Azkario Rizky Pratama

Distributed Software Development (DSD) or Global Software Development (GSD), today GSD is popular among practicing software developers. This is because GSD has advantages such as faster lead times, a wider range of superior workforce, and increased project cost efficiency. However, GSD also has challenges, including the implementation of RCM. Implementing RCM in GSD requires special attention because GSD has a working model that is applied at different locations, times and cultures for each project team. Poor implementation of RCM can result in project failure. The success of implementing RCM is influenced by the Critical Challenge Factor (CCF) and Critical Success Factor (CSF). In this paper, we conducted a Systematic Literature review to identify frequently occurring CCFs, looking at what methods are frequently used in identifying CCFs from previous research. In this paper, we conducted a Systematic Literature Review to identify frequently occurring CCFs, looking at what methods are frequently used in identifying CCFs from previous research. The results of the review found that as many as 29% of research used Systematic Literature Review (SLR), and in 52% of research, SLR was combined with several other approaches (Interview, Questionnaire Surveys, Expert Suggestion, or Observation). 14% of studies used an interview approach, and 5% used questionnaire surveys only. It means that SLR is the approach most often used to identify CCF, whether SLR is used alone (without combining it with other approaches) or in combination with other approaches. Furthermore, CCF, with the highest intensity of appearance in each literature, examines the fields of Communication and Coordination, namely at 24%, Social Culture at 12%, Management and Knowledge Sharing, and Time Zone difference at 6%. The approach used in identifying CCF is very dependent on the research objectives and research object. Therefore, choosing the right approach is expected to help practitioners to identify CCF better so that risk mitigation plans from CCF can be formulated appropriately.

Byzantine fault-tolerant (BFT) web services provide critical integrity guarantees for distributed applications but face significant latency challenges that hinder interactive user experiences. We propose a novel two-layer architecture that addresses this fundamental tension between security and responsiveness in BFT systems. Our approach introduces a session-aware transaction buffer layer (Layer 2) that delivers immediate feedback to users through consensus simulation, while periodically committing batched operations to a fully Byzantine fault-tolerant consensus layer (Layer 1). By separating interactive operations from consensus finalization, our system achieves responsive user experiences of under 200ms, while maintaining strong BFT security guarantees. We demonstrate the efficacy of our architecture through a supply chain management implementation, where operators require both immediate feedback during multi-step workflows and tamper-proof record keeping. Our evaluation shows that our Layer 2 operations perform four times faster than the Layer 1 counterpart, while substantially preserving the end-to-end transaction integrity. Our approach enables BFT applications in domains previously considered impractical due to latency constraints, such as metaverse environments, where users require both responsive interaction and guaranteed state consistency.

This paper presents a comparative analysis of fingerprinting and machine learning techniques for bluetooth low energy (BLE)-based localization. Two fingerprinting algorithms, namely fingerprint feature extraction (FPFE) and Bayesian estimation (BE), along with various machine learning approaches including support vector regression (SVR), ensemble learning, and instance-based learning, are investigated. The selection of techniques depends on the availability of training data or the fingerprint database, explored in both ideal scenario and real-world scenario. In ideal scenario where the system administrator can collect fingerprint data through users’ devices, FPFE emerges as the preferred algorithm, achieving superior performance with a mean error of 0.50 m. In the context of real-world scenario, where data collection from multiple devices is limited, the system administrator may gather fingerprint data for localization using one or a few specific devices. Our experiments reveal that when there is a scarcity of fingerprint data, BE and SVR exhibit acceptable performance, reaching a mean error of 1.785 m and 1.965 m, respectively.

Counterfeit products have become a pervasive problem in the global marketplace, necessitating effective strategies to protect both consumers and brands. This study examines the role of cybersecurity in addressing counterfeiting issues, specifically focusing on a multi-level grayscale watermark-based authentication system. The system comprises a generator responsible for creating a secure 2D code, and an authenticator designed to extract watermark information and verify product authenticity. To authenticate the secure 2D code, we propose various features, including the analysis of the spatial domain, frequency domain, and grayscale watermark distribution. Furthermore, we emphasize the importance of selecting appropriate interpolation methods to enhance counterfeit detection. Our proposed approach demonstrates remarkable performance, achieving precision, recall, and specificities surpassing 84.8%, 83.33%, and 84.5%, respectively, across different datasets.

Counterfeiting has been one of the crimes of the 21st century. One of the methods to overcome product counterfeiting is a copy detection pattern (CDP) stamped on the product. CDP is a copy-sensitive pattern that leads to quality degradation of the pattern after the print and scan process. The amount of information loss is used to distinguish between original and fake CDPs. This paper proposed a CDP estimation model based on the convolutional neural network (CNN), namely, CDP-CNN. The CDP-CNN addresses the spatial dependency of the image patch. Thus, it should be better than the state-of-the-art model that uses a multi-layer perceptron (MLP) architecture. The proposed model had an estimation bit error rate (BER) of 9.91% on the batch estimation method. The error rate was 9% lower than the previous method that used an autoencoder MLP model. The proposed model also had a lower number of parameters compared to the previous method. The effect of preprocessing, namely the use of an unsharp mask, was tested using a statistical testing method. The effect of preprocessing had no significant difference except in the batch estimation scheme where the unsharp mask filter reduced the error rate by at least 0.5%. In addition, the proposed model was also used for the authentication method. The authentication using the estimation model had a good separation distribution to distinguish the fake and original CDPs. Thus, the CDP can still be used as the authentication method with reliable performance. It helps anti-counterfeiting on product distribution and reduces negative impacts on various sectors of the economy.

Precise fine-grained office occupancy detection can be exploited for energy savings in buildings. Based on such information one can optimally regulate lighting and climatization based on the actual presence and absence of users. Conventional approaches are based on movement detection, which are cheap and easy to deploy, but are imprecise and offer coarse information. We propose a power monitoring system as a source of occupancy information.The approach is based on sub-metering at the level of room circuit breakers. The proposed method tackles the problem of indoor, office occupancy detection based on statistical approaches based on power consumption information thus contributing to building context awareness which, in turn, is a crucial stepping stone for energy efficient buildings. The key advantage of the proposed approach is to be low intrusive, especially when compared with image or tag based solutions, while still being sufficiently precise in its classification. Such classification is based on nearest neighbors and neural networks machine learning approaches, both in sequential and non-sequential implementations. To test the viability, precision, and saving potential of the proposed approach we deploy in an actual office over several months. We find that the room-level sub- metering can acquire precise, fine-grained occupancy context for up to three people, with averaged kappa measures of 93-95% using either the nearest neighbors or neural networks based approaches.

Indoor occupancy provides information about human occupation in the closed space, most notably, office and residential buildings. This information is useful in dwindling unnecessary energy usage, such as consumption in unoccupied spaces or energy-wasting due to unnecessarily active appliances. We present an empirical experiment on office occupancy detection using simple office sensors. We choose generic power meters and mobile phones. First, we classify beacon signals received by mobile phones into a room location. A workspace map is assumed to be available to facilitate the mapping between room locations and the occupancy state of users' workspace. Second, we infer the individual occupancy state utilizing the aggregated electricity consumption of occupant-related devices (i.e., monitors) in shared offices. The later solution helps to keep costs and intrusiveness level low compared to deploying a power meter for each device or user. We experiment in an work environment with two shared offices, a personal office, and a social corner involving five volunteers. Given the acquired data, three techniques based on machine learning, optimization, and probabilistic approach are implemented and compared to evaluate their performance. The results indicate that localization and occupancy based on beaconing works best for three of the five volunteers, reaching 95% F-measure. Further findings shows that occupancy inference based on the aggregated power consumption performs well for the four volunteers when using Decision Tree classification, reaching more than 90% F-measure. Our effort on the fusion of two modalities gives a positive result for all five volunteers, ranging from 92% to 99% F-measure.

Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach.