OpenRaiser/Intern-Atlas huggingface dataset:
Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systems
Venue: arXiv cs.NI 2021
Year: 2021

In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always at stake to meet the objective of local energy minimization. In this regard, Multi-access Edge Computing (MEC)-enabled FL addresses the tradeoff between the model performance and the energy consumption of the mobile devices by allowing users to offload a portion of their local dataset to an edge server for the model training. Since the edge server has high computation capability, the time consumption of the model training at the edge server is insignificant. However, the time consumption for dataset offloading from mobile users to the edge server has a significant impact on the total time consumption. Thus, resource management in MEC-enabled FL is challenging, where the objective is to reduce the total time consumption while saving the energy consumption of the mobile devices. In this paper, we formulate an energy-aware resource management for MEC-enabled FL in which the model training loss and the total time consumption are jointly minimized, while considering the energy limitation of mobile devices. In addition, we recast the formulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capture the coupling constraints between the radio resource management and dataset offloading. We then analyze the impact of the dataset offloading and computing resource allocation on the model training loss, time, and the energy consumption.
Modeling of crisis periods in stock markets
Venue: arXiv cs.CG 2021
Year: 2021

We exploit a recent computational framework to model and detect financial crises in stock markets, as well as shock events in cryptocurrency markets, which are characterized by a sudden or severe drop in prices. Our method manages to detect all past crises in the French industrial stock market starting with the crash of 1929, including financial crises after 1990 (e.g. dot-com bubble burst of 2000, stock market downturn of 2002), and all past crashes in the cryptocurrency market, namely in 2018, and also in 2020 due to covid-19. We leverage copulae clustering, based on the distance between probability distributions, in order to validate the reliability of the framework; we show that clusters contain copulae from similar market states such as normal states, or crises. Moreover, we propose a novel regression model that can detect successfully all past events using less than 10% of the information that the previous framework requires. We train our model by historical data on the industry assets, and we are able to detect all past shock events in the cryptocurrency market. Our tools provide the essential components of our software framework that offers fast and reliable detection, or even prediction, of shock events in stock and cryptocurrency markets of hundreds of assets.
IoT Data Quality Issues and Potential Solutions: A Literature Review
Venue: arXiv cs.NI 2021
Year: 2021

The Internet of Things (IoT) is a paradigm that connects everyday items to the Internet. In the recent decade, the IoT's spreading popularity is a promising opportunity for people and industries. IoT utilizes in a wide range of respects such as agriculture, healthcare, smart cities, and manufacturing sectors. IoT data quality is crucial in IoT real-life applications. IoT data quality dimensions and issues should be considered because we require data to make accurate and timely decisions, produce commodities, and gain insights about events, people, and the environment. It is essential to point out that we cannot reach valuable results by using poor quality data. This paper aims to develop a new category for IoT data quality. Hence, we examine existing IoT data quality dimensions and IoT data quality issues in general and specific domains and IoT data quality dimensions' categories. It is worth considering that categories in the context of IoT are not many. We developed a new category in which IoT data quality dimensions and issues are separated. Concerning this category, we can get familiar with related dimensions and issues in each category. To enhance data quality dimensions and minimize data quality issues, we suggest potential solutions using Blockchain to overcome IoT's security issues.
End-to-End Quality of Service Management in the Physical Internet: a Digital Internet Inspired Multi-Domain Approach
Venue: arXiv cs.NI 2021
Year: 2021

For the layer 'System Level Functionality' of the Phyisical Internet, it is needed to estimate end-to-end performance characteristics of transportations that visit multiple logistic domains. This paper proposes an approach based on a Digital Internet functionality: a combination of a Service Level Agreement registry and a Quality of Service processor. Existing SLA-calculus gives tools for the QoS-processor to combine the SLA-parameters for all possible end-to-end paths and gives the QoS-processor the possibility to propose the best path given the required performance. A realistic implementation is proposed using a multi objective/constraint approach and a related communication form between the domain owner and the QoS Processor.
A Multi-Tenant Framework for Cloud Container Services
Venue: arXiv cs.DC 2021
Year: 2021

Container technologies have been evolving rapidly in the cloud-native era. Kubernetes, as a production-grade container orchestration platform, has been proven to be successful at managing containerized applications in on-premises datacenters. However, Kubernetes lacks sufficient multi-tenant supports by design, meaning in cloud environments, dedicated clusters are required to serve multiple users, i.e., tenants. This limitation significantly diminishes the benefits of cloud computing, and makes it difficult to build multi-tenant software as a service (SaaS) products using Kubernetes. In this paper, we propose Virtual-Cluster, a new multi-tenant framework that extends Kubernetes with adequate multi-tenant supports. Basically, VirtualCluster provides both control plane and data plane isolations while sharing the underlying compute resources among tenants. The new framework preserves the API compatibility by avoiding modifying the Kubernetes core components. Hence, it can be easily integrated with existing Kubernetes use cases. Our experimental results show that the overheads introduced by VirtualCluster, in terms of latency and throughput, is moderate.
Object Localization Through a Single Multiple-Model Convolutional Neural Network with a Specific Training Approach
Venue: arXiv cs.CV 2021
Year: 2021

Object localization has a vital role in any object detector, and therefore, has been the focus of attention by many researchers. In this article, a special training approach is proposed for a light convolutional neural network (CNN) to determine the region of interest (ROI) in an image while effectively reducing the number of probable anchor boxes. Almost all CNN-based detectors utilize a fixed input size image, which may yield poor performance when dealing with various object sizes. In this paper, a different CNN structure is proposed taking three different input sizes, to enhance the performance. In order to demonstrate the effectiveness of the proposed method, two common data set are used for training while tracking by localization application is considered to demonstrate its final performance. The promising results indicate the applicability of the presented structure and the training method in practice.
Information Freshness Analysis of Slotted ALOHA in Gilbert-Elliot Channels
Venue: arXiv cs.IT 2021
Year: 2021

This letter analyzes a class of information freshness metrics for large IoT systems in which terminals employ slotted ALOHA to access a common channel. Considering a Gilbert- Elliot channel model, information freshness is evaluated through a penalty function that follows a power law of the time elapsed since the last received update, in contrast with the linear growth of age of information. By means of a signal flow graph analysis of Markov processes, we provide exact closed form expressions for the average penalty and for the peak penalty violation probability.
Beamforming in LEO Constellations for NB-IoT Services in 6G Communications
Venue: arXiv cs.NI 2021
Year: 2021

With the first commercializations of 5G networks, Beyond 5G (B5G), or 6G, systems are starting to be defined. In this context, Internet of Things (IoT) services will be even more impactful with respect to 5G systems. In order to cope with the huge amount of IoT devices, and the potentially large capacity requirements for the most advanced of them (e.g., live camera feeds from first responders in emergency scenarios), non-terrestrial systems will be pivotal to assist and complement the terrestrial networks. In this paper, we propose a multi-layer non-terrestrial architecture for NarrowBand-IoT (NB-IoT) services based on Geosynchronous Orbit (GSO) and Non GSO (NGSO) nodes. To cope with both the number of devices and the potentially large capacities, we propose to implement Minimum Mean Square Error (MMSE) beamforming in an aggressive full frequency reuse scenario. The performance assessment shows the significant benefits of the proposed solution.
Frequency and Impact of Technical Debt Characteristics in Companies Producing Mechatronic Products
Venue: arXiv cs.SE 2021
Year: 2021

Complexity of products, volatility in global markets, and the increasingly rapid pace of innovations may make it difficult to know how to approach challenging situations in mechatronic design and production. Technical Debt (TD) is a metaphor that describes the practical bargain of exchanging short-term benefits for long-term negative consequences. Oftentimes, the scope and impact of TD, as well as the cost of corrective measures, are underestimated. Especially for mechatronic teams in the mechanical, electrical, and software disciplines, the adverse interdisciplinary ripple effects of TD incidents are passed on throughout the life cycle. The analysis of the first comprehensive survey showed that not only do the TD types differ in cross-disciplinary comparisons, but different characteristics can also be observed depending on whether a discipline is studied in isolation or in combination with others. To validate the study results and to report on a general consciousness of TD in the disciplines, this follow-up study involves 15 of the 50 experts of the predecessor study and reflects the frequency and impact of technical debt in industrial experts' daily work using a questionnaire. These experts rate 14 TD types, 47 TD causes, and 33 TD symptoms in terms of their frequency and impact. Detailed analyses reveal consistent results for the most frequent TD types and causes, yet they show divergent characteristics in a profound exploration of discipline-specific phenomena. Thus, this study has the potential to set the foundations for future automated TD identification analyses in mechatronics.
Female ICT participation in South-Eastern Nigerian Tertiary Institutions: Inhibiting Factors
Venue: arXiv cs.CY 2021
Year: 2021

The study examined the participation of female students of South Eastern Nigerian tertiary institutions in Information and Communication Technologies (ICTs). The study discussed the attendant gender divide in ICTs participation, reasons for low female participation in ICT, consequences of not bridging the divide and ways of encouraging female participation in ICT. A structured questionnaire was used to elicit information from respondents. A multi stage random sampling technique was used in the selection of respondents. One hundred and thirty six (136) undergraduate female students of tertiary institutions in South Eastern Nigeria constituted the study sample. Data collected was analysed using descriptive statistics. Findings suggest that high cost of ICT and high level of male dominance, which made females think that ICT is for males were the major reasons for low female participation in ICT. Reducing the cost of Information Technology, and parental involvement in their children selection choice of study were suggested to encourage female participation in Information and Communication Technologies.
TagMe: GPS-Assisted Automatic Object Annotation in Videos
Venue: arXiv cs.CV 2021
Year: 2021

Training high-accuracy object detection models requires large and diverse annotated datasets. However, creating these data-sets is time-consuming and expensive since it relies on human annotators. We design, implement, and evaluate TagMe, a new approach for automatic object annotation in videos that uses GPS data. When the GPS trace of an object is available, TagMe matches the object's motion from GPS trace and the pixels' motions in the video to find the pixels belonging to the object in the video and creates the bounding box annotations of the object. TagMe works using passive data collection and can continuously generate new object annotations from outdoor video streams without any human annotators. We evaluate TagMe on a dataset of 100 video clips. We show TagMe can produce high-quality object annotations in a fully-automatic and low-cost way. Compared with the traditional human-in-the-loop solution, TagMe can produce the same amount of annotations at a much lower cost, e.g., up to 110x.
Hierarchical Proxy-based Loss for Deep Metric Learning
Venue: arXiv cs.CV 2021
Year: 2021

Proxy-based metric learning losses are superior to pair-based losses due to their fast convergence and low training complexity. However, existing proxy-based losses focus on learning class-discriminative features while overlooking the commonalities shared across classes which are potentially useful in describing and matching samples. Moreover, they ignore the implicit hierarchy of categories in real-world datasets, where similar subordinate classes can be grouped together. In this paper, we present a framework that leverages this implicit hierarchy by imposing a hierarchical structure on the proxies and can be used with any existing proxy-based loss. This allows our model to capture both class-discriminative features and class-shared characteristics without breaking the implicit data hierarchy. We evaluate our method on five established image retrieval datasets such as In-Shop and SOP. Results demonstrate that our hierarchical proxy-based loss framework improves the performance of existing proxy-based losses, especially on large datasets which exhibit strong hierarchical structure.
Term-community-based topic detection with variable resolution
Venue: arXiv cs.CL 2021
Year: 2021

Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models. We present in detail a method that is especially designed with the requirements of domain experts in mind. Like similar methods, it employs community detection in term co-occurrence graphs, but it is enhanced by including a resolution parameter that can be used for changing the targeted topic granularity. We also establish a term ranking and use semantic word-embedding for presenting term communities in a way that facilitates their interpretation. We demonstrate the application of our method with a widely used corpus of general news articles and show the results of detailed social-sciences expert evaluations of detected topics at various resolutions. A comparison with topics detected by Latent Dirichlet Allocation is also included. Finally, we discuss factors that influence topic interpretation.
Engineering an Intelligent Essay Scoring and Feedback System: An Experience Report
Venue: arXiv cs.LG 2021
Year: 2021

Artificial Intelligence (AI) / Machine Learning (ML)-based systems are widely sought-after commercial solutions that can automate and augment core business services. Intelligent systems can improve the quality of services offered and support scalability through automation. In this paper we describe our experience in engineering an exploratory system for assessing the quality of essays supplied by customers of a specialized recruitment support service. The problem domain is challenging because the open-ended customer-supplied source text has considerable scope for ambiguity and error, making models for analysis hard to build. There is also a need to incorporate specialized business domain knowledge into the intelligent processing systems. To address these challenges, we experimented with and exploited a number of cloud-based machine learning models and composed them into an application-specific processing pipeline. This design allows for modification of the underlying algorithms as more data and improved techniques become available. We describe our design, and the main challenges we faced, namely keeping a check on the quality control of the models, testing the software and deploying the computationally expensive ML models on the cloud.
Towards improving architectural diagram consistency using system descriptors
Venue: arXiv cs.SE 2021
Year: 2021

Communication between practitioners is essential for the system's quality in the DevOps context. To improve this communication, practitioners often use informal diagrams to represent the components of a system. However, as systems evolve, it is a challenge to synchronize diagrams with production environments consistently. Hence, the inconsistency of architectural diagrams can affect communication between practitioner and their understanding of systems. In this paper, we propose the use of system descriptors to improve deployment diagram consistency. We state two main hypotheses: (1) if an architectural diagram is generated from a valid system descriptor, then the diagram is consistent; (2) if a valid system descriptor is generated from an architectural diagram, then the diagram is consistent. We report a case study to explore our hypotheses. Furthermore, we constructed a system descriptor from the Netflix deployment diagram, and we applied our tool to generate a new architectural diagram. Finally, we compare the original and generated diagrams to evaluate our proposal. Our case study shows all Docker compose description elements can be graphically represented in the generated architectural diagram, and the generated diagram does not present inconsistent aspects of the original diagram. Thus, our preliminary results lead to further evaluation in controlled and empirical experiments to test our hypotheses.
A Semidefinite Optimization-based Branch-and-Bound Algorithm for Several Reactive Optimal Power Flow Problems
Venue: arXiv cs.RO 2021
Year: 2021

The Reactive Optimal Power Flow (ROPF) problem consists in computing an optimal power generation dispatch for an alternating current transmission network that respects power flow equations and operational constraints. Some means of action on the voltage are modelled in the ROPF problem such as the possible activation of shunts, which implies discrete variables. The ROPF problem belongs to the class of nonconvex MINLPs (Mixed-Integer Nonlinear Problems), which are NP-hard problems. In this paper, we solve three new variants of the ROPF problem by using a semidefinite optimization-based Branch-and-Bound algorithm. We present results on MATPOWER instances and we show that this method can solve to global optimality most instances. On the instances not solved to optimality, our algorithm is able to find solutions with a value better than the ones obtained by a rounding algorithm. We also demonstrate that applying an appropriate clique merging algorithm can significantly speed up the resolution of semidefinite relaxations of ROPF large instances.
Evaluation of Sampling-Based Optimizing Planners for Outdoor Robot Navigation
Venue: arXiv cs.RO 2021
Year: 2021

Sampling-Based Optimal(SBO) path planning has been mainly used for robotic arm manipulation tasks. Several research works have been carried out in order to evaluate performances of various SBO planners for arm manipulation. However, not much of work is available that highlights performances of SBO planners in context of mobile robot navigation in outdoor 3D environments. This paper evaluates performances of major SBO planners in Open Motion Planning Library(OMPL) for that purpose. Due to large number of existing SBO planners, experimenting and selecting a proper planner for a planning problem can be burdensome and ambiguous. SBO planner's probabilistic nature can also add a bias to this procedure. To address this, we evaluate performances of all available SBO planners in OMPL with a randomized planning problem generation method iteratively. Evaluations are done in various state spaces suiting for different differential constraints of mobile robots. The planning setups are focused for navigation of mobile robots in outdoor environments. The outdoor environment representation is done with prebuilt OctoMaps, collision checks are performed between a 3D box representing robot body and OctoMap for validation of sampled states. Several evaluation metrics such as resulting path's length, smoothness and status of acquired final solutions are selected. According to selected metrics, performances from different SBO planners are presented comparatively. Experimental results shows the significance of parallel computing towards quicker convergence rates for optimal solutions. Several SBO methods that takes advantage of parallel computing produced better results consistently in all state spaces for different planning inquiries.
ECINN: Efficient Counterfactuals from Invertible Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Counterfactual examples identify how inputs can be altered to change the predicted class of a classifier, thus opening up the black-box nature of, e.g., deep neural networks. We propose a method, ECINN, that utilizes the generative capacities of invertible neural networks for image classification to generate counterfactual examples efficiently. In contrast to competing methods that sometimes need a thousand evaluations or more of the classifier, ECINN has a closed-form expression and generates a counterfactual in the time of only two evaluations. Arguably, the main challenge of generating counterfactual examples is to alter only input features that affect the predicted outcome, i.e., class-dependent features. Our experiments demonstrate how ECINN alters class-dependent image regions to change the perceptual and predicted class of the counterfactuals. Additionally, we extend ECINN to also produce heatmaps (ECINNh) for easy inspection of, e.g., pairwise class-dependent changes in the generated counterfactual examples. Experimentally, we find that ECINNh outperforms established methods that generate heatmap-based explanations.