THE FOUNDATIONS OF MARX’S THEORY OF ALIENATION: MARX’S CRITIQUE OF HIS PREDECESSORS AND ALIENATED LABOUR

ABSTRACT: Marx’s conceptualization of alienation is influenced by his predecessors Hegel and Feuerbach. However, Marx neither accepts these conceptualizations as they are nor makes a synthesis of them. Instead, he builds his original theory of alienation on the criticism of his predecessors’ views on the subject. As a result, Marx’s theory of alienation becomes materialistic, historical and social. The historical and social conditions Marx was in pointed to the capitalist mode of production and the alienation of the working class caused by it as the causes of unfreedom. In the Economic and Philosophic Manuscripts of 1844, he focuses on the wage worker’s alienation stemming from the labour process. The purpose of this article is to present Marx’s critique of his predecessors in grounding the concept of alienation and his original contribution. For this, first of all, Marx’s criticisms of Hegel’s and then Feuerbach’s alienation theories will be explained. In this context, three points of criticism will be identified for each of them. Then, Marx’s theory of alienated labour will be discussed and the four aspects of the alienation of the worker will be examined. Based on Marx’s definition of alienated labour as forced labour, it will be argued that what causes alienation to productive activity, which Marx attributes a principal role compared to other aspects, is not division of labour or unpleasant work—or working conditions—but rather forced labour, which is a characteristic of the modes of production based on private property. The question of whether the alienation is specific to capitalism, which arises with this determination, may be a precursor for future studies. ÖZ: Marx’ın yabancılaşma kavramsallaştırması, kendinden önce gelen Hegel ve Feuerbach’tan etkilenir. Ancak Marx ne bu kavramsallaştırmaları olduğu gibi kabul eder ne de onların bir sentezini yapar. Bunun yerine, kendi özgün yabancılaşma kuramını öncellerinin görüşlerinin eleştirileri üzerine inşa eder. Böylece Marx’ta yabancılaşma kuramı materyalist, tarihsel ve toplumsal bir içerik kazanır. İçinde bulunduğu tarihsel ve toplumsal koşullar, özgür olmama halinin nedeni olarak Marx’ın karşısına kapitalist üretim tarzı ve onun sebep olduğu işçi sınıfının yabancılaşmasını çıkartır. 1844 El Yazmaları’nda ücretli işçinin emek sürecinden kaynaklanan yabancılaşmasına odaklanır. Bu makalenin amacı, yabancılaşma kavramının temellendirilmesinde Marx’ın öncellerine eleştirisini ve kendi özgün katkısını ortaya koymaktır. Bunun için ilk önce Marx’ın Hegel’in, sonra da Feuerbach’ın yabancılaşma kuramlarına yönelik eleştirilerine odaklanılacaktır. Bu bağlamda öncellerin her birine yönelik üç eleştiri noktası tespit edilecektir. Ardından Marx’ın yabancılaşmış emek kuramı ele alınacak ve ücretli işçinin yabancılaşmasının dört veçhesi (dört ilişki) incelenecektir. Ayrıca Marx’ın yabancılaşmış emeği zorla çalışma olarak tanımlamasından hareketle, diğer veçhelere kıyasla temel bir önem atfettiği üretici etkinliğe yabancılaşmaya neden olan unsurun sıklıkla iddia edildiği gibi esasında iş bölümü ve hoş olmayan çalışma ya da çalışma koşulları değil, emeğin, özel mülkiyete dayalı üretim tarzlarında kazandığı zorla çalışma karakteri olduğu öne sürülecektir. Bu tespit ile birlikte ortaya çıkan, yabancılaşmanın kapitalizme özgü olup olmadığı sorusu gelecekteki çalışmalar için ön açıcı olabilir

Closing the gap between software engineering education and industrial needs

According to different reports, many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learnt in their university education with what is needed in industry. To address that need, many studies have been conducted to align software engineering education with industry needs. To synthesize that body of knowledge, we present in this paper a systematic literature review (SLR) which summarizes the findings of 33 studies in this area. By doing a meta-analysis of all those studies and using data from 12 countries and over 4,000 data points, this study will enable educators and hiring managers to adapt their education / hiring efforts to best prepare the software engineering workforce

Design of a data management reference architecture for sustainable agriculture

Effective and efficient data management is crucial for smart farming and precision agri-culture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture.Scopus2-s2.0-8510941411

Applications of deep learning for mobile malware detection: A systematic literature review

For detecting and resolving the various types of malware, novel techniques are proposed, among which deep learning algorithms play a crucial role. Although there has been a lot of research on the development of DL-based mobile malware detection approaches, they were not reviewed in detail yet. This paper aims to identify, assess, and synthesize the reported articles related to the application of DL techniques for mobile malware detection. A Systematic Literature Review is performed in which we selected 40 journal articles for in-depth analysis. This SLR presents and categorizes these articles based on machine learning categories, data sources, DL algorithms, evaluation parameters & approaches, feature selection techniques, datasets, and DL implementation platforms. The study also highlights the challenges, proposed solutions, and future research directions on the use of DL in mobile malware detection. This study showed that Convolutional Neural Networks and Deep Neural Networks algorithms are the most used DL algorithms. API calls, Permissions, and System Calls are the most dominant features utilized. Keras and Tensorflow are the most popular platforms. Drebin and VirusShare are the most widely used datasets. Supervised learning and static features are the most preferred machine learning and data source categories. 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Scopus2-s2.0-8511772663

Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey

Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and of which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.Comment: Accepted for Publication at PROFES 202

Design of a Data Management Reference Architecture for Sustainable Agriculture

Effective and efficient data management is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture

Sentez Tabanlı Yazılım Mimarisi Tasarım Yaklaşımının Essence Çerçevesiyle Modellenmesi

Software architecture design is a pivotal yet a difficult phase in software development process. It is important to manage conflicting goals of the stakeholders and derive architectural abstractions from the relevant requirements. Moreover, it is significant to produce semantically rich artifacts based on the existing solution domain knowledge. Resulting architectural artifacts guides the rest of the software development process and facilitates planning. Recently the Essence framework has been proposed to provide an abstract and general view of software engineering on which software development methods and activities can be mapped. In this work, a mapping of the synthesis-based software architecture design activities to the Essence framework is presented. By doing so, these activities are explained using an abstract and general model of software engineering. Moreover, a state-based activity tracking mechanism for synthesis-based software architecture design activities is proposed. The lessons learnt about the Essence framework and the synthesis-based architecture design approach are reported