019-020 for Jones’ work on this book. Cliff Jones Jayadev Misra

We have been talking about 5G and beyond (B5G) in IEEE future networks. The Exabyte of data moving from Cloud enabled edge to distributed edge has led to a push for local processing at the edge. We start with what is intelligence and how to distribute it in light of Edge resources. It started with a need for a nimble edge, essentially microdata centers to another extreme "replace every O-RAN and BBUs with a Hyperscale DC". We try to ascertain through this survey can Intelligent devices termed xPU (covering GPUs, FPGAs, SoCs and NVME and many more) provide functional baseline for lowering power consumption & performance, to support AI and ML to deliver edge native low latency & ultra-reliable functions beyond 5G as a better alternative? This is a survey of different industry parties to enable innovation and intelligent edge local processing, discovering challenges and trends to arrive at runtime baseline and abstractions. The survey covers the challenges of different evolving architectures x86, ARM, RISC-V with players like Intel, NVIDIA, Microsoft, IBM, Google & Amazon to name a few. The common theme turns out to be intelligent connected edge services on mini–Data Center as one option while the Edge focus to be Scalable, Secure, Reliable, Performant & Automated platform & Services. We have Reference, Glossary at the end for acronyms used.

Code Inspection is a validation process widely used to improve the quality of software. To streamline this process and decrease the possibility of human error, improving the reliability of inspection results, it is possible to use specialized automated code inspection tools. Thus, this article proposes to analyze code inspection tools for PHP programming language, freely available on Github Marketplace. To achieve this goal, the GLPI system was chosen to be inspected, in addition, four code inspection tools were selected, out of twenty-eight available. Criteria were used for the tools selection, consistent with the sytem profile to be inspected and that do not have limitations on the inspection result. To classify the results obtained, the Common Weakness Enumeration (CWE) was used, a list of software and hardware weaknesses developed by numerous renowned companies, such as Microsoft, Apple and IBM. As a result of the inspection work, we found more than ten thousand failures divided into thirty-four different CWEs and from these we analyzed the individual feedback of each tool, as each one of them had unique advantages and disadvantages.

With the development of modern information technology and network technology, the construction of online learning platform and English learning website is becoming more and more abundant. They provide learners with rich and convenient knowledge sources, but there are also many problems at the same time, the main reason is that it focuses on the "information" rather than "knowledge". Some famous knowledge management companies, IBM, lotus, Microsoft and other online learning suppliers at home and abroad have integrated learning and knowledge management on the whole. Many achievements have been made in the theoretical research of knowledge management in education field. However, for the related research of basic education, especially for teachers and students, the application of knowledge management is still in the initial stage. Therefore, it is necessary to introduce the concept of knowledge management into the network learning platform. The main part of this paper is the design and implementation of the network learning platform. In the design part, the author analyzes the learning needs of English learners, according to the knowledge management activities, the design process of the network learning platform, the structure of knowledge management activities, and then analyzes the four aspects of the personal knowledge management subsystem. The project coordination subsystem, knowledge base system and system management subsystem are designed in detail.

The healthcare system has been effectively administered in such a way that doctors and patients are perceiving it with recent advancements in technologies such as a Chatbot. The Chatbot has been pervasively utilized in a wide range of applications such as travel, retail, medical, education, etc. The dialogue-based interactive diagnostic Chatbot has been designed, developed, and deployed over the cloud environment so that any messenger could get the support of the services offered by the healthcare system. The algorithms such as Machine Learning (ML), Deep Learning, Information Retrieval (IR), Natural Language Processing (NLP), and cloud are facilitating the service. This chapter covers such conversational agents aiming at responding to the questions asked by the public or by the patients who have the fear of COVID-19 disease. A detailed study of technological support, user experience, and healthcare research is also taken into consideration and thereby able to reduce the deadly threats that persist among the people and provide timely assistance. The developed Chatbot is being deployed in a multi-cloud environment to minimize the risk of downtime and prevalent data loss. We have tested the execution of the Chatbot on the IBM cloud platform with a dataset collected related to COVID 19. The workflow of the same has been visualized stage by stage.

Scalable management of user workloads on large-scale supercomputers remains a challenge due to the tradeoff between capturing adequate detail for analysis from various data sources and minimizing overhead. Co-designed frameworks, such as IBM’s Cluster System Management (CSM), provide a unified approach and novel insights for large-scale cluster management. This paper presents a longitudinal study and detailed analysis of a first-of-its-kind dataset collected by CSM from one of the world’s fastest supercomputers – comprised of over 1.4 million jobs on heterogenous nodes over multiple years. Furthermore, by focusing on a case study for power management, we identify the strengths and limitations of current CSM power measurement techniques in production. We present a deep dive into a large-scale scientific workflow, where finer-grained monitoring reveals power fluctuations at megawatt-levels resulting from the dynamic nature of the application, which are not captured by CSM. We make our unique datasets available to the HPC community, and discuss potential mitigation strategies by analyzing both coarse-grained and fine-grained data.

The increased demand for machine learning applications made companies offer Machine-Learning-as-a-Service (MLaaS). In MLaaS (a market estimated 8000M USD by 2025), users pay for well-performing ML models without dealing with the complicated training procedure. Among MLaaS, text-based applications are the most popular ones (e.g., language translators). Given this popularity, MLaaS must provide resiliency to adversarial manipulations. For example, a wrong translation might lead to a misunderstanding between two parties. In the text domain, state-of-the-art attacks mainly focus on strategies that leverage ML models' weaknesses. Unfortunately, not much attention has been given to the other pipeline' stages, such as the indexing stage (i.e., when a sentence is converted from a textual to a numerical representation) that, if manipulated, can significantly affect the final performance of the application. In this paper, we propose a novel text evasion technique called "\textit{Zero-Width} attack" (ZeW) that leverages the injection of human non-readable characters, affecting indexing stage mechanisms. We demonstrate that our simple yet effective attack deceives MLaaS of "giants" such as Amazon, Google, IBM, and Microsoft. Our case study, based on the manipulation of hateful tweets, shows that out of 12 analyzed services, only one is resistant to our injection strategy. We finally introduce and test a simple \textit{input validation} defense that can prevent our proposed attack., Accepted to appear in the Proceedings of the 2021 IEEE European Symposium on Security and Privacy (EUROS&P)

Next generation Z processor is optimized to run enterprise workloads with embedded real time AI insights.

The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models, there are limited efforts towards improving the data quality. Assessing the quality of the data across intelligently designed metrics and developing corresponding transformation operations to address the quality gaps helps to reduce the effort of a data scientist for iterative debugging of the ML pipeline to improve model performance. This tutorial highlights the importance of analysing data quality in terms of its value for ML applications. Finding the data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue. This tutorial surveys all the important data quality related approaches for structured, unstructured and spatio-temporal domains discussed in literature, focusing on the intuition behind them, highlighting their strengths and similarities, and illustrates their applicability to real-world problems. Finally we will discuss the interesting work IBM Research is doing in this space.

With the development of intelligent phones and speech recognition technology, there is a great demand for generating meeting minutes automatically. In this paper, we design and implement Yanji, an automated system for generating meeting minutes based on speech and speaker recognition. The Yanji system realizes the following functions: recording the audio of the conference, uploading audios to IBM cloud in real time, transcribing audios to texts and identifying various speakers with IBM Speech to Text API, and finally generating complete meeting minutes. Yanji greatly reduces the recording storage space and the labor cost of listening and writing, and improves the meeting efficiency.

In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results. How well does Mono2Micro perform against other decomposition techniques, and how do practitioners perceive the tool? This paper describes the technical foundations of Mono2Micro and presents results to answer these two questions. To answer the first question, we evaluated Mono2Micro against four existing techniques on a set of open-source and proprietary Java applications and using different metrics to assess the quality of decomposition and tool’s efficiency. Our results show that Mono2Micro significantly outperforms state-of-the-art baselines in specific metrics well-defined for the problem domain. To answer the second question, we conducted a survey of twenty-one practitioners in various industry roles who have used Mono2Micro. This study highlights several benefits of the tool, interesting practitioner perceptions, and scope for further improvements. Overall, these results show that Mono2Micro can provide a valuable aid to practitioners in creating functionally cohesive and explainable microservice decompositions.

Natural language processing is a broad field that encompasses several sub-tasks. One problem that has gained visibility over the past several years is that of Sentiment Analysis. This is the process of determining the attitude of an author towards some subject across some spectrum, typically “positive” or “negative,” by analyzing the textual information. Whereas the field started with simple counting of words with certain characteristics, it has grown in complexity with the advent of deep learning and neural network based language models. Typically, datasets used to train and evaluate these models consist of text with appropriate labels, such as movie reviews with an accompanied star rating. However, the applicability of those results to other scenarios, such as unstructured or natural text has not been clear. In this paper, we demonstrate a clear and simple case that shows that the problem of sentiment analysis is fundamentally unsuitable for natural text. We consider state-of-the-art black box models developed and hosted by 3 of the largest companies in this field: Amazon, Google and IBM.