Miracle-dz/UniFAR-FTUs huggingface dataset:
The Order of Circular Business Models: An Empirical Taxonomy Using Cluster Analysis

In the wake of increasing demands for raw materials and the changing climate, the circular economy concept has recently gained traction in academia, business and policy making. A central constituent in order realize it, i.e. to shift to a system in which environmental impact is decoupled from economic growth by circulating products, components and materials at their highest economic and resource value at all times, is the design and implementation of circular business models. Hence, over the last years, academics and practitioners alike have created tools and frameworks that support firms in coming up with new or more effective models. Further, emulating the evolution of general business model literature, researchers have started to propose circular business model definitions and classifications in order to consolidate the existing work and establish common ground. However, a clear understanding of what a circular business model really constitutes is still missing and a careful review of the existing literature reveals that the proposed classifications are either lacking methodological transparency or being purely conceptually derived. Consequently, from a positivistic stance, there is no basis for wider generalization and mid-range theory development. To address this gap, the thesis at hand constructs a conceptually grounded and empirically derived circular business model taxonomy. Following existing approaches to taxonomy development and building upon an extensive literature review as well as empirical data, it first creates an integrative framework on which basis circular business models can be described. In the process of its development, also a binary-coded matrix expressing the defining business model characteristics of 100 randomly selected firms is generated. This data is subsequently analyzed using hierarchical and non-hierarchical cluster analysis techniques. The final cluster solution reveals a set of seven major circular business model types which are further characterized on the basis of descriptive statistics and representative case examples. Split-sampling and the application of different cluster algorithms indicate that the solution is stable and a silhouette coefficient of 0.53 strengthens its internal validity. Finally, a comparison with existing classifications demonstrates the taxonomy’s usefulness. While not generating a definitive answer, the proposed circular business model taxonomy provides a novel perspective to the question of what types of circular business models exist and how they can be characterized. It offers a stepping stone for mid-range theory development and in combination with the review of 116 circular business model publications gives a comprehensive overview of the phenomena’s current manifestations. From a practical viewpoint, the thesis’ findings provide useful insights into the structure of circular business models thereby serving as a source of inspiration for the development of new models or as a tool for the strategic positioning of existing ones.
A Novel Reduced Components Model Predictive Controlled Multilevel Inverter for Grid-Tied Applications

This paper presents an improved single-phase Multilevel Inverter (MLI) which is conceptualized to reduce power switches along with separate DC voltage sources. Compared with recent modular topologies, the proposed MLI employs a reduced number of components. The proposed inverter consists of a combination of two circuits, i.e., the level generation and polarity generation parts. The level generation part is used to synthesize different output voltage levels, while the polarity inversion is performed by a~conventional H-bridge circuit. The performance of the proposed topology has been studied using s single-phase seven-level inverter, which utilizes seven power switches and three independent DC voltage sources. Model Predictive Control (MPC) is applied to inject a sinusoidal current into the utility grid which exhibits low Total Harmonic Distortion (THD). Tests, including a~change in grid current amplitude as well as operation under variation in Power Factor (PF), have been performed to validate the good performance obtained using MPC. The effectiveness of the proposed seven-level inverter has been verified theoretically using MATLAB Simulink. In addition, Real-Time (RT) validation using the dSPACE-CP1103 has been performed to confirm the system performance and system operation using digital platforms. Simulation and RT results show improved THD at 1.23% of injected current.
STEM 101: intro to tomorrow's jobs

This article provides an overview of STEM work, analyzing nearly 100 occupations from a list created by a committee comprising several federal agencies. The first section of the article offers a brief description of the life and physical sciences, computer science, engineering, and mathematics fields. The second section includes data showing selected STEM occupations with the most employment and projected job openings and growth. The third section discusses the rewards and challenges of STEM work. The fourth section describes how to prepare for a career in a STEM field. Resources for more information are listed at the end of the article.
A Low-Power Reconfigurable Narrowband/Wideband LNA for Cognitive Radio-Wireless Sensor Network

This article proposes a low-power reconfigurable multimode low-noise amplifier (LNA) that can be configured for multiple narrowband as well as wideband operations while providing simultaneous input matching and output load reconfigurabilty. A component sharing technique and a component-Q-aware impedance-matching technique are introduced in the proposed LNA circuit. The proposed LNA, implemented in a United Microelectronics Corporation (UMC) 0.18- $\mu \text{m}$ CMOS process, can operate in a narrowband mode with center frequencies around 1.8, 2.1, and 2.4 GHz as well as in a wideband mode (2–5 GHz). Measured results show that the LNA has achieved 14.1, 14.5, and 14.1 dB of voltage gain, 8.5, 8.2, and 8.7 dB of noise figure (NF) in 1.8-, 2.1-, and 2.4-GHz modes, respectively. In the wideband mode, the measured gain and the NF are 14.5 and 7.8 dB, respectively. The LNA consumes 2 and 3.8 mW of power from the 1.8-V supply in the narrowband and wideband modes, respectively.
AutoSeC: An Integrated Middleware Framework for Dynamic Service Brokering

In this paper, we integrate components for (a) resource provisioning and (b)information collection that allows for cost-effective enforcement of application QoS while tolerating imprecision of system state information. We discuss a family of policies for each component and provide mechanisms for dynamically selecting the appropriate combinations of the policy. Our objective is to select suitable management policies that maximizes the number of concurrent supported users while minimizing the overhead needed to ensure QoS enforcement for these users. Our performance results indicate that the “best” combination of policies is dependent on existing system conditions. However, we generally observe that the highly adaptive dynamic range-based information collection mechanisms exhibit superior performance under most system conditions and user workloads. All of these observations are applied into our integrated middleware framework AutoSeC (Automatic Service Composition). AutoSeC provides support for dynamic service brokering that ensures effective utilization of system resources in a distributed environment.
Witty : randomization of time-limited video sharing and content creation inside a credit-based platform

of Master’s Thesis of Academic Year 2014! Witty: Randomization of Time-Limited Video Sharing and Content Creation Inside a Credit-Based Platform! Category: Design! Abstract! User-generated content has evolved rapidly in the last few years, allowing users to share and upload content on the go, but it has generated many distribution problems. Research shows that only a small amount of contents are accountable for most of the views inside the existing platforms. Changing some of the traditional features on these platforms could change the way video sharing networks work. This paper proposes the concept of a platform that uses erasable content and randomness in a credit-based video sharing platform. This paper includes the research information about each individual feature of the platforms as well as the planning and development of a woking prototype and its evaluation. The results from this evaluation demonstrate that this platform has the potential of success, however more testing has to be done before it can be confirmed that the platform presented as “Witty” can be successful.
A multi-algorithm data mining classification approach for bank fraudulent transactions

This paper proposes a multi-algorithm strategy for card fraud detection. Various techniques in data mining have been used to develop fraud detection models; it was however observed that existing works produced outputs with false positives that wrongly classified legitimate transactions as fraudulent in some instances; thereby raising false alarms, mismanaged resources and forfeit customers’ trust. This work was therefore designed to develop a hybridized model using an existing technique Density-Based Spatial Clustering of Applications with Noise (DBSCAN) combined with a rule base algorithm to reinforce the accuracy of the existing technique. The DBSCAN algorithm combined with Rule base algorithm gave a better card fraud prediction accuracy over the existing DBSCAN algorithm when used alone. Key words: Card fraud detection, density-based spatial clustering of applications with noise (DBSCAN), rule base algorithm, data mining.
Containerization for HPC in the Cloud: Docker vs Singularity

In recent years, ‘containerization’, also known as ‘light-weight virtual-ization’, has arguably been one of the dominating buzzwords, both in industry and academic research. While scalability has been the main driving force in industry, reproducibility is often stated as the main beneﬁt of containers in scientiﬁc High Performance Computing (HPC). Docker is currently the most widely deployed framework, ideally suited for micro-service architecture. Singularity is a framework that aims to provide mobility of compute with a focus on bare-metal HPC cluster systems. In earlier studies the performance of different light-weight virtualization frameworks when deployed on bare metal HPC systems has been evaluated and Singularity was mostly found to be superior to others in it’s application niche. However, performing HPC applications in the cloud has recently become a viable alternative to dedicated bare-metal HPC clusters. This results in running light-weight virtualization frameworks on virtual machines. The aim of this study was to benchmark the performance of Singularity when run on virtual machines in the cloud compared to a setup using ’Docker’. Adhering to earlier studies, performance metrics for compute, I/O, memory, and network throughput/latency were established.
Intelligent Energy Management for Plug-in Hybrid Electric Bus with Limited State Space

Tabular Q-learning (QL) can be easily implemented into a controller to realize self-learning energy management control of a plug-in hybrid electric bus (PHEB). However, the “curse of dimensionality” problem is difficult to avoid, as the design space is huge. This paper proposes a QL-PMP algorithm (QL and Pontryagin minimum principle (PMP)) to address the problem. The main novelty is that the difference between the feedback SOC (state of charge) and the reference SOC is exclusively designed as state, and then a limited state space with 50 rows and 25 columns is proposed. The off-line training process shows that the limited state space is reasonable and adequate for the self-learning; the Hardware-in-Loop (HIL) simulation results show that the QL-PMP strategy can be implemented into a controller to realize real-time control, and can on average improve the fuel economy by 20.42%, compared to the charge depleting–charge sustaining (CDCS) strategy.
Decoupling coding habits from functionality for effective binary authorship attribution

Binary authorship attribution refers to the process of identifying the author of a given anonymous binary ﬁle based on stylistic characteristics. It aims to automate the laborious and error-prone reverse engineering task of discovering information related to the author(s) of a binary code. Existing works typically employ machine learning methods to extract features that are unique for each author and subsequently match them against a given binary to identify the author. However, most existing works share a common critical limitation, i.e., they cannot distinguish between features representing program functionality and those representing authorship (e.g., authors’ coding habits). Such distinction is crucial for effective authorship attribution because what is unique in a particular binary may be attributed to either author, compiler, or function. In this study, we present B IN A UTHOR a system capable of decoupling program functionality from authors’ coding habits in binary code. To capture coding habits, B IN A UTHOR leverages a set of features that are based on collections of functionality-independent choices made by authors during coding. Our evaluation demonstrates that B IN A UTHOR outperforms existing methods in several aspects. First, it successfully attributes a larger number of authors with a signiﬁcantly higher accuracy (around 90% ) based on the large datasets extracted from selected open-source C++ projects in GitHub, Google Code Jam events, Planet Source Code contests, and several programming projects. Second, B IN A UTHOR is more robust than previous methods; there is no signiﬁcant drop in accuracy when the code is subjected to refactoring techniques, simple obfuscation, and processed with different compilers. Finally, decoupling authorship from functionality allows us to apply B IN A UTHOR to real malware binaries ( Citadel , Zeus , Stuxnet , Flame , Bunny , and Babar ) to automatically generate evidence on similar coding habits.
Comprehension Monitoring In First And Second Language Reading

This paper presents a study of comprehension monitoring of Chinese lower intermediate EFL (English as a foreign language) learners in first language (L1) and foreign language (FL) reading. It investigates the outcome of comprehension monitoring in L1 and FL reading, as well as the separate contributions of L1 reading proficiency and FL comprehension monitoring to FL reading proficiency. 126 students in the vocational department of a foreign languages university in China participated in the study. They completed a Chinese and an English error detection task, in which they were asked to read two texts in each language embedded with external and internal inconsistencies. Measures of Chinese and English reading proficiency were obtained from the mid-term examinations. Four of the 126 readers also participated in retrospective interviews in which they explained the basis on which they had identified the textual inconsistencies and also offered possible corrections. Quantitative data from the two error detection tasks and the reading scores were analyzed using
Guidepost: Scalable MU-MIMO User Selection via Indirect Channel Orthogonality Evaluation

Multi-user MIMO (MU-MIMO) can serve multiple users concurrently, and is the key technology to enable ultra-high-speed wireless access. However, in practice MU-MIMO networks are far from their full potential due to the poor scalability problem, including high computational complexity at PHY layer and large-overhead channel contention at MAC layer. Moreover, cross-cell interference among multiple MU-MIMO cells also counteracts network performance. In this paper, we perform a systematic study on MU-MIMO and propose a fully scalable MU-MIMO user selection protocol called Guidepost. In contrast with previous works, Guidepost builds on a novel principle of indirection channel orthogonality evaluation, so as to decouple and simplify the complicated computational/contention interaction among users. Based on the principle, Guidepost first achieves scalable MU-MIMO user selection with only linear computational complexity. Second, Guidepost realizes distributed user selection through a two-dimensional prioritized contention mechanism, which can single out the best concurrent users efficiently by utilizing both the time and frequency domain resources. Third, Guidepost incorporates a lightweight AP-assisted contention mechanism to handle cross-cell interference in distributed MU-MIMO (netMIMO) where users are widely distributed and cannot sense each other. Software-radio based implementation and experimentation show that Guidepost significantly outperforms state-of-the-art methods under various traffic patterns and node mobility.
Cochlea modelling and its application to speech processing

Models of the cochlea provide a valuable tool for both better understanding its mechanics and also as an inspiration for many speech processing algorithms. Realistic modelling of the cochlea can be computationally demanding, however, which limits its applicability in signal processing applications. To mitigate this issue, an eﬃcient numerical method has been proposed for performing time domain simulations, based on a nonlinear state space formulation [1]. This model has then been contrasted with another type of cochlear model, that is established from a cascade of digital ﬁlters. A comparison of the responses from these two models has been conducted, in terms of their realism in simulating the measured nonlinear cochlear response to single tones and pairs of tones. Guided by these results, the ﬁlter cascade model is chosen for subsequent signal processing applications because it is signiﬁcantly more eﬃcient than the state space model, while still producing realistic responses. Using this nonlinear ﬁlter cascade model as a front-end, two speech processing tasks have been investigated: voice activity detection and supervised speech separation. Both tasks are tackled within a machine learning framework, in which a neural network is trained to reproduce target outputs. The results are compared with those using a number of other simpler auditory-inspired analysis methods. Simulation results show that although the nonlinear ﬁlter cascade model can be more eﬀective in many testing scenarios, its relative advantage against other analysis methods is small. The incorporation of temporal context information and network structure engineering are found to be more important in improving the performance of these tasks. Once a suitable context expansion strategy has been selected, the diﬀerence between various front-end processing methods considered is marginal.
EdgeDB: An Efficient Time-Series Database for Edge Computing

Massive time-series data streams from high-sampling-frequency sensors in Internet of Things (IoT) can overwhelm the networks connecting the sensors to centralized clouds. Thus, edge computing servers have to be introduced to locally store and analyze growing time-series data. Unfortunately, conventional time-series databases exhibit low efficiency on edge nodes with limited resources for both computation and storage. In this paper, we propose a highly efficient time-series database, called EdgeDB, to fully utilize the capacity of the edge nodes. EdgeDB effectively improves the performances of both inserting and retrieving data from ingest streams by efficiently merging multiple streams and optimizing the storage data structure concurrently. EdgeDB first compactly organizes multiple online streams into a tablet within a time window and embeds predefined aggregate query results together. EdgeDB adopts Time Partitioned Elastic Index (TPEI) to build indexing on all tablets, enhancing the time-range query performance while reducing the memory usage by optimizing the indexing storage. EdgeDB further develops Time Merged Tree (TMTree) to combine a set of tablets into a large one, significantly boosting the write throughput and potentially strengthening the performance of inter-tablet query. Extensive experiments based on real-world datasets show that, compared with the state-of-the-art time-series database BTrDB, EdgeDB achieves performance improvements of up to $2.2\times $ in insert throughput, $3.6\times $ in write throughput, and 67% in query latency with lower memory consumption.
The challenges in implementing future interoperable joint C4ISR systems

The unprecedented scale of ultra large scale (ULS) systems spans into various dimensions like lines of code, amount of information processed, number of interdependent hardware and computational components, number of people involved etc. The characteristics of ULS systems due to their scale pose different challenges which could only be addressed by a multi‐disciplinary research rather than the traditional software engineering approaches. In this paper, we focused on proposing solutions against challenges in implementing the future interoperable joint Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) systems. For this purpose, we examined the main research topics to address the challenges in ULS systems. We discussed two main capabilities to enable the vision of future interoperable joint C4ISR. One of these capabilities is the effective and secure use of information across components of the system, while the other one is real‐time fusion of the available information with the local context.
A Hardware and Software Task-Scheduling Framework Based on CPU+FPGA Heterogeneous Architecture in Edge Computing

Real-time performance is the primary requirement for edge computing systems. However, with the surge in data volume and the growing demand for computing power, a computing framework consisting solely of CPUs is no longer competent. As a result, CPU+ heterogeneous architecture using accelerators to improve edge computing systems’ computing capacity has received great attention. The type of accelerators determines the performance of the edge computing system largely. The accelerators include Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA). FPGAs with its reconfigurability and high energy efficiency are widely used in many edge computing scenarios. Nontheless, the performance depends also on the scheduling efficiency between software tasks on CPUs and hardware tasks on FPGAs. Unfortunately, the existing strategies have not fully exploited the differences between hardware and software tasks, thus resulting in low scheduling efficiency. This paper proposes a task scheduling framework on the Dynamic Partial Reconfiguration (DPR) platform. We take full account of the characteristics of task switching overhead and predictable execution time of hardware tasks in DPR, and reduce the number of task-switching times and active tasks in the system, thus improving the scheduling efficiency. We conduct a set of experiments on the Zynq platform to verify the proposed framework. Experimental results demonstrate that when the execution time of the accelerator exceeds the reconfiguration cost by an order of magnitude, the efficiencies of all the cases are more than 98%, and the efficiencies can reach 90%-98% in the same order of magnitude.