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A Software Development Kit and Translation Layer for Executing Intel 8080 Assembler on a Quantum Computer (August 2022)
کیت توسعه نرم افزار و لایه ترجمه برای اجرای اسمبلر اینتل 8080 در رایانه کوانتومی (اوت 2022)-2022 One of the major obstacles to the adoption of quantum computing is the requirement to define
quantum circuits at the quantum gate level. Many programmers are familiar with high-level or low-level
programming languages but not quantum gates nor the low-level quantum logic required to derive useful
results from quantum computers. The steep learning curve involved when progressing from quantum gates to
complex simulations such as Shor’s algorithm has proven too much for many developers. The purpose of this
article and the software presented within addresses this challenge by providing a Software Development Kit
(SDK), translation layer, emulator, and a framework of techniques for executing Intel 8080/Z80 assembler on
a quantum computer, i.e., all salient points of CPU execution, logic, arithmetic, and bitwise manipulation will
be executed on the quantum computer using quantum circuits. This provides a novel means of displaying
the equivalency and interoperability of quantum and classical computers. Developers and researchers can
use the SDK to write code in Intel 8080/Z80 assembler which is executed locally via traditional emulation
and remotely on a quantum computer in parallel. The emulator features side-by-side code execution with
visibility of the running quantum circuit and reusable/overridable methods. This enables programmers to
learn, reuse, and contrast techniques for performing any traditional CPU-based technique/instruction on a
quantum computer, e.g., a programmer may know how to multiply and perform checks on a classical CPU
but is not able to perform the same tasks in a quantum implementation, and this SDK allows the programmer
to pick and choose the methods they would like to use to fulfil their requirements. The SDK makes use of
open-source software, specifically Python and Qiskit for the emulation, translation, API calls, and execution
of user-supplied code or binaries.
INDEX TERMS: Emulation | macro and assembly languages | processors | Qiskit | quantum computing | quantum gates | translation. |
مقاله انگلیسی |
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Synthesizing researches on Knowledge Management and Agile Software Development using the Meta-ethnography method
ترکیب تحقیقات مدیریت دانش و توسعه نرم افزار چابک با استفاده از روش فرا قوم نگاری-2021
Context: Software development processes are considered as knowledge intensive and therefore Knowledge Management (KM) can be applied to efficiently manage the knowledge generated. Agile practices
can benefit software organizations in terms of KM. Some studies have already presented evidence about
this relationship. However, the intersection of these two areas still requires further clarification.
Objective: This study synthesizes research on KM and Agile Software Development (ASD) using the meta-ethnography method considering Scrum and XP frameworks. Method: In order to achieve the proposed goal, first, a diagnostic was conducted in different project domains with agile and traditional software development in order to identify the performance of KM activities. Second, the phases of the meta-ethnography analysis method were applied in a set of studies selected from a tertiary review on KM and ASD, as well as classic guides and area references. Finally, the relationships that were identified among the areas investigated were analyzed from interviews with agile development methodology experts. Results: The most common activity investigated between KM and ASD is knowledge sharing. However, in the practical view of software development companies, the attention is on how to use the generated knowledge. Conclusion: The clarification of how KM is present in each agile value, practices, and artifacts allows a reflection on how much knowledge was created, shared, and applied during ASD. Besides, such results presented in this study enable organizations to know each other better and to explore more each KM activity, thus contributing to delivering more value to the customer keywords: Meta-ethnography | Knowledge management | Agile software development | Scrum | XP |
مقاله انگلیسی |
3 |
It is about time: Bias and its mitigation in time-saving decisions in software development projects
درباره زمان است: تعصب و کاهش آن در تصمیم گیری های صرفه جویی در زمان در پروژه های توسعه نرم افزار-2020 Estimates of completion times in software development projects are frequently inaccurate, potentially resulting in
failure to meet project objectives. The present work aims at empirically investigating whether the time-saving
bias, describing the human failure to correctly estimate the relationship between speed increase and time
saving, can inform our understanding of the decades-long problem of time estimation in software development. In
particular, this work examines whether a decision to save time in a software development project by increasing
development speed is biased, whether this bias is observed when the decision is framed using plan-based and agile
terminology, and whether the availability of relevant information mitigates this bias. These objectives are
addressed in three experimental studies, in which senior information systems students (Study 1) and professional
software project managers (Studies 2 and 3) are asked to make time-saving decisions about two similar scenarios,
with and without relevant information. The findings confirm the existence of the bias and show that it is more
likely to occur under an agile framing than under a plan-based framing, although students are highly biased in
both cases. The findings also show that while the bias is mitigated, but not eliminated, when relevant information
is included in the scenario, this effect dissipates once the information is no longer included in the following
scenario. The accumulated evidence reported here contributes to research on the consequences of cognitive biases
for project management decisions. Keywords: Decision making | Time-saving bias | Software development projects | Experiments |
مقاله انگلیسی |
4 |
Hacking the AI - the Next Generation of Hijacked Systems
هک کردن هوش مصنوعی - نسل بعدی سیستم های ربوده شده-2020 Within the next decade, the need for automation, intelligent data handling
and pre-processing is expected to increase in order to cope with the vast amount of
information generated by a heavily connected and digitalised world. Over the past
decades, modern computer networks, infrastructures and digital devices have grown
in both complexity and interconnectivity. Cyber security personnel protecting these
assets have been confronted with increasing attack surfaces and advancing attack
patterns. In order to manage this, cyber defence methods began to rely on automation
and (artificial) intelligence supporting the work of humans. However, machine learning
(ML) and artificial intelligence (AI) supported methods have not only been integrated
in network monitoring and endpoint security products but are almost omnipresent in
any application involving constant monitoring, complex or large volumes of data.
Intelligent IDS, automated cyber defence, network monitoring and surveillance as
well as secure software development and orchestration are all examples of assets that
are reliant on ML and automation. These applications are of considerable interest to
malicious actors due to their importance to society. Furthermore, ML and AI methods
are also used in audio-visual systems utilised by digital assistants, autonomous
vehicles, face-recognition applications and many others. Successful attack vectors
targeting the AI of audio-visual systems have already been reported. These attacks
range from requiring little technical knowledge to complex attacks hijacking the
underlying AI. With the increasing dependence of society on ML and AI, we must prepare for the
next generation of cyber attacks being directed against these areas. Attacking a system
through its learning and automation methods allows attackers to severely damage the
system, while at the same time allowing them to operate covertly. The combination of being inherently hidden through the manipulation made, its devastating impact
and the wide unawareness of AI and ML vulnerabilities make attack vectors against
AI and ML highly favourable for malicious operators. Furthermore, AI systems
tend to be difficult to analyse post-incident as well as to monitor during operations.
Discriminating a compromised from an uncompromised AI in real-time is still
considered difficult.
In this paper, we report on the state of the art of attack patterns directed against AI
and ML methods. We derive and discuss the attack surface of prominent learning
mechanisms utilised in AI systems. We conclude with an analysis of the implications
of AI and ML attacks for the next decade of cyber conflicts as well as mitigations
strategies and their limitations. Keywords: AI hijacking | artificial intelligence | machine learning | cyber attack | cyber security |
مقاله انگلیسی |
5 |
The design of software development platform for CFETR plasma control system
طراحی بستر توسعه نرم افزار برای سیستم کنترل پلاسما CFETR-2020 The Plasma Control System (PCS) is a critical system of the tokamak device to guarantee the physical experiment
operation. While the Chinese Fusion Engineering Testing Reactor (CFETR) PCS is in the preliminary development
stage, the newly designed Plasma Control System Software Development Platform (PCS-SDP) will provide
an effective, convenient, and visual development environment for PCS software developers. The PCS-SDP is
developed based on the Eclipse framework as an extension and finally realized as an Eclipse plug-in. It is deployed
in a thin-client C/S mode in which developers log in and work remotely and all the developments are
carried on a development server. The PCS-SDP possesses an intuitive UI and contains modules of project
management, algorithm management, visualization management, testing management, and version management.
Because of these customized functions, the PCS-SDP makes the developers focus on the control logic
design of the PCS algorithms without the need to pay attention to the PCS details; the work efficiency is improved
significantly. In this paper, the requirements are analyzed, the system architecture and module design are
presented, and some functions are demonstrated. The initial hardware environment deployment has been implemented
and is also presented in this paper. Further efforts will be made to implement and demonstrate the
functions of all modules on the EAST PCS, then serve CFETR PCS development and can be appropriate for most
Plasma Control Systems Keywords: Software platform | Plasma control system | Eclipse | Visualization | Algorithm management |
مقاله انگلیسی |
6 |
Evaluation of the implementation of a subset of ISO/IEC 29110 Software Implementation process in four teams of undergraduate students of Ecuador. An empirical software engineering experiment
ارزیابی اجرای زیر مجموعه ای از فرآیند اجرای نرم افزار ISO / IEC 29110 در چهار تیم از دانشجویان کارشناسی ارشد اکوادور. یک آزمایش مهندسی نرم افزار تجربی-2020 The competitiveness of software development companies depends on their ability to offer software products with
quality attributes within approved budget and schedule. Most Very Small Entities (VSEs) that develop software
do not see the benefits of implementing software standards. Consequently, they limit their potential to be recognised
as quality software development entities. In this study, the authors present results obtained through the
application of empirical software engineering in an experiment in which the ISO/IEC TR 29110–5–1–2 “Software
engineering – Lifecycle profiles for Very Small Entities (VSEs) – Part 5–1–2: Management and engineering guide:
Generic profile group: Basic profile” was used. The guide includes two processes: Project Management (PM)
process and Software Implementation (SI) process. The objective of the project was the development of a
software product for the scheduling of medical appointments for the Student Wellness Center of a university of
Ecuador. Four teams of undergraduate students were involved. Two of them (controlled teams) implemented a
subset of the SI process, while the other two (non-controlled teams) had freedom to choose development activities
that were subsequently mapped with the activities of the standard. All teams developed the software
product using the SCRUM framework within the same timeframe. Although the experiment was focused on the SI
process, the teams also used a tailored version of the PM process defined by the professors. The experiment
execution encountered several difficulties. For example, the timeframe of six weeks established in the design of
the experiment was too short since students worked part time in the project. All the teams experienced this
difficulty, especially when they had to construct and test the software components. Overall, the teams that used
the ISO/IEC TR 29110–5–1–2 guide achieved better scores in the quality evaluation of their software processes. Keywords: ISO/IEC 29110 | ISO/IEC 25000 | Software implementation process | Experimentation | Empirical software engineering | Software quality |
مقاله انگلیسی |
7 |
Supporting the deployment of ISO-based project management processes with agile metrics
پشتیبانی از استقرار فرآیندهای مدیریت پروژه مبتنی بر ISO با معیارهای چابک-2020 Agile approaches are well perceived in software development companies. These approaches are not faced with
traditional process models. This research analyses how by defining and monitoring a set of Agile metrics, Agile
mature companies can be also conformant with the best practices proposed by the targeted ISO process reference
models. Conformance has been identified with five Technical Management processes of the ISO/IEC/IEEE 12207
standard and with two activities of the Project Management process of the ISO/IEC TR 29110-5-1-2 standard.
The findings may be of the interest of those Agile settings that need to work according to the process model
established in the company. Keywords: Agile, Metrics | Project Management | Software Process Improvement | ISO/IEC/IEEE 12207 | ISO/IEC TR 29110-5-1-2 |
مقاله انگلیسی |
8 |
foxBMS - free and open BMS platform focused on functional safety and AI
foxBMS - پلت فرم رایگان و باز BMS با تمرکز بر ایمنی عملکردی و هوش مصنوعی-2020 The last years have shown a strong market demand for lithium-ion battery systems with higher energy
densities, longer lifetimes, and lower costs, but at the same time without compromising safety. To help
developers, engineers and researchers worldwide, Fraunhofer IISB has established the free and open
source Battery Management System (BMS) development platform ”foxBMS”. The foxBMS platform consists
of a modular hardware and software architecture and a complete software development toolchain. Based
on the experience providing foxBMS-based solutions to customers and the research community, the next
generation of foxBMS is strongly focused on functional safety standards. The hardware architecture and
the hardware components themselves help to ensure that functional safety standards are met. Additionally,
foxBMS supports a workflow for implementing Artificial Intelligence (AI)-based battery state estimators for
the BMS. Using foxBMS as a data generator within this workflow a Neural Network (NN) based on a Long
Short-Term Memory (LSTM) is trained offline to estimate the state of charge (SOC) based on the current
and voltage measurement input. The simulation results, obtained with the trained NN running online on
the device, are shown in this paper. |
مقاله انگلیسی |
9 |
Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names
مدل یادگیری عمیق برای تخمین پایان به پایان اندازه کاربردی COSMIC بر اساس نامهای مورد استفاده-2020 Context: COSMIC is a widely used functional size measurement (FSM) method that supports software development effort estimation. The FSM methods measure functional product size based on functional requirements. Unfortu- nately, when the description of the product’s functionality is often abstract or incomplete, the size of the product can only be approximated since the object to be measured is not yet fully described. Also, the measurement performed by human-experts can be time-consuming, therefore, it is worth considering automating it. Objective: Our objective is to design a new prediction model capable of approximating COSMIC-size of use cases based only on their names that is easier to train and more accurate than existing techniques. Method: Several neural-network architectures are investigated to build a COSMIC size approximation model. The accuracy of models is evaluated in a simulation study on the dataset of 437 use cases from 27 software develop- ment projects in the Management Information Systems (MIS) domain. The accuracy of the models is compared with the Average Use-Case approximation (AUC), and two recently proposed two-step models —Average Use-Case Goal-aware Approximation (AUCG) and Bayesian Network Use-Case Goal AproxImatioN (BN-UCGAIN). Results: The best prediction accuracy was obtained for a convolutional neural network using a word-embedding model trained on Wikipedia + Gigaworld. The accuracy of the model outperformed the baseline AUC model by ca. 20%, and the two-step models by ca. 5–7%. In the worst case, the improvement in the prediction accuracy is visible after estimating 10 use cases. Conclusions: The proposed deep learning model can be used to automatically approximate COSMIC size of software applications for which the requirements are documented in the form of use cases (or at least in the form of use- case names). The advantage of the model is that it does not require collecting historical data other than COSMIC size and names of use cases. Keywords: Functional size approximation | Approximate software sizing methods | COSMIC | Deep learning | Word embeddings | Use cases |
مقاله انگلیسی |
10 |
Scientific Authors in a Changing World of Scholarly Communication: What Does the Future Hold?
نویسندگان علمی در دنیای متغیر ارتباطات علمی: آینده چیست؟-2020 Scholarly communication in science, technology, and medicine has been organized around journal-based
scientific publishing for the past 350 years. Scientific publishing has unique business models and includes
stakeholders with conflicting interests—publishers, funders, libraries, and scholars who create, curate, and
consume the literature. Massive growth and change in scholarly communication, coinciding with digitalization,
have amplified stresses inherent in traditional scientific publishing, as evidenced by overwhelmed
editors and reviewers, increased retraction rates, emergence of pseudo-journals, strained library budgets,
and debates about the metrics of academic recognition for scholarly achievements. Simultaneously,
several open access models are gaining traction and online technologies offer opportunities to augment
traditional tasks of scientific publishing, develop integrated discovery services, and establish global and
equitable scholarly communication through crowdsourcing, software development, big data management,
and machine learning. These rapidly evolving developments raise financial, legal, and ethical dilemmas
that require solutions, while successful strategies are difficult to predict. Key challenges and trends are
reviewed from the authors’ perspective about how to engage the scholarly community in this multifaceted
process. KEYWORDS: Open access | Peer review | Predatory publishing | Preprint repository | Self-archiving |
مقاله انگلیسی |