Miracle-dz/UniFAR-FTUs huggingface dataset:
Whole-Program Privilege and Compartmentalization Analysis with the Object-Encapsulation Model

We present the object-encapsulation model, a low-level program representation and analysis framework that exposes and quantifies privilege within a program. Successfully compartmentalizing an application today requires significant expertise, but is an attractive goal as it reduces connectability of attack vectors in exploit chains. The object-encapsulation model enables understanding how a program can best be compartmentalized without requiring deep knowledge of program internals. We translate a program to a new representation, the Program Capability Graph (PCG), mapping each operation to the code and data objects it may access. We aggregate PCG elements into encapsulated-object groups. The resulting encapsulated-objects PCG enables measuring program interconnectedness and encapsulated-object privileges in order to explore and compare compartmentalization strategies. Our deep dive of parsers reveals they are well encapsulated, requiring access to an average of 545/4902 callable interfaces and 1201/29198 external objects. This means the parsers we evaluate can be easily compartmentalized, applying the encapsulated-objects PCG and our analysis to facilitate automatic or manual trust boundary placement. Overall, the object-encapsulation model provides an essential element to language-level analysis of least-privilege in complex systems to aid codebase understanding and refactoring.
Graph Neural Network-based Delay Prediction Model Enhanced by Network Calculus

Network modeling is critical to network management. Existing network modeling approaches still have room for improvement in performance prediction of important performance metrics, such as latency. Graph Neural Network (GNN) has proven their effectiveness for network performance prediction. However, the shortcomings of GNN in data interpretability limit their performance. To address this problem, we introduced network calculus to enhance the interpretation and learning of GNN for data. We proposed a modified GNN model - NetCTRT, which understands the information between topology, routing and traffic and interacts with the Network Calculus (NC)-based delay bound so that the end-to-end delay can be accurately predicted. Experimental results show that NetCTRT can significantly improve the prediction accuracy.
Ubiquitous Computing Integration Models Enabling Wireless Networks Of Sensors

Proposed system, pollution monitoring, the automobile industry, and sports are just a few of the application areas that have grown as a result of ubiquitous sensing and the distinctive features (Sensor systems). As the underlying significantly expanded the number of linked things with real-time communication and data computation, WSNs have grown in importance in recent years. However, owing to the scale and accessibility of IoT, building a complex challenge, and past methodologies established for Iot technologies cannot be implemented directly. In this paper, pairwise clusters models for Iot networks in the Iot paradigm are proposed: I a resource grouping model and (ii) a business clusters model where responsibilities are allocated to individual sensor nodes depending on how well they provide services. The end-to-end latency, and communication bandwidth balancing.
EEPH: An Efficient Extendible Perfect Hashing for Hybrid PMem-DRAM

In recent years, the performance of hash indexes has been significantly improved by exploiting emerging persistent memory (PMem). However, the performance improvement of hash indexes mainly comes from exploiting the hardware features of PMem. Only a few studies optimize the hash index itself to fully exploit the potential of PMem. Interestingly, many of these studies improve the performance of write, but disregard the performance of read, of hash indexes on PMem. With extensive experimental evaluation, we find the major reason for inefficient read in the hash index on PMem is that the overhead of hash collision processing is expensive.To address that, we propose a novel Efficient Extendible Perfect Hashing (EEPH) on PMem-DRAM hybrid data layout to improve read performance of hash indexes. Specifically, we reduce the overhead of dynamic perfect hashing extension on PMem by combing extendible hashing. We then design a hybrid data layout to unlock the inherent read strengths of perfect hashing (i.e., zero collision). Last, we devise a complement move algorithm to efficiently guarantee the zero collision of perfect hashing when data move is conducted on PMem. We compare EEPH with the state-of-the-art hash indexes on PMem by conducting comprehensive experiments on several real-world read-intensive and read-skew workloads. The experimental results confirm the superiority of our EEPH as it achieves up to 2.21× higher throughput and about 1/3 of the 99th percentile latency than state-of-the-art hash indexes.
An Accurate Brain Tumor Segmentation using Deep Learning

Gliomas, which can appear in different sizes, and locations, and with scattered boundaries, pose a significant challenge as an aggressive type of tumor. Convolutional Neural Networks (CNNs), one of the most effective deep learning approaches for image analysis problems, have been utilized to develop an automatic deep learning-based 2D-CNN model for brain tumor segmentation in this study. The architecture of the model was designed to be deeper by using small convolution filters (3x3). Additionally, the number of convolutional layers was increased to 16 for the HGG model and 13 for the LGG model to facilitate efficient learning of complex features from large datasets and achieve better results. Fine tuning among the dataset and hyperparameters was employed to obtain the results. The pre-processing for this model includes the generation of a brain pipeline, intensity normalization, bias correction, and data augmentation. The proposed method’s performance was evaluated using the BRATS-2015 dataset and the Dice Similarity Coefficient (DSC). Our method achieved DSC scores of 0.79, 0.77, and 0.78 for complete, core, and enhanced tumor regions, respectively. These results are comparable to other 2D CNN architecture-based methods.
Contributions to the string topology of product manifolds

The thesis discusses several aspects of string topology presented by Chas and Sullivan in [5] on the homology of the free loop space of a closed oriented manifold. After an introductory chapter we use a speci c chain model for string topology de ned by Irie [20] to perform the homotopy transfer to homology in a special case. We prove vanishing results and combine these with a theorem of Fukaya [13] to get the following result (cf. Theorem 4.17) as a corollary: Theorem 0.1 A closed, oriented, spin Lagrangian submanifold X  pC, ω0q for k n m ¥ 3 cannot be of the form M N where M, N are smooth, closed and oriented manifolds of nite dimension dimM m ¥ 0 and dimN n ¥ 3 respectively with M simply connected and N admitting a Riemannian metric of negative sectional curvature. Earlier publications derived from the dissertation: Zusammenfassung Die Arbeit behandelt verschiedene Aspekte der String Topologie, dargelegt von Chas und Sullivan in [5], auf der Homologie des freien Schleifenraums einer geschlossenen und orientierten Mannigfaltigkeit. Nach einem einführenden Kapitel benutzen wir ein konkretes Kettenmodell für String-Topologie von Irie [20], um in einem Spezialfall den Homotopie-Transfer auf Homologie durchzuführen. Die daraus resultierenden Verschwindungsresultate kombinieren wir mit einem Theorem von Fukaya [13] und erhalten folgenden Satz (vgl. Theorem 4.17): Theorem 0.2 Eine geschlossene, orientierte, spin Lagrangesche Untermannigfaltigkeit X  pC, ω0q für k n m ¥ 3 kann nicht von der Form M N sein, für M, N glatte, geschlossene und orientierte Mannigfaltigkeiten der Dimension dimM m ¥ 0 beziehungsweise dimN n ¥ 3, wobei M einfach zusammenhängend ist und N eine Riemannsche Metrik mit negativer Schnittkrümmung zulässt. Aus dieser Dissertation hervorgegangene Vorverö entlichungen:
6G BRAINS Topology-aware Industry-grade Network Slice Management and Orchestration

This paper describes the integration between the Open Network Automation Platform (ONAP) and UWS Slice Manager within the European project 6G Brains. The proposed solution allows for End-To-End(E2E) Network Slicing, enabling fine-grain and optimal traffic engineering of the Network components. This work's findings ensure an E2E connection. The solution allows external services to create slices and attach them easily. The UWS Network Slice Manager allows for detailed monitoring of the network slice's inner components. With this information, ONAP can improve the network by creating and optimising slices on demand. The validation of the integration presents the workflow to create and attach slices. These operations enable autonomous workflows for deploying E2E Services that ensure the QoS/QoE in the network.
Productive teaming under uncertainty: when a human and a machine classify objects together

We study the task of object categorization in an industrial setting. Typically, a machine classifies objects according to an internal, inferred model, and calls to a human worker if it is uncertain. However, the human worker may be also uncertain. We elaborate on the challenges and solutions to assess the certainty of the human without disturbing the industrial process, and to assess label reliability and human certainty in conventional object classification and crowdworking. Albeit there are methods for measuring stress, insights on the correlation of stress and uncertainty and uncertainty indicators during labeling by humans, these advances are yet to be combined to solve the aforementioned uncertainty challenge. We propose a solution as a sequence of tasks, starting with a experiment that measures human certainty in a task of controlled difficulty, whereupon we can associate certainty with correctness and levels of vital signals.
Self-attention and Online Hard Example Mining Based Network for Marine Microalgae Detection

With the utilization and exploitation of marine resources, the consciousness of protecting the environment is rising, and the classification and localization of marine microalgae is a good solution. In this regard, we propose self-attention and online hard example mining based network for marine microalgae detection, which is based on Cascade-RCNN network. First, the Mixup method is introduced to enhance and augment data. In the backbone network, Transformer self-attention and feature pyramid network (FPN) are introduced to make the model getting stronger feature extraction ability and can adapt to objects of multi-scale. By introducing online hard example mining (OHEM) method, the training can be completed under the condition of imbalanced data distribution. We also use multi-scale training and multi-scale testing methods to improve the training performance of the model. Through experiments on the marine microalgae dataset provided by IEEE UV 2022 “Vision Meets Algae” Object Detection Challenge, compared with the baseline network, our proposed method improves by 3.97%.
Studying How Object Handoff Orientations Relate to Subject Preferences on Handover

Data collection involving human-human handover has provided enormous leaps in driving human-robot interaction research. However, all existing datasets lack information on giver and receiver preferences in handover interactions. Most previous studies have relied on small-scale human participant experiments involving a limited range of objects, where participants are often expected to share similar handover attitudes. Nevertheless, in real-world scenarios with diverse objects, it is likely that giver and receiver preferences will not always align. In this paper, we present a large-scale study of human-human handover behavior involving 96 participant dyads derived from 32 participants in total and 204 objects. Each dyad consists of 2 participants engaging in handovers, where after a giver-initiated handover, participants provide comfort ratings and binary responses indicating whether they agreed on the handover location. We also ask the receiver to demonstrate their preferred handover to gain detailed information on object pose at the handoff point. Our study captures 4-viewpoint RGB-D recordings of both giver-initiated forward handover and receiver-initiated demonstration handover. Using the collected data, we evaluate how the subjective ratings provided by participants correlate with objective measures of alignment of object orientation at handoff.
When Computer Vision Meets Algae

Universal Village (UV) is a new concept proposed by MIT’s Universal Village Program, which envisions an ideal future society that prioritizes environmental and ecosystem preservation while fulfilling human needs, thereby ensuring sustainable wellbeing for society’s residents. Marine ecological conservation is an integral part of the Global Village plan, and algae are essential marine biological resources. The organic matter and accumulated energy they produce serve as the foundation for the survival and development of the entire marine biological community. With the rapid development of computer vision technology, there is now the possibility of combining advanced object detection algorithms with traditional algae detection methods. In this work, we leveraged Swin Transformer and Covnext as feature extractors and Cascade RCNN as the main network to detect and classify eight types of microalgae in microscope images. We also employed Weighted Boxes Fusion in post-processing to improve detection accuracy and model generalization.
End-to-End 5G Priority Scheduling Strategy for a WBAN Health Monitoring System

A Healthcare Monitoring System (HMS) based on Wireless Body Networks (WBAN) might implement Quality of service (QoS) tools to guarantee some physiological data requirements such as preventing data loss and delay as healthcare applications are time and loss sensitive. Moreover, one of the promising technologies which is being regarded in 5G standard Long Term Evolution (LTE) is Machine-to-Machine communications (M2M) which is a key component of Internet of Things (IoT). In this paper, two complementary issues have been addressed at the WBAN coordinator radio interface and at the radio 4G/5G interface, respectively, for a complete end-to-end Quality of Service support. At the WBAN part, we focus on mitigation of delay at the coordinator for emergency and medical traffic flows by proposing a new scheduling strategy named PWRR (Priority-Weighted Round Robin). At the cellular 5G interface, service differentiation strategies for a reliable transmission of priority packets for the traffic have been considered. To this end, an adaptive Multiple ACB factors (MACB) algorithm is proposed to improve the regular ACB (Access Class Baring) method, according to M2M traffic category with respect to their access delay budget and drop rate. Analytical and simulation results show that the overall system offers much improvements in terms of delay of emergency and medical data flows compared to the other strategies in literature.
Data collection from LoRaWAN sensor network by UAV gateway: design, empirical results and dataset

Collecting data from Internet-of-Things (IoT) devices, especially the variety of sensors dispersed in the environment, is an increasingly important and difficult task. Several long-range radio-access technologies, such as low-power wide-area networks (LPWAN) and specifically LoRaWAN, have been proposed to address this challenge. However, until now, the key focus of the related studies has been on static terrestrial LPWAN deployments. In this study, we depart from this vision and investigate the practical feasibility and performance of a LoRaWAN gateway (GW) on a flying platform, specifically - an unmanned aerial vehicle (UAV). The key contributions of this study are (i) the design and field-testing of a packet-sniffer-based mobile LoRaWAN GW prototype, allowing collection of the data from LoRaWAN networks, including the already deployed ones; (ii) the open-publication of the data collected during our experimental campaign in the 426 LoRaWAN sensor node network of the University of Oulu illustrating the performance of different drone trajectories; (iii) the initial results of the system's performance analysis, revealing some interesting trends and setting goals for further studies, and pinpointing the lessons learned during the experimental campaign. Our empirical findings suggest that the Travelling Salesman Problem (TSP) trajectory is the most effective moving trajectory for the number of packets collected and the average energy consumed per packet collected.
TSN Gatekeeper: Enforcing stream reservations via P4-based in-network filtering

Real-time communication is crucial for mission critical scenarios, such as industrial automation and automotive applications. To meet these applications' strict quality of service (QoS) requirements, a new set of specifications, known as time-sensitive networking standards (TSN), has been proposed. TSN requires pre-registration of data streams before actual communication to help guarantee bandwidth and ensure constrained end-to-end latency. However, this mechanism is vulnerable to traffic overload and denial of service (DoS) attacks. This paper proposes a P4-based dynamic attack filtering as a link-layer network function to defend TSN against malicious network elements, such as faulty talkers or switches, directly on the data plane. Our experiments indicate that our P4-based implementation can filter malicious traffic with minimal overhead and minimize frame losses for legitimate traffic.
Spatiotemporal Changes in aerosol loadings across Bangladesh from 2010-2021

Climate simulations are uncertain due to aerosols' significant contribution to the atmospheric process. In current climate diagnostics, it is vital to assess aerosols properly. The present study investigated spatiotemporal changes in aerosol loadings across Bangladesh based on Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol products (at 550 nm) during the time period 2010–2021. Results of this study showed high aerosol loadings (Aerosol Optical Depth, AOD >0.6) throughout the entire country excluding some small areas of the Chattogram and Sylhet divisions. Post-monsoon aerosol loadings were the lowest and pre-monsoon and winter aerosol loadings were the highest. The highest aerosol loadings (0.87) demonstrated in June whereas the lowest (0.46) in October. The regional AOD patterns in eight distinct divisions of Bangladesh were studied, all the divisions were showing increasing trends of AOD. The highest AOD levels were found in Rajshahi and Khulna but the highest increasing rate of AOD was reported in Rangpur. At the Dhaka University site (Bhola site), the correlation (r) between satellite and ground-based measurements was 0.82 (0.80) for Dark Target (DT) and 0.87 (0.76) for Deep Blue (DB), suggesting further applications can be made to these datasets.
Digit-DM: A Sustainable Data Mining Modell for Continuous Digitization in Manufacturing

Manufacturing as an industry is under continuous pressure to deliver the right product, at the right quality, quantity and in time. To do so it becomes increasingly important to detect the source of manufacturing problems in a short amount of time but also to prevent further occurrence of know problems. Data Mining is focused on identifying problem patterns and inferring the right interpretation to trace and resolve the root cause in time. However, lessons learned are rarely transported into digital solutions that then thoroughly enable to automatize detection and resolving of incidents. Data mining models exist, but no structured approach for transforming and sustaining found solutions digitally. We are introducing Digit-DM as a structured and strategic process for digitizing analytical results. Digit-DM is building on top of existing data mining models but defines a strategic process for continuous digitization, enabling sustainable, digital manufacturing support, utilizing analytical lessons learned.
Discrete-Time Gradient Systems Governed by Difference Equation with Minima

This article explores the theory of discrete-time gradient systems that converge in a finite amount of time and are governed by a difference equation with minima. Two algorithms with distinct structures are discussed, both aimed at achieving finite-time stabilization of these systems. These gradient-based algorithms have significant applications in solving optimization problems. Using the finite-time convergent techniques discussed in the article, a quadratic programming problem is solved, and an optimal solution is obtained within a finite time frame. The effectiveness of these proposed methods is demonstrated through simulation results.
Steps Toward Industry 5.0: Building “6S” Parallel Industries With Cyber-Physical-Social Intelligence

Very recently, intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers, entrepreneurs, and policymakers from various sectors around the world. However, there is no consensus on why and what is Industry 5.0 yet. In this paper, we define Industry 5.0 from its philosophical and historical origin and evolution, emphasize its new thinking on virtual-real duality and human-machine interaction, and introduce its new theory and technology based on parallel intelligence (PI), artificial societies, computational experiments, and parallel execution (the ACP method), and cyber-physical-social systems (CPSS). Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development. We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies. Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces, Secure in cyberspaces, Sustainable in ecology, Sensitive in indi-vidual privacy and rights, Service for all, and Smartness of all.
Incipient Current and Voltage Sensors Fault Diagnosis Scheme for Grid Side Converters

This paper proposes a model-based fault diagnosis scheme for incipient faults in the DC voltage and AC current sensors of the grid-tied converter, considering the coupling phenomena between the two sides of the converter that might cause fault propagation. First, a linear descriptor estimator is designed, based on a linear augmented descriptor model of a nonlinear grid side converter (GSC) system, while considering the effect of sensor noise and modeling uncertainty. Second, a time-varying estimator gain matrix for the design of the descriptor estimator is obtained by minimizing the sum of the variances of the estimation error. Lastly, the diagnosis of incipient sensor faults is designed using the derived analytical redundancy relations, based on the estimations provided by linear descriptor estimator. The results of this work are useful for the early diagnosis of incipient sensor faults, which can improve the safety and reliability of GSCs.
Complete Join Reordering for Null-Intolerant Joins

The join reordering problem is a core task in query optimization to find the most efficient evaluation order for join operations. The Enhanced Compensation-based Approach (ECA) is the state-of-the-art approach for this problem which is based on using new operators called compensation operators to enlarge the query plan search space with more join reorderings. However, ECA cannot provide complete join reorderability for queries involving one or more full outerjoins. In this paper, we present the first complete join reordering solution, named CJR. By introducing a new and more expressive compensation operator and an enhanced set of rewriting rules, CJR is able to provide complete join reorderability for all join queries with null-intolerant join predicates. Our experimental results on the Join Order Benchmark demonstrate that CJR can improve query performance by a factor of 12.32.