Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing cohort aggregation to minimise bias in pseudo-panels

Pseudo-panels allow estimation of panel models when only repeated cross-sections are available. This involves grouping individuals into cohorts and using the cohort means as if they are observations in a genuine panel. Their practical use is constrained by a lack of consensus on how the pseudo-panels should be formed, particularly to address potential sampling error bias. We show that grouping can also create substantial aggregation bias, calling into question how well pseudo-panels can mimic panel estimates. We create two metrics for assessing the grouping process, one for each potential source of bias. If both metrics are above certain recommended values, the biases from aggregation and sampling error are minimised, meaning results can be interpreted as if they were from genuine panels.
CUBIC Local Loss Recovery vs. BBR on (Satellite) Internet Paths

Paths with high delay and packet loss are problematic for transport protocols, especially when loss-based congestion control algorithms are used. Our topology of interest is a geostationary satellite path (round trip time approx. 600 ms) with additional packet loss, which could be caused by a lossy wireless local area network. In geostationary satellite networks, Performance Enhancing Proxies split TCP connections to improve performance, but this is not possible in case of encrypted transport protocols (VPNs, QUIC). This motivates the use of local loss recovery, i.e., avoiding packet loss on specific path segments. We briefly discuss related concepts, architectures, and protocols. We then evaluate local loss recovery using a TCP tunnel on the lossy link. By means of a simple emulation setup, we demonstrate that local loss recovery has great performance benefits if CUBIC is used. It is also shown that BBR works well for high-delay and lossy paths even without local loss recovery.
Multisource Indoor Positioning System Based on Edge Computing

Due to its significance in numerous applications, indoor positioning has garnered increasing interest. The WiFi fingerprinting method and pedestrian dead reckoning approach are the most popular methods for indoor positioning. However, both methods have certain limitations in terms of accuracy and response time, rendering them ineffective for some applications. To overcome these problems, we propose a system based on edge computing that combines these two methods. The results of our experiments show that our proposed method outperforms standalone methods by giving an average positioning error of 0.75 m in just 0.7 seconds.
Building Trust-based Dynamic IT Capabilities: The Case of a State-owned Enterprise in South Africa

The literature on trust with IT units in organisations tends to emphasise interpersonal trust, inter-organisational trust, and trust between people and technology. Little is known about trust in IT intra-organisational relationships and its role in value co-creation. A dynamic capability lens is applied as a lens in this interpretive case study of a state-owned enterprise to understand how IT intra-organisational trust relationships promote and constrain the development of IT-enabled capabilities. The findings suggest that the IT unit's culture, reputation and strategic decision-making expertise are important factors in building trust-based IT capabilities. This study adds to the literature on how IT units can foster trust-based dynamic IT capabilities as a source of value co-creation. Future research can investigate the role of IT personnel and leadership expertise in fostering trust-based dynamic IT capabilities.
Exploring both Individuality and Cooperation for Air-Ground Spatial Crowdsourcing by Multi-Agent Deep Reinforcement Learning

Spatial crowdsourcing (SC) has proven as a promising paradigm to employ human workers to collect data from diverse Point-of-Interests (PoIs) in a given area. Different from using human participants, we propose a novel air-ground SC scenario to fully take advantage of benefits brought by unmanned vehicles (UVs), including unmanned aerial vehicles (UAVs) with controllable high mobility and unmanned ground vehicles (UGVs) with abundant sensing resources. The objective is to maximize the amount of collected data, geographical fairness among all PoIs, and minimize the data loss and energy consumption, integrated as one single metric called "efficiency". We explicitly explore both individuality and cooperation natures of UAVs and UGVs by proposing a multi-agent deep reinforcement learning (MADRL) framework called "h/i-MADRL". Compatible with all multi-agent actor-critic methods, h/i-MADRL adds two novel plug-in modules: (a) h-CoPO, which models the cooperation preference among heterogeneous UAVs and UGVs; and (b) i-EOI, which extracts the UV’s individuality and encourages a better spatial division of work by adding intrinsic reward. Extensive experimental results on two real-world datasets on Purdue and NCSU campuses confirm that h/i-MADRL achieves a better exploration of both individuality and cooperation simultaneously, resulting in a better performance in terms of efficiency compared with five baselines.
Axial Flux Permanent Magnet Vernier Machine with Single-wound Dual-stator and Spoke Permanent Magnet Rotor for Electric Vehicle In-wheel Traction

In this paper, a dual-stator axial flux permanent magnet machine with a spoke-type rotor for electric vehicle applications is investigated. The rotor PMs are positioned in a spoke-type field-concentrating arrangement, which provides high flux concentration. Two stators-one stator configuration with double-layer wound coils and another without winding-are employed. The usage of single-layer wound coils is possible in this topology, and by using this type of winding it is possible to implement advanced in-slot cooling systems. Analytical and electromagnetic finite element analysis (FEA) studies considering its geometrical and electrical properties have been performed on this machine. The performance indices for the traction application based on the analytically calculated torque-speed characteristic, and FEA calculated torque versus torque angle have been assessed, and the results indicate that by using the proposed flux weakening methods, this machine can perform the flux weakening and operate in the constant power region of the torque-speed characteristic.
SPA: Economical and Workload-Driven Indexing for Data Analytics in the Cloud

Selective queries are not uncommon in large-scale data analytics, for example, when drilling down into a specific customer in a dashboard. Traditionally, selective queries are accelerated by creating secondary indexes. However, because of their large size, expensive maintenance, and difficulty to tune and automate, indexes are typically not used in modern cloud data warehouses or data lakes. Instead, such systems rely mostly on full table scans and lightweight optimizations like min/max filtering, whose effectiveness depends heavily on the data layout and value distributions.We propose SPA as the vision for automatically optimizing selective queries for immutable copy-on-write data formats. SPA adaptively indexes subsets of the data in an incremental and workload-driven manner. It makes fine-grained decisions and continuously monitors their benefit, dynamically allocating an optimization budget in a way that bounds the additional cost of indexing. Furthermore, it guarantees a performance improvement in the cases where indexes—potentially partial ones—prove to be beneficial. When indexes lose their benefit due to a shifting workload, they are gradually deconstructed in favor of optimizations that accommodate recent trends. As SPA does not require information about updates performed on the data, it can also be employed as an accelerator for systems that do not control the data, e.g., in cloud data lakes.
High-Linearity 76-81 GHz Radar Receiver with an Intermodulation Distortion Cancellation and High-Power Limiter

A highly linear 76–81 GHz direct conversion receiver has been fabricated in a 16 nm FinFet CMOS process for phased array radar applications. The receiver includes a Low Noise Amplifier (LNA), a semi active mixer, a passive filter and a baseband amplifier. A third order intermodulation distortion cancellation technique is implemented in the LNA and it is used to compensate for distortions in the entire receiver. The peak gain of the receiver is 36 dB with Input IP3 of -8.1 dBm and Noise Figure (NF) of 5.5 dB. The total power consumption of the receiver is 68 mW. To protect the LNA from high input power levels that affect the reliability of the transistors, an innovative limiter circuit is suggested to monitor the operating point of the first stage of the LNA, whereas the mmW signal path is not affected from this monitoring circuitry at tolerable power levels.
Interest-based Routing in Opportunistic Networks: Evaluating IRP against SCORP

We present an evaluation of the Interest-based Routing Protocol (IRP), a routing strategy for Opportunistic Networks based on learning about users’ real-time changing interests in different topics. We compare IRP with Social-aware Content-based Opportunistic Routing Protocol (SCORP), which is also based on users’ interest, and Epidemic. We conducted a set of experiments using real traces extracted during an academic year at MIT, and with users’ real-time changing interest in receiving messages from specific topics. The results obtained show that IRP delivers more messages than the other algorithms, and also has better results in Messages Relayed, Buffer Occupancy, and Network Overhead.
An Intellectual Diffused Configuration for High-Level Edge Network Elasticity

The Network Distribution System is a rapid evolution of the Internet of Everything (IOE). This system enabled the deployment of new solutions that reduce low-latency edge applications close to cloud servers and resilient edge applications to edge networks close to IoT devices. The network edge must make intelligent decisions that require fast response times and a wide range of edge application requirements. It also requires a heterogeneous dynamic edge network with constrained resources. Increasing the edge network capacity level increases the frequency and latency of many edge devices such as smart cars, robots, and smart IoT devices. With dynamic edge response time, applications automatically adapt to resilience requirements. This paper describes the role of edge networks. It also employs a lightweight, self-adapting, decentralised mechanism for deploying edge applications. It explains optimization methods for edge applications based on IoT technology. Finally, this paper increases the performance of smart waste management system by using the elasticity by reducing the latency of edge network.
Physical Design Considerations for Three-Level Neutral-Point Clamped DC-DC Converters Using Discrete SiC MOSFETs

This article examines electrical, thermal, and packaging considerations for realizing DC-DC converters based on neutral-point clamped (NPC) phase legs. Four variations of a three-level NPC leg are contrasted. These variants cover different device packaging technologies, viz., surface mount, and through-hole devices. The scope of variants is limited to systems with liquid cooling and converters with a cuboid shape of a limited z-axis dimension. Each variant and gate driver are designed using Altium Designer, assuming certain converter specifications. Printed circuit board (PCB) parameters are extracted using finite-element analysis (FEA) in ANSYS Q3D. Electrical switching behavior of the MOSFETs under each variant is evaluated using double-pulse testing in LTSpice. Thermal interfacing and its associated consequences on device capability utilization, manufacturing challenges, and cost are also discussed. A comparison showing the merits and limitations of all four variations based on device technology usage—the basis of comparison being volume, thermal management, cost, parasitic circuit elements, and switching behavior is presented. Furthermore, the design consideration trade-offs for building a converter are listed to achieve the application priorities. The list, along with the comparison, aids in selecting one of the four variants or their derivatives for designing converters that fall under the defined scope.
COME: Learning to Coordinate Crowdsourcing and Regular Couriers for Offline Delivery During Online Mega Sale Days

Crowd logistics, as an emerging delivery paradigm, provides a cost-efficient way of leveraging crowdsourcing couriers to help express enterprises to match the surging delivery demands that are hard to be addressed by regular couriers only during online mega sale days. However, it is a challenging problem how to recruit an appropriate number of crowdsourcing couriers and assign an appropriate number of parcels to them and regular couriers, as many practical issues need to be considered, such as the dynamic competitive crowdsourcing market, the turnover of crowdsourcing couriers, and unique workload patterns of regular couriers. We design a crowdsourcing-assisted express system called COME to coordinate crowdsourcing and regular couriers for minimizing the overall cost of labor payment and parcel backlog. In COME, we design an Opponent-Aware Reinforcement Learning model to learn the recruitment difficulty in a competitive crowdsourcing market to make an appropriate recruitment plan, and design a four-staged approach to make an appropriate parcel assignment plan, which can address not only the dynamic recruitment difficulty but also the dynamic number of couriers. We have implemented and deployed COME on a real-world crowdsourcing-assisted express system in China involving 1358 delivery stations over 145 cities, and extensively evaluated it with a four-year real-world dataset, demonstrating its great advantage over other alternative solutions and showing high feasibility and generality.
The State of Secure Coding Practice: Small Organisations and “Lone, Rogue Coders”

Software security is a rapidly developing problem. Malware, ransomware and spyware routinely leverage vulnerabilities in software to gain access to systems, escalate privileges and run adversarial code. One approach to solving this issue is to use secure software methods, which attempt to guide organisations in improving their software assurance. However, these methods implicitly assume the presence of substantial resources deployed in a compliance-mandated environment. The distinct and often limited environment in which small organisations, independent teams and lone coders operate is not considered. Advice for software security in small teams is almost absent from the literature, as is a way to measure the levels of secure coding in such teams. In order to address this problem, we must begin by understanding it. As part of the analysis of a large survey on current software security practice, we examined the current software security practices of small and open source organisations, and of lone and non-company developers. We present our results in this paper. We hope that they will facilitate the targeting of security advice to these neglected developer categories.
SDN-Enabled Distributed Access Architecture Cable Networks

Cable networks are embracing Distributed Access Architectures (DAA) that push traditionally centralized network functions to the network edge. While this shift offers higher data rates, it complicates the management and configuration of the network by having functions distributed in remote nodes. Separately, SDN has evolved for enabling programmable networks of distributed switches, managed in a logically centralized manner. This paper presents an evolutionary path for SDN-based cable DAA that can overcome the aforementioned challenges and support new network services. We present an SDN-DAA architecture, implemented in a real DAA remote device, and evaluated using Mininet to demonstrate the operational benefits.
An Efficient Spine Segmentation Method

The spine is the most complex load-bearing structure in the human body, and herniated discs, spinal stenosis, and degenerative discs are common spinal disorders. MRI is an effective imaging method in medicine, but the identification and quantitative analysis of lesions require physician judgment, which is not only a huge workload but also carries the subjective judgment of physicians, and such drawbacks can be solved by using image segmentation technology. In this paper, we propose an efficient spine segmentation method consisting of selective preprocessing and post-processing and an improved UNET network structure. In the selective pre-post processing, meaningful parts of the MRI are selected for random input, and the selected parts are effectively restored back to the original size of the segmented image. In the improved UNET network, differing from the traditional UNET structure, the perceptual field of the image input is increased by using inflated convolution, and the attention mechanism is added in the up-sampling and down-sampling end parts for better filtering of features. The experimental results show that our method outperforms the traditional method by substantially reducing the training elapsed time and performing well in terms of the accuracy of the model.
Detection of Situational Context From Minimal Sensor Modality of A Smartphone Using Machine Learning Algorithm

Early detection and continuous monitoring can help reduce the complexity of treatment and recovery. For this purpose, many modern technologies are being used like smart wearable devices to make the diagnosis of different types of human diseases and automated tutoring systems. There is a vast improvement in the sector of human healthcare and education delivery using artificial intelligence (AI). For these AI algorithms, there can be high error rates if situational contexts are ignored. Currently, there is no automated approach to detect situational context. In this work, we propose a novel approach to automatically detect situational context with a smartphone context detection app using AI from minimal sensor modality. We begin the process by converting a few sensor data from the smartphone app to a multitude of axes, then determine situational context from these axes by using a machine learning algorithm. At first, we evaluated $k$-means algorithm performance on the converted data and grouped them into different clusters according to the contexts. However, the $k$-means algorithm has many challenges that negatively affect its clustering performance. For this reason, to automatically detect the situational contexts more accurately we have performed different machine learning (ML) algorithms to differentiate their characteristic parameters and attributes. To train and test ML models, 145 features were extracted from the dataset. In our case, we have used a dataset with 53,679 distinct values to evaluate the performance of different algorithms in detecting five situational contexts of the users. Experimental result shows that the accuracy of the Support Vector Machine, Random Forest, Artificial Neural Network, and Decision Tree Classifiers are 95%, 99%, 97%, and 98% respectively. The most effective classifier overall is Random Forest. This preliminary work shows the feasibility of detecting situational context automatically from a few sensor data collected from the smartphone app by converting the sensor data to multiple axes and applying a machine learning algorithm.
Narrowband IoT Network Self Localization

This article proposes a self-localization method for narrowband internet of things (NB-IoT) networks. The proposed system uses the received signal strength indicator (RSSI) with a trilateration algorithm to determine the location of NB-IoT nodes within indoor environments. The adopted path loss model for the indoor environment is in accordance with the fifth-generation (5G) millimeter wave (mm-Wave) standard. The pro-posed method eliminates the need for additional infrastructure or external references, making it efficient and cost-effective. Simulation results are presented to corroborate the accuracy of the proposed technique, and to investigate the impact of different system and channel parameters on the overall performance. Reported results reveal the accuracy of the developed system where an average positioning error of less than 0.2 m is achieved.
Sustainability of an Electric Arc Flash at a Voltage of 240 Vac and 150 Vdc

The purpose of this paper is to present arc flash laboratory results related to the occurrence of an electric arc flash and the protection offered to workers during arc flash event. The goal was to carry out laboratory tests in order to evaluate the sustainability of an electric arc flash at a single phase voltage of 240 Vac, and the adequacy of the choice of the formula for evaluating the incident energy at 150 Vdc compared to the values obtained in the laboratory. For alternating current, there is a discrepancy according to the applicable standard for the same task. This discrepancy results in the application of different safety measures. Preliminary research tends to support the thesis of a non-sustainability of the electric arc flash at 240 Vac, whereas the use of the formula employed for the direct current would be far too conservative compared to laboratory tests. Following confirmation of the preliminary results, it would be possible to review the protection required during high-risk dc tasks and to rule on the dangerousness of several activities. Doing so could eliminate the need to wear PPE, or reduce the necessary protection associated with risky procedures.
305-GHz Cascode Power Amplifier Using Capacitive Feedback Fabricated Using SiGe HBT's with fmax of 450 GHz

A 305-GHz power amplifier (PA) fabricated in a 130-nm SiGe HBT BiCMOS technology with HBT $f_{t}/f_{max}=350/450$ GHz is presented. The PA employs 4 cascode amplification stages with capacitive feedback between the collector of common base stage and the base of common emitter stage that increases power gain of each stage by ~4 dB and a 4-way power combiner at the output. The PA achieves a measured Psat of 7.5 dBm and OP1dB of 4.5 dBm at 290 GHz. The design reaches a peak small signal gain of 14.5 dB at 305 GHz. The circuit consumes 1008 mW DC power from a 4-V supply and achieves a PAEmax of 0.39%. The PA exhibits the highest Psat and OP1dB at 290 GHz, and the highest small signal gain at 305 GHz among the PA's fabricated using SiGe HBT's with $f_{max}$ less than 500 GHz.
A Distance-based Health Indicator for RUL Prediction of Power Electronics

Power electronic (PE) converters are considered as essential components of electric vehicles and their reliability is receiving increasing attention by vehicle manufacturers. In this vein, remaining useful life (RUL) prediction of PE components is crucial for extending their lifetime and for avoiding unexpected downtime of the power-converter based electrical systems. In this paper, we developed a novel prognostic methodology, utilizing a distance-based health indicator and tracking it using an interacting multiple model (IMM), to estimate the RUL of a degraded system. The distance between the test vector and the cluster center of a self-organizing map (SOM) obtained from a trained healthy model is identified as the failure precursor. The time evolution of the precursor is estimated by an interacting multiple model (IMM) consisting of multiple particle filters or Kalman filters with a linear trend. Then the RUL estimate and the concomitant uncertainty of the estimate are evaluated by extrapolating the IMM state estimates until they cross a pre-defined failure threshold. The algorithms are tested on data from accelerated aging tests under various operating conditions. The results demonstrate that the proposed algorithms provide good RUL predictions for the degraded devices and the methodology is applicable to a variety of systems.